Population Growth and Sustainable Transport in China 9811974691, 9789811974694

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Population, Regional Development and Transport

Pengjun Zhao Dandan Yuan

Population Growth and Sustainable Transport in China

Population, Regional Development and Transport Series Editor Pengjun Zhao, College of Urban and Environmental Sciences, Peking University, Beijing, China

This book series chiefly explores population change, regional development and transport in contemporary China. Its goal is to enhance our current understanding of population, regional development and sustainable transport in a context of rapid urbanization and transition – characterized by the shift from a centrally planned system to a market system, together with growing economic globalization and political decentralization. The series will enrich the existing literature on population studies, regional development studies and transport studies. In particular, it highlights academic research on the interactions between population, regional development and transport. It will also shed new light on government practices with regard to regional development planning and management and transport investment.

Pengjun Zhao · Dandan Yuan

Population Growth and Sustainable Transport in China

Pengjun Zhao College of Urban and Environmental Sciences Peking University Beijing, China

Dandan Yuan College of Urban and Environmental Sciences Peking University Beijing, China

School of Urban Planning and Design Peking University Shenzhen Graduate School Shenzhen, China

Funding information: NSFC, No. 41571147 ISSN 2662-4613 ISSN 2662-4621 (electronic) Population, Regional Development and Transport ISBN 978-981-19-7469-4 ISBN 978-981-19-7470-0 (eBook) https://doi.org/10.1007/978-981-19-7470-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

With the rapid progress in society and economy of China, interaction between population and transport has become one of the key issues associated with the quality and sustainability of regional development. Population growth and its structure determine the structure of transport demand and investment in transport facilities. Meanwhile, it has dynamic changes and spatial differences which will bring new requirements on the supply side of transport system. Whether the development of transport matches with population growth determines the efficiency. Thus, construction level and spatial configuration of transport infrastructure should keep a high matching relationship with regional population growth in order to give play to the resource value. Moreover, the coupling between population growth, population structure, which is believed a major aspect of social transformation, and transport is an important part of the human– land harmonisation. Research on the complicated relationships between population growth and transport can provide the necessary theoretical and empirical evidence to guide sustainable and human-oriented development in developing regions and countries. Since the reform and opening up several decades ago, China’s economy has gone through an important period of social transformation. During this period, the demographic transition in China has been accelerated greatly and the social development strategy has been adjusted constantly. Against this background, this book is organised with three main research goals, including (1) Exploring China’s population structure and transport development, (2) Analysing the differentiation of travel behaviour and (3) Providing policy implications on an empirical basis. Guided by these goals, this book aims to make contributions to promoting the refined and customised transport in developing countries such as China, matching the supply with the demand and creating a sustainable and people-oriented transport system with better quality and higher efficiency. Different from most studies discussing the influencing factors of travel behaviour, this book focuses on the effects of population structure. To comprehensively analyse China’s population growth and its influence on transport demand, four aspects including urban–rural structure, family structure, gender structure and education structure are taken into discussion. It provides clear images of the temporal and v

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spatial characteristics of China’s transport system as well as population growth, to help readers all over the world learn about China’s previous achievements and current development in economy and society. What is more, it may be the first book to focus on the relationship between population structure and travel behaviour in China. Based on the multidimensional attributes of China’s population structure and its temporal and spatial characteristics, as well as the analysis on the differentiated performance of residents’ traffic and travel behaviour in different demographic groups, policy implications for China’s future transport system development from a macro perspective are put forward. Chapter 1 elaborates the importance and necessity of this study, major research purpose as analytical framework and mainly applied data source. Chapter 2 focuses on summarising and analysing the development history and spatial distribution characteristics of China’s comprehensive transport system. The first section will help readers to develop a better understanding of China’s transport system, and the second section explores the time–space changes in China’s fixed asset investment in transport facilities since the start of the twenty-first century. Chapter 3 analyses the transition of China’s population reproduction in recent decades by observing the changing trends of several indicators such as birth rate, death rate, natural growth rate and total fertility rate, and moves forward to focus on one basic and unique aspect of China’s population, namely the dual urban–rural population structure. Chapter 4 focuses on the other three vital aspects of population structure, namely family structure, gender structure and education structure, which lays a basic and empirical foundation for further discussions on people-oriented and sustainable transport. Chapter 5 analyses the development status of China’s comprehensive transport system through quantitative evaluation from two perspectives. Firstly, an evaluation analysis of China’s transport service level on the basis of the characteristics of population spatial distribution will be made. Secondly, it analyses the regional accessibility of China’s transport network while considering demographic factors, by combining data about population and transport infrastructure. Chapters 7–9 are aimed at exploring the relationship between population’s social and economic attributes and their travel behaviour, relatively from the perspective of urban–rural structure, family structure, gender structure and quality structure represented by education level. Based on the empirical analysis, Chap. 10 is organised into three sections. The first part offers a comprehensive summary of China’s current policies and implementation measures for high-quality and sustainable transport. The second part concludes the experience and lessons from the relationship between population and transport. The third part focuses on the future development of China’s comprehensive transport through giving a look ahead at what we can do to help to build a sustainable transport system. Beijing, China

Pengjun Zhao Dandan Yuan

Preface

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Acknowledgements We acknowledge the financial support of the National Natural Science Foundation of China (No. 42130402 and No. 41925003) and the Shenzhen Science and Technology Program (JCYJ20220818100810024). The authors are responsible for all errors and interpretations.

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Importance and Necessity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 Regional Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.2 Transport Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.3 Transport Investment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.4 Sustainable and Human-Oriented Transport . . . . . . . . . . . 1.1.5 Human–Land Harmonisation . . . . . . . . . . . . . . . . . . . . . . . 1.1.6 Social Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.7 Transport Equity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Research Purpose and Analytical Framework . . . . . . . . . . . . . . . . . 1.2.1 Purposes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Main Data Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Data for National-Level Analysis . . . . . . . . . . . . . . . . . . . 1.3.2 Data for City Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 1 3 4 7 9 11 11 13 13 16 21 21 24 26

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China’s Transport System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 History of China’s Comprehensive Transport Corridor . . . . . . . . . 2.1.1 Rudimentary Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Initial Stage of Formation . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Stage of Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Fixed-Asset Investment in Transport Facilities . . . . . . . . . . . . . . . . 2.2.1 Highway Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Waterway Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Railway Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4 Air Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.5 Logistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

31 31 31 34 37 42 42 44 49 54 55 56

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Contents

Population Growth and Urban–Rural Structure . . . . . . . . . . . . . . . . . 3.1 Population Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Dual Urban–Rural Population Structure . . . . . . . . . . . . . . . . . . . . . 3.2.1 Formation of the Urban–Rural Dual System . . . . . . . . . . 3.2.2 Changes in the Urban–Rural Structure . . . . . . . . . . . . . . . 3.2.3 Spatial Characteristics of the Urban–Rural Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

59 59 65 65 67

Family Structure, Gender and Education . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Family Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Changes in Family Structure . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 Migrant Families and Family Employment . . . . . . . . . . . 4.1.3 Fertility Policy in China and Its Impacts on Family Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Gender Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 General Characteristics of Gender Structure . . . . . . . . . . 4.2.2 Spatial Characteristics of Gender Structure . . . . . . . . . . . 4.3 Education Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Basic Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Gaps in Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

97 97 97 108

Population-Based Service Level and the Accessibility of Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Spatial Match Between Population and Transport . . . . . . . . . . . . . 5.1.1 Railway Transport Service Level Evaluation . . . . . . . . . . 5.1.2 Expressway Transport Service Level Evaluation . . . . . . . 5.1.3 High-Speed Rail Transport Service Level Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.4 Air Transport Service Level Evaluation . . . . . . . . . . . . . . 5.2 Population-Based Transport Accessibility in China . . . . . . . . . . . . 5.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Travel Differences Between the Urban and Rural Population . . . . . . 6.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Urban–Rural Gap in Travel Mode . . . . . . . . . . . . . . . . . . . 6.1.2 Urban–Rural Gap in Trip Frequency and Travel Distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.3 Urban–Rural Gap in Travel Purpose and Other Relevant Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.4 Research Gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

89 94 95

114 122 123 132 135 136 140 143 144 147 147 147 150 152 154 156 164 166 167 167 167 169 170 170

Contents

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The National Level: Evidence from CFPS . . . . . . . . . . . . . . . . . . . . 6.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 A City Level: Comparison Among Urban Areas, Townships and Villages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 City Level: Taking the Megacity Beijing as the Case . . . . . . . . . . . 6.4.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

172 172 173 175

7

Effects of Family Structure on Travel Behaviour . . . . . . . . . . . . . . . . . 7.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 Family Size and Travel Behaviour . . . . . . . . . . . . . . . . . . . 7.1.2 Family Type and Travel Behaviour . . . . . . . . . . . . . . . . . . 7.1.3 Family Income and Travel Behaviour . . . . . . . . . . . . . . . . 7.1.4 Research Gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Statistical Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Regression Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

233 233 234 235 236 237 237 237 238 241 241 252 262 263

8

Gendered Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.1 Gender Gap in Mode Choice and Travel Purpose . . . . . . 8.1.2 Gender Gap in Trip Frequency and Travel Distance . . . . 8.1.3 Gender Equality in Transport . . . . . . . . . . . . . . . . . . . . . . . 8.1.4 Existing Evidence in China . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.5 Research Gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.1 Gender Differences in Travel Purpose . . . . . . . . . . . . . . . . 8.3.2 Gender Differences in Travel Mode . . . . . . . . . . . . . . . . . 8.3.3 Gender Differences in Trip Frequency . . . . . . . . . . . . . . . 8.3.4 Gender Differences in Spatial Range . . . . . . . . . . . . . . . .

267 267 268 269 270 271 272 273 273 275 275 275 277 279 280

188 188 188 191 199 199 205 206 226 228

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8.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 9

Relationship Between Education and Travel Behaviour . . . . . . . . . . . 9.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.1 Education and Travel Behaviour . . . . . . . . . . . . . . . . . . . . 9.1.2 Research Gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.1 Departure Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.2 Mode Choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.3 Travel Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

287 287 287 289 290 290 290 292 292 295 297 300 301

10 Policy Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Current Policies Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.1 People-Oriented Transport Services . . . . . . . . . . . . . . . . . 10.1.2 Refined and Customised Transport . . . . . . . . . . . . . . . . . . 10.1.3 Inclusive and Fair Transport . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Experience and Lessons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.1 Pay More Attention to Vulnerable Groups . . . . . . . . . . . . 10.2.2 Advance the Comprehensive Transport System . . . . . . . . 10.3 Policy Recommendations: Building Sustainable Transport for All . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.1 People-Oriented Transport Services . . . . . . . . . . . . . . . . . 10.3.2 Refined and Customised Transport . . . . . . . . . . . . . . . . . . 10.3.3 Provide Inclusive and Fair Transport . . . . . . . . . . . . . . . . .

303 303 303 320 330 338 338 341 342 342 345 348

11 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357

Chapter 1

Introduction

1.1 Importance and Necessity 1.1.1 Regional Development Interaction between population growth and transport is a primary driver of regional development. Transport facilities may have a direct influence on population agglomeration through lowering the cost of migration, enhancing interpersonal communication and information exchange and reducing the problems caused by information asymmetry (Chi, 2010). More specifically, they can make great contributions to enhancing regional accessibility, reducing the time cost for the cross-region transfer of products, which ensures that resources and products can be transferred in time among different regions and areas. Fully functional transport facilities can help to provide more effective services for enterprises, promote urban labour to flows across different cities and intensify the process of population agglomeration. Furthermore, the continuous construction of new transport hubs and main roads, as well as the expansion of transport networks, is likely to form a boundary effect in terms of the agglomeration of transport facilities, resulting in production factors flowing with a higher frequency. From a spatial perspective, improving the accessibility of a certain region is likely to generate huge spatial externalities in transport facilities for other regions and to expand the scope and scale of labour flow as well as the flow of production factors, which greatly contributes to making full use of the market potential (Kotavaara et al., 2011; Vaturi et al., 2011). In general, the development and improvement of transport networks seem to have positive impacts on accelerating the process of migration and population growth, and they further promote the development of agglomeration economies across cities. The laws of migration and the factors influencing it were first mentioned and explored in the Six Laws of Migration (Ravenstein, 1889). These laws stated that economic factors are the main cause of migration, and that the development of transport has © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Zhao and D. Yuan, Population Growth and Sustainable Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-7470-0_1

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1 Introduction

a positive effect on increasing the migration rate. On this basis, some scholars put forward the push–pull theory of migration, proposing that migration is the result of combined effects from the push forces around points of origin and the pull forces around destinations (Heberle, 1938). Lee (1966) further categorised push–pull factors into four aspects, namely factors associated with origins, factors associated with destinations, intervening obstacles during the migration process and personal factors. Economic factors have always been among the most important factors in migration (Lewis, 1954). Having an expected wage that outweighs the costs of migration, unemployment risk and other expected economic losses is a main driving factor behind migration (Davanzo, 1976; Todaro & Longman, 1977). Although family factors and other factors related to individual preference also have a remarkable influence on labour migration, income is always likely to be the most important motivator for migration (Stark & Bloom, 1985). The positive influence of the transport network on migration in urban agglomerations can be divided into direct influence and indirect influence. In terms of direct influence, an efficient and high-speed transport network can directly improve regional accessibility, help to overcome time and space restrictions and finally reduce the costs of migration (Chi, 2010, 2012). As a result, population agglomeration tends to follow labour flows among urban agglomeration areas that are accelerating transport development (Vaturi et al., 2011). In terms of indirect influence, a well-built transport network can make a great contribution to the development of agglomeration economies within urban areas. It helps to accelerate the overall development of the economy in urban agglomerations, enhancing the pulling forces of migration to attract more potential migrants. Transport networks with advanced facilities enable enterprises to share the use of a diverse infrastructure and various kinds of intermediate products, which reduces the costs of exchanging products and searching for labour resources. As a result, the development of agglomeration economies can be better promoted, and the process of population agglomeration can be further accelerated (Kotavaara et al., 2011). When labour is allowed to flow freely, the spatial agglomeration can generate knowledge spillovers and stimulate innovation by increasing the frequency of people’s face-to-face communication (Baptista & Swann, 1998; Hoover, 1937), promote the development of industry clusters (Asheim & Coenen, 2005), achieve incremental compensation for scale (Fujita et al., 2001) and further form a virtuous circle between business clustering and economic growth (Audretsch & Feldman, 1996; Martin et al., 2001). Extensive empirical evidence has shown that transport hubs, such as ports and airports, have a significant positive impact on the development of industry clusters (He & Zhang, 2012; Zhang & Lam, 2016). Although the transport network makes contributions to the process of populations’ and industries’ spatial agglomeration, the agglomeration of population and industry has both positive and negative effects on regional economic growth (Chen & Miao, 2010). The negative externalities of populations’ and industries’ spatial agglomeration come from multiple kinds of problems, including severe traffic congestion, environment pollution, inadequate supply of public services, high cost of living, increasing crime rates and so on. As a result, the agglomeration may not be economical when too many firms and people are concentrated in the central city, and it may

1.1 Importance and Necessity

3

lead to the negative effects exceeding positive effects. Then, firms and people will begin to spread within the urban agglomerations, which in turn has a significant impact on the spatial distribution characteristics of transport infrastructure. Based on the perspective of urban agglomerations’ spatial structure, regional development can be divided into four stages. The first stage is isolated central cities, when each city is self-sufficient and intercity connections are quite limited. The second stage is strong central cities, when enterprises begin to concentrate in cities with better economic conditions and more advanced transport infrastructure, resulting in the urban agglomerations forming a core–periphery spatial structure. The third stage is the development of subcentral cities, when some enterprises may relocate to the surrounding areas of central cities after weighing the benefits and the costs of agglomeration, resulting in the urban agglomerations gradually shifting from a core-periphery to a multicore spatial structure. In the last stage, however, multiple central cities may repeatedly go through these stages. Enterprises will continue to agglomerate and spread, and the peripheral areas will move forward to higher development levels, gradually realising economic integration within urban agglomerations and shifting to a networked spatial structure. On the one hand, the evolution of the spatial structure in urban agglomerations strongly affects inter- and intracity traffic flows. On the other hand, it is certain to put forward new requirements for transport networks to provide better support for regional economies. According to the gravity model, the magnitude of intercity traffic flow usually depends on the degree of economic and population agglomeration between the two cities, as well as the spatial obstacles (Tao et al., 2017; Zhang & Zhang, 2016; Zheng et al., 2014). Therefore, to give full play to the positive effects of population agglomeration and to promote agglomeration economies, transport networks need to adapt to the new requirements posed by the changing characteristics of urban agglomerations’ spatial structure. For example, in central cities with high population density and more concentrated businesses, governments are often expected to alleviate traffic congestion problems by investing more in public transport facilities to meet people’s various travel demands (Wang & Xia, 1999). To conclude, the interaction between population growth and transport is a primary driver for promoting high-quality regional development in developing counties.

1.1.2 Transport Efficiency Matches between population growth and transport supply significantly influence transport efficiency. A country’s infrastructure is a necessary condition of and basic guarantee for maintaining the progress of social production and life in a country, as well as promoting the development of the national economy. The efficient operation of infrastructure is conducive to forming beneficial regional development environment, maximising the utilisation value of resources and promoting the modernisation and high-quality development of people’s lives. According to the World Development Report 1994: Infrastructure for Development published by the World Bank,

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1 Introduction

infrastructure is an umbrella term for many activities. Infrastructure covers three types of services: (a) public utilities, including power, telecommunications, piped water supply, sanitation and sewerage, solid waste collection and disposal and piped gas; (b) public works, including roads and major dam and canal works for irrigation and drainage; (c) other transport sectors, including urban and interurban railways, urban transport, ports and waterways, and airports. It is certain that good infrastructure raises productivity and lowers production costs. To contribute to a country’s success, infrastructure must adapt to support changing patterns of demand as the country develops (Bank, 1994). The utilisation efficiency of transport infrastructure refers to the actual frequency and value of the infrastructure that undertakes passenger or cargo transport functions after the construction is completed. Taking the carrier equipment in transport infrastructure as an example, the utilisation index mainly includes the carrier equipment load utilisation index (static load, load capacity utilisation rate, ton/passenger seat utilisation rate, actual load rate), time utilisation index of carrier equipment (operation rate and work rate during working hours) and itinerary utilisation index of carrier equipment (travel utilisation rate, empty driving rate) (Jia, 2011). The utilisation efficiency of transport infrastructure is an important criterion for evaluating the effectiveness of infrastructure investment and construction. It is also a basis for guiding the supply-side adjustment of infrastructure investment and construction. The utilisation efficiency level of transport infrastructure could be affected by various factors, both endogenous and exogenous. In terms of endogenous factors, the construction technology, the way of operating, the way of charging, and the type of service it provides are all likely to affect the actual utilisation. In terms of exogenous factors, the development level of the social and economic environment and whether infrastructure can expand fast enough to accommodate economic growth also affect its utilisation efficiency. Among these exogenous factors, population plays a significant role. For example, in areas with high population densities, railways, roads and other transport infrastructure that meet the travel demand of passengers and cargo tend to be used more frequently. However, areas with low population densities are less likely to use transport infrastructure often. Similarly, in areas with higher levels of aging, public transport and barrier-free transport infrastructure may be used more frequently. In areas with lower per-capita income, low-cost public transport tends to have more supporters, while new vehicles or services that are relatively expensive are less likely to have users. To conclude, the construction level and spatial configuration of transport infrastructure should have a high matching relationship with the population structure to give play to the resource value of transport infrastructure.

1.1.3 Transport Investment The population structure determines regional transport investment through influencing the structure of travel demand. Population structure, also known as the composition of the population, refers to the multidimensional characteristics represented

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during the process of population growth, such as natural (Adlakha & Parra, 2020), socioeconomic (marital status, education level, occupational type, religious belief) or geographical (Hukou location, permanent residence). Based on these characteristics, the total population of a country or a region may be divided into various groups with different socioeconomic attributes (Liu, 1986). Population is the foundation of a country. Population structure can reflect the relationship between one group and the total population, or one group and another group, by calculating the proportion of each group in the total population or the ratio between different groups of population. Through these indexes and ratios, the composition of the population can be learnt and understood better. In recent years, population structure, along with population size, has become an increasingly vital issue in demographic research focusing on population growth. Population structure has three main aspects. Most demographic researchers identify these three aspects as natural population structure, social population structure and regional population structure (Li, 2009). Natural population structure mainly refers to the attributes determined by individuals’ natural physiology and not affected by the outside world, such as age structure and gender structure. Social population structure mainly refers to the attributes determined by individual social and economic conditions and affected by external conditions, such as marriage and family structure, cultural and educational structure, occupational structure and so on. Regional population structure mainly refers to the attributes determined by the spatial location of individual production and living activities, such as urban–rural structure. Population structure often appears with dynamic evolution and spatial heterogeneity. In the time dimension, the population structure changes with the stages of social and economic development. In the space dimension, the population structure appears to have significant differences among areas with different natural resource endowments and socioeconomic environments. Based on the population structure, subjects such as population sociology, population geography and population ecology have been developed and led to a large number of important issues for study. Through exploring the spatial distribution and the historical evolution of the population structure, demographic issues and other important issues for socioeconomic development can be integrated to explore the interrelationships or mutual constraints between population and various natural as well as social elements. Thus, discussing social issues from the perspective of population is of great significance, and it makes great contributions by providing empirical evidence for promoting coordinated and sustainable development in a country or a region. The population structure and transformation trend in the development process of China have unique characteristics and deserve particular notice (Bontje, 2020; Carter, 2018; Lopreite & Zhu, 2020). Since 2013, with the introduction of the 2child policy, China’s fertility policy has been in a new stage. With major changes in fertility policy, population structure tends to change dramatically, which may significantly influence national socioeconomic development. On the one hand, the basic national situation that China has a large population base will not change much. Although the national average annual population growth rate, which was 0.57% from 2000 to 2010, is lower than in other areas in the world, the total population is still

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1 Introduction

maintaining a steady growth. From 2000 to 2010, there was an annual net increase of up to 7.39 million in population. It is predicted that China’s total population will reach a peak of 1.5 billion in 2033 (based on the total fertility rate of 1.8) (Zhang, 2015). The contradiction between population growth and development is still acute. As a result, the fundamental realities of a large population in China place great pressure on social and economic development, as well as on resource and environmental protection. So, population structure has become one of the key issues in China’s development process. On the other hand, the problem of population structure has become increasingly prominent. During the past few decades, fertility policy aimed at keeping a low fertility rate has had an important impact on China’s population size and population structure. Following the reform efforts in relevant policies, population structure is expected to undergo great changes in several ways. Social development may face many challenges, such as population aging and urban– rural older care issues, limited labour supply and demographic dividend shrinkage issues, issues related to new urbanisation and citizenisation, problems caused by the 1-child phenomenon and so on. These social issues urgently require researchers, planners and policymakers to shift their attention from population size to population structure, to adapt to the demographic transition better. Population structure is a key determinant of transport demand structure in a city or a country. Transport demand refers to the demand for spatial movement of people or things in a certain period of time. Transport demand can be satisfied through various types of travel modes, and it can be associated with various travel purposes. A notable feature of transport demand is the completion of spatial displacement within a particular time (Yan, 2012). Depending on the source of the demand, transport demand can be divided into two categories: (a) passenger transport demand with people as the main focus, (b) cargo transport demand with objects as the main focus (Hu et al., 2014). Passenger transport demand is directly generated by human activities, and individuals are directly involved in the process. Cargo transport demand is generated indirectly by human activities, including the transport of food and other cargo transport for consumption. It is obvious that transport demand (basic or non-basic) comes directly or indirectly from people’s daily activities. As the fundamental determinant of the characteristics of social and economic activities in various areas, population structure is closely related to transport demand and its structure. In this context, urban–rural ratios, regional age compositions, gender compositions and family types may cause great differences in transport demand across different areas. For example, areas with a higher proportions of rural residents and areas with fewer rural areas tend to have different lifestyles and travel preferences. Evidence has shown that the characteristics of the trips of rural residents and urban residents differ markedly, and thus their demand for public transport services can also vary greatly (Zhou et al., 2019). In terms of age, previous studies have shown that the mobility patterns of older people are usually characterised by fewer trips that are more often made by non-motorised travel modes and cover shorter distances (Mercado & Paez, 2009; Roorda et al., 2010). Furthermore, older people have fewer trips at night (Scott et al., 2009). Thus, areas with higher levels of aging and those with lower levels of aging will have quite different travel demand and travel characteristics.

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Based on its impacts on transport demand, there is no doubt that characteristics and changes of population structure are major factors in regional investment in transport facilities. It is commonly believed that population structure of an area determines the travel pattern of local residents (Roorda et al., 2010; Zhou et al., 2019). To make regional investment in transport facilities more effective with appropriate spatial patterns and provision scales, it is necessary to focus on the temporal and spatial evolution of population structure. Previous research has proved that only when the supply side meets the basic transport demand of people and keeps the transport system operating under an acceptable load will regions and countries be able to maintain sustainable and high-quality development (Hu et al., 2014). To conclude, evolution in the population structure brings new requirements for the transport supply side. The scale, structure, spatial pattern and service quality of investment in transport facilities all deserve rethinking with full consideration going to the temporal and spatial characteristics of local population structures.

1.1.4 Sustainable and Human-Oriented Transport Population structure is the basis of promoting sustainable and human-oriented transport. As a basic concept guiding the development of cities and countries, sustainability has rich connotations. It does not simply refer to the sustainable development of the economy and society or just sustainable development from a perspective of ecology. Instead, sustainability for a country refers to the green, harmonious and human-oriented development of human–land relationships comprehensively composed by ecology, the economy and society. To achieve this goal, not only a higher goal for efficiency or a fairer ideal, but also new modes of production, new kinds of lifestyle and new ways of thinking are needed. In recent years, the concept of sustainability has become increasingly attractive in the modernisation process of developing countries, guiding regions’ production activities and individuals’ socioeconomic behaviours. Generally, the promotion of sustainable development implies four different, but equally important principles: to safeguard a natural resource base within critical loads, levels and usage patterns; to maintain the option value of a productive capital base for future generations; to improve the quality of life for individuals; and to secure an equitable distribution of life quality (Gudmundsson & Hojer, 1996). Considering that transport is a crucial factor driving a country’s development, it is necessary to optimise the transport system to meet the requirements of sustainable development (Jedliˇcka et al., 2011). Sustainable transport (also called sustainable transportation, sustainable traffic and sustainable mobility in the relevant literature) has rich and comprehensive connotations, and often refers to any form of transport that minimises its negative impacts on the ecological system, such as public transport, car sharing, walking and cycling, as well as new technologies such as electric and hybrid vehicles and biofuels (Joumard & Gudmundsson, 2010). Discussions on the

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1 Introduction

promotion of sustainable transport need to engage with three vital aspects called three Es, namely the environment, equity and the economy (Hall, 2002, 2006). In the past few years, a series of global commitments have provided a clear view of what sustainable transport should look like, including the Sustainable Development Goals, the Paris Agreement on Climate Change, the New Urban Agenda at Habitat III, the Brasilia Declaration on Road Safety and the Ashgabat Declaration. According to the World Bank (2017), sustainable transport can be defined by four aspects: (a) accessible to all, (b) safe and secure, (c) efficient and reliable, and (d) green, clean and resilient. There is extensive evidence that human beings are the key factor in sustainable development for regions and countries (Qun-Ying, 2002). The discussion and evaluation of sustainable development cannot be separated from human beings (Pearson, 2004), and the sustainable development of transport cannot be effectively promoted without fully considering people. Specifically, population structure is the basis for promoting the four aspects of sustainable transport. Firstly, population structure means demographic differentiation, that different groups of people tend to have different preferences and demands for transport. For example, older females are more likely than others to rely on public transport (Ermagun, 2016; Kawgan-Kagan, 2020; Mahadevia & Advani, 2016). To achieve the goal of accessibility to all, transport service needs to be match the local population structure. Secondly, the aging process is an important changing trend of population structure, since older people and young children are more concerned about travel safety and security (Du et al., 2019; Kim, 2019). The goal of safe and secure may not be achievable until enough attention is paid to the age structure of the population. Thirdly, transport efficiency can be directly or indirectly affected by people’s choices of travel mode. In many large cities in China, increasing car ownership and car dependence have caused severe traffic congestion, air pollution and high carbon emissions (World Bank, 2018). Thus, to move towards the sustainable goal of being efficient and reliable, future efforts are needed to reduce people’s car ownership and to promote public transport services that satisfy their actual travel demands. The World Bank also pointed out that efficient mobility solutions should address passenger and freight transport demand with market-oriented strategies (World Bank, 2016). Fourthly, a sustainable transport system needs to be green, clean and resilient, which cannot be effectively implemented without considering the population structure. For age structure, evidence has shown that older people are less likely than others to use private cars (Ermagun, 2016; Kim & Ulfarsson, 2004). Thus neighbourhoods or areas with more older people tend to have smaller demand for private cars, which influences transport energy consumption directly. For education structure, evidence has shown that people with higher education levels may hold more positive attitudes toward green transport and new travel modes. Thus the promotion of green, clean and resilient transport can be given priority in areas with higher education levels and fewer constraints. To conclude, the development of sustainable transport needs to be based on the characteristics and changing trend of population structure. Only when it is adapted to people’s actual demand can transport system achieve the sustainable goal of being accessible, safe, efficient and green.

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1.1.5 Human–Land Harmonisation Coupling of population growth and transport is an important part of human–land harmonisation. The human part of the goal of human–land harmonisation refers to those who engage in production activities or other social activities using a certain mode of production within a certain spatial range such as a region or a country, who are also commonly referred to as social people (Chen, 2013). Human beings have the ability to think and create, as well as mastering tools and technologies, resulting in human society being a huge, comprehensive, and complicated system. Changes in humans’ natural and social attributes greatly affect the characteristics and evolution trend of human society. The land in the goal of human–land harmonisation refers to the geographical environment, which usually differs greatly among different regions and areas. It can be divided into two categories: the natural land environment and the human-made land environment (Chen, 2013). The natural land environment is formed by the combination of natural elements in accordance with certain ecological rules, which can provide the raw materials for production activities. The natural land environment performs as the material basis for the emergence and progress of human society, by providing spaces for human survival and development. The human-made land environment refers to the geographical environment that has been changed under the influence of human beings, including the economic environment, cultural environment and social environment. The human–land relationship is a complex relationship formed by the interaction between humans and the natural or human– made land environment in which they live. The coupling of population growth and transport is an important part of human– land harmonisation. On the one hand, the construction and improvement of the transport system provide a vital foundation for the high-quality development of a region or a country’s population. The modernisation of population in urban and rural areas cannot be achieved without transport services as a guarantee. The importance of transport systems to modern human society is evident from seven main perspectives (Thompson, 1974). Firstly, the unbalanced distribution of natural resources means that residents need to exchange goods between different areas through transport. Secondly, the material civilisation of modern society depends on specialised division of labour, as society needs not only to obtain raw materials from different places, but also to transport products to markets located in different areas. Thirdly, highly efficient transport systems can bring economies of scale supported by technological innovation, automation, mass production and marketing, as well as human activities related to scientific research and education. Fourthly, transport plays an important political and military role, not only providing national defence and political cohesion within the country, but also serving as a representation of comprehensive national strength on the international stage. Fifthly, promoting high-quality and highefficiency transport is conducive to enhancing social communication and integration, which help to solve social problems caused by regional gaps. For example, improving the transport service in less developed areas can help to narrow the regional gaps in productivity, life quality, culture and education. Sixthly, when provided with more

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1 Introduction Issues about our society

Exchange of goods across areas

Unbalanced natural resources

Delivery of raw materials and products

Specialized division of labor

Mass production and marketing

Scale economies effect

National defence and national strength

Political and military role

Social communication and integration

Problems due to regional gaps

More diversified travel choices

People’s daily trips

International cultural communication

Quality of spiritual life

Modern Population Society

Transport System

What transport can do

Fig. 1.1 Importance of the transport system to modern society. Source Developed by the author

sustainable and human-oriented transport services based on their actual needs and preferences, residents have more available and diversified choices for their daily trips as well as other activities. Seventhly, building a high-quality transport system with cross-regional connections strengthens international cultural exchanges, which is necessary to enrich people’s spiritual worlds and to improve their quality of life. Figure 1.1 summarises what transport can do and the issues it addresses. On the other hand, population development also has important impacts on the sustainable development of a country’s transport system. Evidence is available from several perspectives. Firstly, the economic activities of the population accumulate capital for the construction of the transport system, which enables an expansion of the scale and improvement of the structure of the transport infrastructure. Secondly, human innovation achievements in science and technology can be applied to transport engineering to guide continuous progress in traffic operation and management (Byk et al., 2021; Gkoumas et al., 2021). For example, new technology may allow some households to eliminate or reduce their car ownership and dependence on private vehicles (Blumenberg et al., 2021). Thirdly, people’s lifestyles, especially their temporal and spatial travel patterns, determine the traffic order to a certain extent, resulting in morning and evening peaks that significantly affect the utilisation of transport facilities in different hours and places (Truong & Somenahalli, 2015; Yao et al., 2021). It is clear that almost every aspect of human economic production and social life is closely related to the development of the transport system. Thus, the goal of human–land harmonisation may not be achievable without the coupling of population growth and transport. Research on the complicated relationships between population structure and transport demand can provide the necessary theoretical and empirical evidence to guide sustainable and human-oriented development in regions and countries, which is a vital basis for human–land harmonisation.

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1.1.6 Social Transformation Changing population structure is a major aspect of social transformation. At present, Chinese society is going through the process of transformation from a traditional society to a modern society, from an agricultural society to an industrial society, and from a closed society to an open society (Lu & Jing, 1994). The transformation of society brings about the disintegration of the traditional social order and a transformation of traditional social values. Meanwhile construction of the new order and the emergence of new values is taking place. The transformation of society is an integral change of social structure, and its connotation is multilevel and multidirectional. The transformation of society includes not only changes in the way of life, the way of communication and the way of practice at the concrete level, but also changes in the way of thinking, the values and social mindsets at the abstract level. The characteristics of transformation of society in different countries and areas tend to differ greatly. In terms of China, the contemporary transformation of society is reflected in the following aspects (Lin, 2018): (a) from a product economy to a market economy; (b) from an agricultural society to an industrial society and then to an knowledge society; (c) from a rural society to an urban society; (d) from a poor society to a moderately prosperous society; (e) from an authoritarian political society to a democratic political society; (f) from a society governed by man to a society governed by law, and from an ethical society to a society governed by law; (g) from a closed society to an open society; and (h) from a homogeneous society to a pluralistic society. No matter which aspect one focuses on, it is obvious that the transformation of society in contemporary China always takes people as the main focus. The endogenous driving force of transformation comes from the changes when traditional or semitraditional people turn into modern people. The changes of people’s lifestyles, behaviour habits, values and social roles constitute the essence of transformation of the whole society. The direction of social transformation is determined by population development. Therefore, against the background of transformation of society, studying social issues from the perspective of population structure and its changes is of great significance to discuss major issues such as national development, social progress and the quality of people’s lives. Research on the development of urban and regional transport system centring on the population structure could make great contributions to guiding the improvement of the transport system, as well as promoting the high-quality transformation of China’s society.

1.1.7 Transport Equity Population structure deserves more attention for promoting transport equity. For most human societies, individuals with different socioeconomic attributes tend to maintain quite different levels of social resources, resulting in the important social

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1 Introduction

phenomenon called social stratification. Social stratification is defined by demographic sociology as a stratification phenomenon of social groups, which divides social members into different rank sequences and levels according to certain standards (Zhang, 2018). Certain standards often refers to multiple aspects of socioeconomic attributes of individuals, such as occupational type, wealth, social prestige and so on. Due to the ubiquitous gaps among different groups of population, the phenomenon of social stratification occurs in almost in every area at every period of development. Social stratification has become an important research field of sociology, with a variety of theoretical models and analytical frameworks constructed by Western scholars. Based on the phenomenon of social inequality, Karl Marx pointed out that the class structure was the most basic structure of society, and that class conflict was the most basic contradiction in society (Marx & Engels, 2018). There are different divisions of social classes. Some scholars hold the common view that a society can be divided into two classes: the bourgeoisie and the proletariat. Max Weber, however, used the trinity model, which uses wealth, power, prestige to analyse the stratification of human society (Weber, 2020), and Pareto’s (2016) elitist theory divides people into two categories socially: the lower class and the higher class. Generally, power, status and reputation are regarded as the most important criteria for social stratification, and they are usually measured by occupation and income (Zhang, 2018). Considering that an individual’s occupational type and income level may be directly or indirectly affected by his or her gender, age, education level, family and other socioeconomic attributes, the essence of social stratification is actually the stratification of population based on these various attributes. Therefore, population structure classified by gender, age, education level and relevant attributes is an important determinant of social stratification phenomena. Social stratification could be regarded as a reflection of population structure under the influence of social activities (such as social production and interpersonal communication). There is no doubt that the phenomenon of social stratification, which refers to differential access to resources, power, autonomy, and status across social groups is closely related to social inequality. If some groups have access to more resources than others, the distribution of those resources is inherently unequal (McLeod & Nonnemaker, 2013). Therefore, the significant differences between groups with different population characteristics are widely believed to be related to social inequality problems. Improving transport services based on the temporal and spatial characteristics of population structure may effectively alleviate the social inequality from a transport perspective. Research focusing on population structure may provide a method for identifying those difference and analysing their temporal and spatial evolution. Based on the empirical results, the transport system can meet the special needs of vulnerable groups more precisely, and it can coordinate the allocation of resources among different social classes of population to alleviate social conflicts. To conclude, discussions on transport from a perspective of population structure are necessary to build up a theoretical basis for developing refined transport and promoting social equity.

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1.2 Research Purpose and Analytical Framework 1.2.1 Purposes By analysing the relationship between population growth and transport and exploring the characteristics and influencing factors of individuals’ travel behaviour in China from different scales and perspectives, we aim to achieve the following three main research purposes. 1) Exploring China’s population structure and transport development Since the reform and opening up several decades ago, China’s economy has gone through an important period of social transformation. During this period, the demographic transition in China has been accelerated greatly and the social development strategy has been adjusted constantly. Against this background, China’s society, with its unique characteristics, has formed and provides a solid foundation for further development. With continuous improvement in the level and quality of social development, China has gradually transitioned from the early stage to an advanced stage of population development. Under the influence of China’s unique development path and fertility policy, the growth of the population size has tended to slow down, and the diversity and complexity of the population structure has become more prominent. Therefore, the impact of population size on the development of society is gradually reducing, while the contribution of population structure keeps increasing. This study focuses on the temporal and spatial characteristics of the population structure during this critical period of social transformation, and its aim is to promote a balanced, coordinated and sustainable development of society. On the one hand, this book explores the characteristics of the temporal and spatial evolution of China’s population structure. According to the definition in demography, determining population structure means dividing the population into different components according to various characteristics of the population, namely natural aspects (e.g., age, gender), social and economic aspects (e.g., marital status, education level, occupational type, religious belief) and spatial aspects (also called as regional aspects, such as Hukou type, permanent residence) (Liu, 1986). Generally, population structure includes urban–rural structure, gender structure, age structure, occupational structure, family structure, population quality structure, income structure and other connotations. Each kind of population structure represents a certain specific aspect of the population problem associated with social and economic development, and it constitutes some complicated population phenomenon. In this study, we focus on natural aspects of gender structure, family structure and education structure in the social aspect, and spatial effects of urban–rural structure to conduct a comprehensive exploration of China’s population structure. Through exploring the temporal and spatial characteristics of China’s population structure evolution under the 3dimension framework, this paper contributes to the existing literature by depicting the current population structure in China, looking forward to the changing trend of

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1 Introduction

future population, and providing a people-oriented basis for understanding relevant vital issues in the transport field. This book also explores the development of China’s transport service system from a people-oriented perspective by moving forward to analyse the development history and service level of China’s transport system based on the temporal and spatial characteristics of the population structure. With the rapid development of and great achievements in China’s transport, both the supply of transport facilities and the provision of travel services have changed significantly in the past few decades. People’s attitudes and habits about travel behaviour have undergone great transformations as well. As a result, the discussions on people’s travel behaviour cannot be divorced from the status quo of transport development. A detailed and deep description and summary of the construction situation, capacity and accessibility is provided to help readers to build a more comprehensive understanding of China’s transport system, and to lay a foundation for further discussions on the relationship between people’s demographic attributes and transport demand. 2) Identifying the differentiation of travel behaviour In addition to exploring the temporal and spatial characteristics of China’s population and transport, this book analyses the differences in travel behaviour among different groups of people. Some sociologists have found inequalities between different groups, and there is a phenomenon of a high and low orderly echelon similar to a geological structure (Pang, 2013). As a result, the deepening of social stratification can lead to significant social differentiation, and the social strata in different social statuses are separated from each other, gradually forming a fragmented social structure. Social differentiation is believed to include endogenous and exogenous differentiation, with different formation mechanisms and different characteristics, but together constituting all aspects of social structure. For example, the urban– rural disparity caused by the separation in the urban and rural household registration system, which is also called a dualistic urban–rural structure, has become a typical differentiation phenomenon in China’s society. Based on the dualistic urban–rural structure, there are various practical social problems such as urban diseases, urban villages and the rich–poor gap. Whether these problems can be solved effectively will directly determine people’s life quality and society’s development level. As an indispensable subsystem of the social system, transport serves the basic needs of social production and life, with humans as the main participants. There is no doubt that social differentiation is significantly represented in the transport system as well. This research focuses on travel behaviour against the background of social differentiation, including urban and rural difference, family difference, gender difference, and education difference. Through statistical description and modelling estimation, we aim to present the characteristics of social differentiation associated with travel behaviour qualitatively. Further efforts are also made to analyse possible causes for the formation of social differentiation to provide empirical evidence for the provision of transport services according to actual local conditions and population travel demands.

1.2 Research Purpose and Analytical Framework

15

3) Providing policy implications on an empirical basis Last but not the least, this book provides policy implications for building sustainable and people-oriented transport systems. Based on the differentiated characteristics of travel behaviour among different population groups, as well as the current situation and future changes in China’s population structure, we aim to conduct a comprehensive review of previously proposed transport policies and put forward policy recommendations targeted at the transport system on an empirical basis. With those efforts, this study contributes to promoting the improvement of China’s transport system in both quality and efficiency, with the implementation of China’s Traffic Power Strategy. In developing countries such as China, high-quality development in the future is especially likely to be people-oriented. According to Hu’s report at 18th Party Congress in 2012, As we advance toward the future, the whole party must more purposefully take putting people first as the core requirement for thoroughly applying the scientific outlook on development. We must always make realising, safeguarding and developing the fundamental interests of the overwhelming majority of the people the starting point and goal of all the work of the party and country. We must respect the people’s creativity, protect their rights and interests, and make continued progress in enabling the people to share in the fruits of development and in promoting well-rounded development of the person.

The Outline for Building China into A Country with a Strong Transport Network issued by the Central Committee of the Communist Party of China (CPC) and the State Council also puts people first. It aims to build up a country with a strong transport network that satisfies the people, has strong guarantees, and makes China the leading power in the world. It is obvious that the people-oriented concept plays a vital role in promoting development and construction in the new era of China, increasing people’s happiness, and alleviating social contradictions. Guided by the people-oriented concept, China has made great efforts to develop a high-quality, refined and customised transport system in recent years. As for a new model of transport service such as shared bicycles and online-rental cars, management rules have been greatly improved. As for the demand for scheduled travel, interline journeys and disadvantaged travellers, more diverse and flexible services have been provided. However, with the continuous transformation in China’s population growth and the significant improvement in quality of life, people’s actual travel demand has become increasingly diverse. At present, the supply of some new transport services has not yet matched the local population structure and the people’s actual demand. For example, there are far too many shared bicycles in some cities are and the supply far exceeds the demand for daily trips (Beijing Daily, 2020; News, 2020). In some areas with aging populations, barrier-free facilities for travel have not yet been built, leaving disadvantaged groups lacking in access to necessary livelihood opportunities (Net, 2019). Without full attention to population growth and population structure, the service quality and usage efficiency of a comprehensive transport system may be greatly limited.

16

1 Introduction

Taking the facts into consideration, this book puts forward policy prospects from several aspects by combining the conclusions of empirical analysis and a review of current policies. With these research goals, it contributes to promoting the refined and customised development of transport, matching the supply with the demand, and creating a sustainable and people-oriented transport system with better quality and higher efficiency.

1.2.2 Framework To achieve the research goals, the analytical framework of this book centres on population structure and travel behaviour, as shown in Fig. 1.2. The research framework has four main parts, which are organised as follows. 1) China’s transport system: Achievement in the past Before discussing the relationship between population structure and transport demand, we provide a comprehensive and descriptive analysis of the development characteristics of China’s transport system. Chapter 2 mainly summarises and analyses the development history and spatial distribution characteristics of China’s comprehensive transport system. The body of Chap. 2 covers two aspects. In the first section, we analyse the development of China’s transport in the past few decades along a timeline. The formation of current transport system has gone through three periods, namely a preliminary form, the development of a complete system, and optimisation. For each stage, we have discussed the overall spatial layout of the transport network, as well as the policy and planning related to the construction and optimisation of China’s transport system. In addition, the practical effects of China’s regional development strategies in transport field are discussed. The first section will help readers to develop a better understanding of China’s transport system. In the second section, we analyse the time-space changes in China’s fixed asset investment in transport facilities since the start of the 21st century to take a look at China’s progress in building transport facilities. The descriptive analysis is based on five transport sectors, namely highway, water transport, aviation, railway, and logistics (which consists of warehousing and postal services in this context). Based on the year-by-year changing trend of fixed asset investment in those different transport sectors, the current situation and future development of China’s transport facilities, in different sectors and across different regions, are also discussed in combination with socioeconomic, demographic and industrial backgrounds in Chinese society. 2) China’s demographic characteristics and the trend of changes In this part, the focus in on the main characteristics of China’s population growth in the past several decades and the changing trends in four aspects of population structure. The four aspects of population structure are dualistic urban–rural structure,

1.2 Research Purpose and Analytical Framework

17

Fig. 1.2 Framework of the book

family structure, gender structure and education structure. The analysis of population structure features in this part lays a basic and empirical foundation for further discussions on people-oriented and sustainable transport. In the first section of Chap. 3, we focus on the periodic changes in population growth since the founding of the People’s Republic of China in 1949. By observing and exploring the changing trends of several indicators such as birth rate, death rate, natural growth rate, and total fertility rate, we analyse the transition of China’s

18

1 Introduction

population reproduction in recent decades and discuss possible reasons for the phased changes of population in combination with vital policies and events through different developing periods. In the second section of Chap. 3, we focus on one basic and unique aspect of China’s population, namely the dual urban–rural population structure. Based on indicators such as urbanisation rate, the urban population and the rural population, we analyse the temporal evolution and spatial distribution characteristics of China’s urban–rural structure. Furthermore, taking other social and economic attributes of the population into consideration, such as residents’ personal income level and occupation categories, we have made efforts to explore the differences between urban residents and rural residents against the background of China’s dual social structure. In Chap. 4, we focus on the other three vital aspects of population structure, namely family structure, gender structure and education structure. In the first section, we focus on analysing the temporal evolution and spatial distribution characteristics of China’s family structure, based on family size, family type (according to the number of generations or the family composition), number of family workers, and family income level. This part depicts the current population situation in China and its changing trend in the family, which is the basic unit in society. In the second section, we turn to the temporal evolution and spatial distribution characteristics of the gender structure of China’s population, looking at sex ratio, male population and female population as the main indicators. In addition, this part analyses the differences between male residents and female residents, especially the differences in their travel behaviour. Through combining this information with other social and economic attributes of the population, such as education level, labour force participation rate and unemployment rate, we explore how the gap between male and female has formed further. In the third section, we pay attention to the evolution characteristics and spatial distribution characteristics of China’s population quality structure. Considering the three dimensions of population quality: natural population quality (such as mortality, average life expectancy), social quality (such as education level) and population fitness (such as prevalence, accident casualties), we focus on people’s education levels when exploring population quality to make it more quantifiable and to provide empirical basis for further discussions. 3) Transport service quality and accessibility based on population Chapter 5 analyses the development status of China’s comprehensive transport system through quantitative evaluation from two perspectives. The first section makes an evaluation analysis of China’s transport service level on the basis of the characteristics of population spatial distribution. More specifically, taking the descriptive analysis of China’s transport development in previous chapters as a basis, the spatial match between population and transport infrastructure distribution is measured by an indicator called service quality using national spatial data on population and transport infrastructure. The service quality of transport in this section is analysed by calculating the proportion of population within the transport infrastructure’s direct service range to the total population, with the prefecture-level

1.2 Research Purpose and Analytical Framework

19

administrative unit as the basic unit for analysis. The results are of great significance to describe the proportion of the population in the prefecture-level areas who can reach the transport facility within a certain time or certain distance accurately. Therefore, the analysis in this section will give readers a better understanding of the service quality of the transport infrastructure when taking demographic factors into consideration. To draw an overall picture of China’s comprehensive transport system, this section organises the population-based evaluation analysis of transport services through four perspectives, namely railways, highways, high-speed railways, and air transport. By comparing the transport infrastructure’s service quality in 2010 and 2015, this section analyses the temporal changing trends of China’s transport system in recent years in terms of satisfying people’s travel demand. Through visualising the evaluation results in different prefecture-level cities in China, this section also discusses the spatial distribution characteristics of the transport infrastructure’s service quality. The second section focuses on analysing the regional accessibility of China’s transport network based on population weighting. By combining data about population and transport infrastructure, we have made efforts to measure the accessibility of China’s comprehensive transport system while considering demographic factors. In this book, regional accessibility refers to the convenience of using a specific transport infrastructure to get from the origin to the destination. The measurement and evaluation of regional accessibility have received a lot of attention in various research fields, including education (Li, 1999), health (Gutiérrez Javier et al., 2009), tourism (Li & Yiman, 2001), and regional development (Zhu & Suxia, 2004). Previous studies mainly focused on the spatial patterns of transport network accessibility among different cities (Gutiérrez Javier, 2001; Javier et al., 1996). As part of the 5Ds of the built environment, accessibility plays an important role in the process of urban development. On the one hand, the micro-foundation of the interaction between the transport network and economic development is that transport network can have an influence on regional accessibility (Zhou et al., 2015). On the other hand, regional accessibility is also the fundamental path through which the transport infrastructure can affect people’s economic and social activities and their spatial distribution. The evaluation analysis of regional accessibility of China’s transport system based on population weighting provides empirical evidence for optimising transport services to be more in harmony with the national demographic characteristics, thus making contributions to building a high-quality and people-oriented transport system. 4) Population differentiation of travel behaviour In this part, we take the previous analysis of China’s population structure as a basis, and move forward to exploring the relationship between people’s social and economic attributes and their travel behaviour. Indicators about social and economic attributes include urban/rural attributes, family attributes, gender and education level. Discussion on travel behaviour focuses on travel mode choice, travel purpose, trip frequency or intensity, and travel space scope. Methods including descriptive statistical analysis and multivariate regression model are used to explore the travel behaviour caused by

20

1 Introduction

population differentiation of social and economic attributes quantitatively. This part explains the relationship between population structure and transport demand. The empirical analysis in this part is organised through four perspectives: Firstly in Chap. 6, we focus on the relationship between the urban–rural structure of the population and residents’ travel behaviour. Data resources include China Family Panel Studies (CFPS), Beijing’s travel survey data, and data from a national small town survey from 2016. There is a focus on the differences in residents’ travel behaviour and its possible causes between urban residents (including the city centre and the suburbs) and rural residents. Secondly in Chap. 7, we focus on the relationship between family structure of population and residents’ travel behaviour. The data mainly came from a travel survey of residents of Beijing in 2015. The study analyses the influence of residents’ family (family size, family type) on their travel behaviour. The role of family factors in residents’ travel decisions is explored through descriptive statistics and quantitative tests. Thirdly in Chap. 8, we focus on the relationship between the gender structure of the population and residents’ travel behaviour. Data from a travel survey of residents collected in Beijing in 2015 is used to analyse the gender differences in residents’ travel behaviour in Megacities in China (taking Beijing as an example), including the gender differences in residents’ travel purposes, travel modes, trip frequency and spatial scope. Fourthly in Chap. 9, we focus on the relationship between the quality structure of the population (represented by education level) and residents’ travel behaviour. Using the travel survey data of residents from Beijing in 2015, we analyse the relationship between the travel behaviour of residents in China’s megacities (taking Beijing as an example) and their education levels. We explore the differences among people with different education levels in their starting time of travel, mode choice of travel and purposes of travel. 5) Transport development based on population structure Based on the research on the multidimensional attributes of China’s population structure and its temporal and spatial characteristics, as well as the analysis on the differentiated performance of residents’ traffic and travel behaviour in different types of population, we put forward policy implications for China’s future transport system development from a macro perspective. Chapter 10 has three sections. The first section offers a brief but comprehensive summary of the current policies and implementation measures for high-quality and sustainable transport development. This section covers three topics: people-oriented transport, refined and customised transport, and inclusive and fair transport. The second section focuses on what we can learn from the analysis of the relationship between population and transport. The third section discusses the future development of China’s comprehensive transport system. Based on the previous empirical conclusions, we give a look ahead at what we can do to help to build a sustainable transport system, which should be more people-oriented, refined, and inclusive, to satisfy the development of the population. Based on these three sections, this chapter makes contributions to improving the

1.3 Main Data Source

21

quality and efficiency of China’s transport system to make it more people-oriented, more sustainable, and better adapted to the changing development of population. The five main bodies of this book are interconnected and progressive, and they jointly realise the research goal of studying transport behaviour from the perspective of population structure. It provides an empirical basis for building an inclusive transport system and improving transport development policies.

1.3 Main Data Source 1.3.1 Data for National-Level Analysis 1) China Family Panel Studies (CFPS) China Family Panel Studies (CFPS) is a national-level, comprehensive tracking survey project designed and conducted by the Institute of Social Science Survey, Peking University. The aim of this survey is to provide a database for academic research and public policy analysis by tracking and collecting data at the individual, family and community levels to learn about the changes in China’s society, economy, population, education and health. The surveys were funded by Peking University Project 985, and they have received strong support from the former National Population and Family Planning Commission. CFPS focuses on the economic and non-economic welfare of residents, as well as a wide range of issues including economic activity, access to education, family relationships and family dynamics, migration, and people’s physical and mental health. The target sample is 16,000 households located across 25 provincial administrative units in China (with Hong Kong, Macao, Taiwan, Xinjiang Uygur Autonomous Region, Tibet Autonomous Region, Qinghai Province, Inner Mongolia Autonomous Region, Ningxia Hui Autonomous Region, and Hainan not included). According to CFPS’s definition, a family is an independent economic unit in which at least one person has Chinese nationality living in a traditional residential house. Family members are those economically connected and living together temporarily at the time of the survey, as well as those not at home temporarily who are still economically connected, have blood/marriage/adoption relations and have lived together for at least 3 months. CFPS started its preliminary work in 2007, and it carried out a preliminary investigation with initial visits and follow-up visits to a total of 2,400 households in Beijing, Shanghai and Guangdong in 2008 and 2009. In 2010, CFPS officially carried out the baseline survey in 25 provincial administrative units. A total of 19,986 sample households were initially selected, and 14,960 households (including 33,600 adults, and 8,990 children) were eventually fully interviewed. In this survey, the cumulative response rate at family level was 81.25%, the cooperation rate was 96.58%, the contact rate was 84.13%, and the rejection rate was 2.67%. At the individual level, the response rate was 84.14%, the cooperation rate was 87.01%, the contact rate

22

1 Introduction

was 96.7%, and the rejection rate was 8.47%. The CFPS 2010 baseline survey was a local survey, focusing on determining the socioeconomic attributes, member composition and members’ individual information of the sample households. In addition, basic information on the members who were not at home during the survey was collected in the family member questionnaire. All family members identified in the 2010 baseline survey will be permanently tracked as CFPS genetic members. The biological/adopted children of these genetic members in the future are also regarded as genetic members for permanent follow-up investigations. Based on the preliminary work in 2007 and the baseline survey, six surveys in total have been carried out every 2 years from 2010 to 2020. The data for this study mainly comes from the CFPS in 2010, supplemented by some data information from other survey years. To provide a brief look, part of the questionnaire for 2010 CFPS is in Table 1.1. 2) Spatial data on population and transport To investigate the service quality and regional accessibility of China’s transport system based on national and regional population, we used spatial data on population and transport. Table 1.1 Part of the questionnaire for the CFPS in 2010 Subjects

Questions

Answers

All

In the last non-holiday month, how many hours per day did you spend on the following activities on average?

Transport activities: hours All travel activities during a 24 h period, especially commuting to and from school or workplace Entertainment and social activities: hours

Leisure and entertainment activities, social interaction activities at the mercy of individual freedom

In the last 3 months, the two most commonly used travel modes for your daily trips were:

Walking, bicycle, electric bicycle, motorcycle, bus, subway, taxi, private car, agricultural vehicle, other

Only two kinds of travel mode to be chosen

Have you ever taken a train?

Yes or no

Have you ever taken an aeroplane?

Yes or no

Have you ever been to Hong Kong/Macao/Taiwan?

Yes or no

Have you ever been abroad?

Yes or no

Adults

Instruments

1.3 Main Data Source

23

The spatial data on population refers to the population grid data in 2010 and 2015 from the Peking University Geographic Data Platform,1 which provides us with the temporal and spatial characteristics of China’s population. The grid data of the national population in kilometres is based on the county-level population statistics, and it includes the geographical distribution of population-natural elements as well. Spatial interpolation is used to generate the 1 km * 1 km grid data, and the value of each grid refers to its population size. The population grid data can represent the spatial characteristics of the national population more accurately, including quantity, density and distribution pattern. Combined with the spatial layout of transport infrastructure, the relationship between the transport system and the population can be further discussed. The spatial data on transport is the spatial information of China’s transport infrastructure in 2010 and 2015 from the Peking University Geographic Data Platform. Taking the difference in operating mode into consideration, we used multiple methods and various elements to analyse different types of transport infrastructure. In terms of railways, high-speed railways, and civil airports, station spots are set as the basic units for analysis. In terms of expressways and national roads, the analysis is focused on the whole roads. 3) Statistical Yearbooks The China Statistical Yearbooks are a series of annual statistical publications, which comprehensively present all the aspects of national socioeconomic development. They mainly show data from the year before publication and key statistical data in recent years and some historically important years. These data are provided by China’s National Bureau of Statistics.2 The yearbooks contain information about various aspects of China’s society and population. Taking the China Statistical Yearbook 2020 as an example, there are 28 chapters: 1. General Survey; 2. Population; 3. National Accounts; 4. Employment and Wages; 5. Prices; 6. People’s Livelihoods; 7. Government Finance; 8. Resources and Environment; 9. Energy; 10. Investment in Fixed Assets; 11. International Trade and Economic Cooperation; 12. Agriculture; 13. Industry; 14. Construction; 15. Wholesale and Retail Trades; 16. Transport, Postal and Telecommunication Services, and the Software Industry; 17. Hotels, Catering Services and Tourism; 18. Financial Intermediation; 19. Property; 20. Science and Technology; 21. Education; 22. Public Health and Social Development; 23. Culture and Sports; 24. Public Administration, Social Security and Social Organization; 25. Urban, rural and regional development; 26. Main Social and Economic Indicators of the Hong Kong Special Administrative Region; and 27. Main Social and Economic Indicators of the Macao Special Administrative Region; 28. Main Social and Economic Indicators of the Taiwan Province. The Main Social and Economic Indicators of Other Countries/Regions are in an appendix. 1

Data source: Peking University Geographic Data Platform, https://geodata.pku.edu.cn/index.php? c=tag&a=list&kw=beijingdaxuedilishujupingtai. 2 Data source: National Bureau of Statistics, http://www.stats.gov.cn/tjsj/.

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1 Introduction

For this book, China Statistical Yearbooks provide us with comprehensive data and information about China’s population. Based on these datasets, as well as the statistical yearbooks published by provinces, the characteristics and changing trends of China’s population growth and structure are analysed in Chaps. 3 and 4. Based on a comprehensive understanding of China’s population, the relationship between population growth and sustainable transport can be further discussed. Furthermore, only when we have taken a deep look at the statistical data of China can we put forward targeted and effective policy recommendations for the future development of transport in China. In addition to statistical datasets from these national and provincial yearbooks, data from previous studies and published reports also contribute our discussions. By comparing the results and findings in this book with those from previous studies, we hope to provide a clearer and more comprehensive perspective for understanding the historical characteristics and future changing trend of China’s population growth.

1.3.2 Data for City Cases 1) National Detailed Survey of Small Towns In June 2016, the Ministry of Housing and Urban–Rural Development of China organised professionals from 13 research institutes, including the Urban Planning Institute of China Architectural Design Institute, Peking University and Tongji University, to conduct a detailed survey of 121 small towns selected from 31 provincial administrative units (including provinces, autonomous regions and municipalities directly under the central government). Stratified sampling, cluster sampling and random sampling were adopted for the selection of survey sample. Considering the development level and the distance to city centres (near, medium and far), 3–4 towns were selected from each provincial administrative units to ensure the survey range covered most typical towns as far as possible. On-site surveys focusing on various aspects of socioeconomic issues were conducted in each sample town. By randomly selecting 120 households in each town, we ensured adequate coverage of the rural residents in the township. The survey was designed with five types of questionnaires: one for households in townships, one for villagers, one for enterprises, one for urban–rural space, and one for the town’s basic information. In total 211 survey questions were asked, and 1,305 indicators were collected. The data for this study mainly came from the questionnaires for households in townships and for villagers. In detail, the data about town residents came from the permanent households in the township areas, with 120 households in each surveyed town. This questionnaire focused on information about town residents’ family members, employment and income, consumption habits, entertainment and leisure life, and cultural identity. Data about villagers came from the selected three

1.3 Main Data Source

25

villages in each surveyed town. Generally 5–6 households were chosen randomly to conduct the household level survey for those villagers. This questionnaire focused on the village residents’ family composition, travel purpose and trip frequency to or from the town, as well as their daily activities. 2) Beijing Comprehensive Travel Survey (BCTS) In 1986, the Beijing Municipal Commission of Transport launched the first citywide travel survey, the BCTS, and it continued to conduct the second, third, fourth and fifth travel surveys every 5 years from 2000 to 2015. This series of surveys has provided a reliable database for studying traffic issues and the development of the transport system in Beijing. Taking the third survey in 2010 as an example, the valid sample included around 116,000 individuals from approximately 47,000 households, with a sampling rate of 1.5% of the total population. Residents’ travel behaviour was collected and analysed at traffic zone level, based on the Traffic Analysis Zones system, with the zones designated by the Beijing Municipal Commission of Transport. The areas range from 0.13 to 5.25 km2 . To cover the city’s traffic issues comprehensively, BCTS contains surveys focusing on eight different topics, covering every aspect of residents’ travel behaviour as well as their daily activities. The main survey contents on each topic are listed in Table 1.2. Of these, the survey on residents’ travel behaviour is the one involving the largest number of indicators and the most comprehensive contents. All the permanent residents of Beijing (including the agricultural population, non-agricultural population Table 1.2 Main contents of Beijing’s comprehensive travel survey in 2010 Survey

Sub-survey

Topic

Subjects

Survey on residents’ travel behaviour

Survey on residents’ travel behaviour

Resident and family members who live or temporarily stay in Beijing

About 82,000 households were sampled at a rate of 2.5%

Survey on migrant population

Survey on migrant population

Migrant population without Beijing registered permanent residence

About 8,500 persons

One-day survey of motor vehicles

One-day survey of public buses One-day survey of public taxis

Motor vehicles with registered licenses within Beijing

15,000 public buses, 6,000 taxis

Survey on road inspection line

Survey on road inspection line

Motor vehicles

8 lines, 300 sections

Survey on attraction points of traffic flow

Survey on attraction points of traffic flow

Attraction point

600 points, 18 types

Survey on land use

Survey on land use

Land use status of the city

Total sample (continued)

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1 Introduction

Table 1.2 (continued) Survey

Sub-survey

Survey on public transport

Survey on the subway Subway lines and bus lines Survey on public buses

Topic

Total samples

Survey on population and social issues

Survey on population and employment Survey on education Survey on vehicle ownership

Total samples

Social institutions, school, companies and registered motor vehicles

Subjects

Table 1.3 Main contents of the survey on residents’ travel behaviour according BCTS in 2010 Contents

Foci

Family background

Address, housing type, building area, annual income, vehicle ownership, vehicle mileage

Family members

Gender, age, household registration, education level, occupation, family member relationship, driving licence, bus ticket, school/company address

Travel behaviour

Information on all trips, including departure time, arrival time, travel purpose, travel mode, travel cost, etc.

and collective household registration), as well as migrants who live in the residential areas of these local permanent residents for a long period (more than 6 months) or a short period (less than 6 months) are included into the survey sample. The focus is on both the central area and the suburban areas of Beijing, and all streets, towns and district offices in the 18 districts (after Xuanwu District is merged into Dongcheng District and Chongwen District is merged into Xicheng District, there are 16 districts) are covered. The survey sample size in each district is determined according to local population situation. The main contents of the survey on residents’ travel behaviour are in Table 1.3.

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Chapter 2

China’s Transport System

2.1 History of China’s Comprehensive Transport Corridor 2.1.1 Rudimentary Stage This stage refers to the period from the eighth Five-Year Plan to the 10th Five-Year Plan, with the five north–south and seven east–west national arterial highways and two north–south and three east–west waterways as the main transport corridors in China. The construction of China’s comprehensive transport system first began in the 1960s, and since the 1980s, theories about transport corridors have been put into practice to guide the transport planning and development in China. At the beginning of China’s reform and opening up, demand for traffic increased rapidly along with the rapid growth of the national economy. Since the construction of transport infrastructure was seriously lagging and transport capacity remained quite insufficient, national economic development faced many bottlenecks. For example, most trunk roads in cities had serious traffic congestion problems around entrances and exits. To crack these problems, the transport industry of China carried out bold innovation and exploration. Continuously widened second-level roads and newly built highways had made contributions to alleviating traffic stress. However, the problems of frequent traffic accidents and chaotic traffic order still had not been effectively solved. In this context, the capacity and service quality of trunk road transport was still unable to satisfy the actual demand for economic and social development in China. Under the new situation, how to make good use of the limited natural resources and human capital to deal with the most prominent problems and challenges China’s transport development faced, as well as quickly relieving the bottleneck of traffic problems on the national economy, had become a major topic for the transport department. In 1990, the Ministry of Transport proposed that starting from the eighth FiveYear Plan period, a comprehensive transport system mainly focusing on national © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Zhao and D. Yuan, Population Growth and Sustainable Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-7470-0_2

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arterial highways should be built, including five north–south and seven east– west national arterial highways with a total length of 350,000 km. These highways would connect the capital, provincial capitals (including autonomous regions and centrally administered municipalities), special economic zones, key transport hubs and major sea and land ports in China. The five north–south national arterial highways are the Tongjiang–Sanya National Highway, Beijing–Zhuhai National Highway, Chongqing–Beihai National Highway, Beijing–Fuzhou National Highway, and Erenhot–Hekou National Highway. The seven east–west national arterial highways are the Lianyungang–Khorgos National Highway, Shanghai– Chengdu National Highway, Shanghai–Ruili National Highway, Hengyang– Kunming National Highway, Qingdao–Yinchuan National Highway, Dandong– Lhasa National Highway, and Suifenhe–Manzhouli National Highway. The construction of these 12 national arterial highways was completed in 2008 (Fig. 2.1). In the early 1990s, the Planning Scheme for the Overall Layout of Major Water Transport Corridors in China proposed that the overall layout of major water transport corridors of China would be completely built up within 30 years (1991–2020). The water transport corridor system would feature two north–south and three east–west water transport corridors, including the north–south corridor along the coast, the north–south corridor along the Jing–Hang Grand Canal and the Huai River, the east– west corridor along the Yangtze River and its main tributaries, the east–west corridor along the Pearl River and its main tributaries, and the east–west corridor along the Heilong River and the Songhua River. Specifically speaking, the two north–south water transport corridors consist of a coastal transport corridor and an inland transport corridor involving the Jing–Hang Grand Canal, the Suzhou–Shanghai Outer Port

Fig. 2.1 The five north–south and seven east–west national arterial highways system

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Line, the Changxing–Huzhou–Shanghai Waterway, the Huai River, and the Shaying River. The inland transport corridor is composed of Class III waterways for 1,000 ton barges and some Class IV waterways for 500 ton ships, involving 20 rivers (river stretches), with a total length of about 15,000 km. The three east–west water transport corridors are the corridor of the Yangtze River system (involving the main lines of the Yangtze River, the Jialing River, the Xiang River, the Chashui River, the Gan River, the Xin River, the Jianghuai Canal and the Liangsha Canal), the corridor of the Pearl River system (involving the main lines of the Xi River, the You River, the Beipan River, the Hongshui River, the Liu River, the Qian River and the Hutiaomen Waterway), and the corridor along the Heilong River and the Songhua River. In terms of the development of two north–south and three east–west water transport corridors system, regional strategy implemented by the government had provided effective and high-quality guidance for the practicalities of construction. In the early stage of China’s reform and opening-up (1980–1992), with the implementation of the priority economic development strategy in coastal areas, the spatial focus of transport construction shifted to East China, with emphasis on the coastal ports, railways and national highways. This had made great contributions to the promotion of opening up and the development of an export-oriented economy. In the early 1990s, China started to implement an all-around opening-up policy, and a strategy for coordinated regional development was put forward. Interregional transport corridors, such as the Beijing–Kowloon Railway and the Nanning–Kunming Railway, became the focus of transport construction. This strategy accommodated the transport demand due to increasing interregional communications and international trade. Since then, the Western Development Strategy has vigorously encouraged further transport construction in the western regions. The rapid growth of urban agglomerations has forced rapid transport across cities and transport corridors between different urban agglomerations to be the focus of China’s transport development. Later, the proposal of the Belt and Road Initiative placed the construction of transnational transport corridors in a more important position, greatly strengthening the transport links between China and neighbouring countries. Since the 1980s, the development of China’s transport corridor has started to emphasise quality and grade improvement, and it has put the expansion of the road network into a secondary position. Enhancing the quality and efficiency of arteries became the priority, and work was progressively carried out from east to west. In the early period, the construction and renovation projects of transport lines were mainly conducted in the eastern regions. From 1980 to 1990, the eastern regions’ share of railways in China increased by 1.4 percentage points, and the length of highways in the eastern regions increased by 195,000 km, accounting for 42.3% of the total length of newly built highways in the whole country in the same years. During the ninth Five-Year Plan period, the number of newly built projects on transport lines in the central and western regions gradually increased. Compared with the eighth Five-Year Plan period, the central regions’ and western regions’ share of transport investment had increased respectively by 0.20 and 7.58 percentage points, and the proportions of their newly built highways in the total length of newly built highways across China had increased to 35% and 32% respectively. Previously, China had

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basically formed a comprehensive transport network linking the eastern, central, and western regions.

2.1.2 Initial Stage of Formation This stage refers to the period from the 10th Five-Year Plan to the 12th Five-Year Plan, with the eight north–south and eight east–west railway transport corridors and five north–south and five east–west comprehensive transport corridors being the main transport corridors in China. In the mid-to-late 1990s, China’s expressways underwent rapid development and showed remarkable localisation characteristics, extending from local areas to larger regions then to the whole country. In the early stage, expressways were constructed mainly with to connect provincial capital cities with other important cities in the provinces, and to develop infrastructure characterised by axial and linear distribution, in a bid to form a local transport network centring on the provincial capital. With the expansion of local transport networks, the interprovincial regional transport network gradually took shape, and the arterial corridors were formed in the main contact directions of interprovincial transport. By 2005, the framework of the expressway networks in the Beijing–Tianjin–Hebei region, the Yangtze River Delta region, Shandong Province, Henan Province and other areas was basically complete. An integrated arterial expressway network was gradually built in China, including the Beijing–Shanghai Expressway, the Shenyang–Haikou Expressway (Shenyang– Zhanjiang Section) and the Beijing–Hong Kong–Macau Expressway. On this basis, transport lines became increasingly denser, and an integrated national transport network was eventually developed. The 10th Five-Year Plan for developing a comprehensive transport system clearly proposed the goal of building up eight north–south and eight east–west railway transport corridors. The total length of railways was expected to reach up to 34,000 km, covering most of the large or medium-sized cities, the major tourist spots, and the sales market of most products in China. The eight north–south railway transport corridors are the coastal corridor, the Beijing–Shanghai corridor, the Beijing– Hong Kong (Taipei) corridor, the Beijing–Harbin, Beijing–Hong Kong (Macau) corridor, the Hohhot–Nanning corridor, the Beijing–Kunming corridor, the Baotou (Yinchuan)–Hainan corridor and the Lanzhou (Xining)–Guangzhou corridor. The eight east–west railway transport corridors are the Suifenhe–Manzhouli corridor, the Beijing–Lanzhou corridor, the Qingdao–Yinchuan corridor, the Eurasian Land Bridge corridor, the Shanghai–Chengdu corridor, the Shanghai–Kunming corridor, the Xiamen–Chongqing corridor and the Guangzhou–Kunming corridor. To build up these railway transport corridors as expected, China formulated development strategies according to the actual development status of different regions. In detail, the eastern region focused more on adjusting the structure, including gradually dividing the busy arteries into passenger and freight lines, building passenger-only lines and high-speed railways, building supplementary branch lines and connecting

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lines where necessary, and promoting technical upgrading (especially electrification) for existing lines. The central region focused more on aligning itself with the Western Development Strategy through the construction of transport arteries connecting the east and the west as well as arteries for outbound energy transport, to expand the transport network and to form high-quality transport corridors with a large carrying capacity. At the same time, the western region focused more on building transport corridors linking the central and eastern regions, improving the internal transport network, and building new international transport corridors, to deal with the limited access to external transport in the southwest and northwest regions. In 2007, the National Development and Reform Commission promulgated the Medium and Long-Term Development Plan for the Comprehensive Transport Network (2006–2020), which clearly proposed the construction of a comprehensive transport system with five north–south and five east–west comprehensive transport corridors. This system would consist of railways, highways, airways, part of waterways and oil-gas pipelines. In detail, the five north–south comprehensive transport corridors are as follows: 1. North–south coastal transport corridor. Starting in Heihe in the north, it passes through Harbin, Changchun, Shenyang, Dalian, Yantai, Qingdao, Lianyungang, Shanghai, Ningbo, Wenzhou, Fuzhou, Xiamen, Shantou, Guangzhou, Shenzhen, Zhanjiang and Haikou to Sanya in the south. 2. Beijing–Shanghai transport corridor. Starting in Beijing in the north, it passes through Tianjin, Jinan, Xuzhou, Bengbu and Nanjing to Shanghai in the south. It is a comprehensive transport corridor that connects North China and East China, especially the two international cities of Beijing and Shanghai. 3. Manzhouli–Hong Kong–Macau–Taiwan transport corridor. Starting in Manzhouli in the north, it passes through Qiqihar, Baicheng, Tongliao, Beijing, Shijiazhuang and Zhengzhou to Wuhan, where two branches emerge. From there, one passes through Changsha and Guangzhou to Hong Kong (Macau), and the other passes through Nanchang and Fuzhou to Taipei. In addition, the transport corridor also includes the Qiqihar–Harbin line. 4. Baotou–Guangzhou transport corridor. Starting in Baotou in the north, it passes through Xi’an, Chongqing and Guiyang to Liuzhou, where two branches emerge. From there, one branch goes to Guangzhou and the other to Zhanjiang. This corridor is an inland marine transport corridor in West China, connected to international marine and air transport networks through Guangzhou Port, Zhanjiang Port, and hub airports in Guangzhou. 5. Linhe–Fangchenggang transport corridor. Starting in Linhe in the north, it passes through Yinchuan, Lanzhou, Chengdu, Kunming and Nanning to Fangchenggang in the south. It is the second north–south comprehensive transport corridor in the inland western regions. The five east–west comprehensive transport corridors are as follows: 1. Northern Northwest marine transport corridor. Starting in Tianjin and Tangshan in the east, it passes through Beijing, Datong, Hohhot, Baotou, Linhe, Hami,

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3.

4.

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Turpan and Kashgar to Torugart Port in Xinjiang in the west. It is a marine transport corridor connecting Northwest China with East China. Qingdao–Lhasa transport corridor. Starting in Qingdao in the east, it passes through Jinan, Dezhou, Shijiazhuang, Taiyuan, Yinchuan, Lanzhou, Xining and Golmud to Lhasa in the west. It is connected to the international marine transport network through Qingdao Port. Eurasian Land Bridge transport corridor. Starting in Lianyungang in the east, it passes through Xuzhou, Zhengzhou, Xi’an, Lanzhou and Urumqi to the Dzungarian Gate in the west. This corridor is part of the Eurasian Land Bridge. The transport corridor along the Yangtze River. Starting in Shanghai in the east, it passes through Nanjing, Wuhu, Jiujiang, Yueyang, Wuhan and Chongqing along the Yangtze River to Chengdu in the west. Shanghai–Ruili transport corridor. Starting in Shanghai and Ningbo in the east, it passes through Hangzhou, Nanchang, Changsha, Guiyang and Kunming to Ruili Port in the west. It is connected to the international marine transport network through Shanghai Port and Ningbo Port and to the Southeast Asian road network through Ruili Port.

In addition, China’s comprehensive transport system at this development stage also included the following four international transport corridors: 1. International transport corridor in Northeast Asia (including the China–Mongolia corridor). International branch lines from Shenyang to Dandong, Harbin to Tongjiang, Harbin to Suifenhe, Changchun to Hunchun, Jining to Erenhot, Ejin Banner to Ceke, and Linhe to Ganqimaodu are laid out around the main axes consisting of the north–south coastal transport corridor, the Manzhouli–Hong Kong–Macau–Taiwan transport corridor and the northern Northwest transport corridor. 2. International transport corridor in Central Asia. International branch lines from Urumqi to Khorgas are laid out around the main axes consisting of the northern Northwest marine transport corridor and the Eurasian Land Bridge transport corridor. 3. International transport corridor in South Asia. International branch lines from Lhasa to Yadong, Lhasa to Zhangmu, and Kashgar to Khunjerab are laid out around the main axes consisting of the Qingdao–Lhasa transport corridor and the Shanghai–Ruili transport corridor. 4. International transport corridor in Southeast Asia. International branch lines from Nanning to Friendship Pass, Kunming to Mohan, and Kunming to Hekou are laid out around the main axes consisting of the Shanghai–Ruili transport corridor and the Linhe–Fangchenggang transport corridor. Figure 2.2 displays these transport corridors. During this phase, the comprehensive transport corridors were mainly built to connect all municipalities directly under the central government, provincial capitals, cities specifically designated in the development plan, and other cities with populations of more than 500,000 in China, linking the major transport ports by land,

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Fig. 2.2 The comprehensive transport corridors system and national transport hubs

sea and air, as well as connecting regional economic centres and key industrial and energy bases. What is more, the plan was designed to provide multiple transport corridors for interregional and interprovincial communication between the western, central and eastern regions to meet the development needs of land use and national defence. An emphasis of this development was to promote the coordination of arterial railways, arterial highways, high-grade waterways of inland rivers, main airways and major oil-gas pipelines in comprehensive transport corridors, as well as fully linking these corridors with international transport networks. As a result, great contributions were made to enhancing the diversity and efficiency of travel modes in China and promoting the formation of an integrated transport system with the advantages of different regions compensating each other.

2.1.3 Stage of Improvement This stage refers to the period since the 13th Five-Year Plan, during which China’s transport system has been characterised by 10 north–south and 10 east– west comprehensive transport corridors and a rapidly improving transport corridor network. Based on the previously built national transport network, China’s National Development and Reform Commission successively released the Medium- and Long-Term Railway Network Plan and the 13th Five-Year Plan for the Development of a Modern Comprehensive Transport System. In detail, the Medium- and Long-Term Railway

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Network Plan proposed the construction of a national high-speed rail network based on the eight north–south and eight east–west railway transport corridors, and the 13th Five-Year Plan for the Development of a Modern Comprehensive Transport System proposed the construction of 10 north–south and 10 east–west comprehensive transport corridors based on the five north–south and five east–west comprehensive transport corridors. The high-speed rail network will be built by speeding up the existing eight north– south and eight east–west railway transport corridors system (Fig. 2.3). China is committed to promoting and supporting rapid transport between large and mediumsized cities, as well as between these cities and surrounding small cities. By building a high-speed rail network with the eight north–south and eight east–west transport corridors as arteries and intercity railways as supplements, China aims to realise the 1–4 h transport circle between adjacent large and medium-sized cities and the 0.5– 2 h transport circle within each urban agglomeration. While optimising the network layout for developed eastern areas, China will also make efforts to accelerate the improvement of the regular rail network, expand the coverage of the rail network in central and western areas, and build interregional fast corridors with a large carrying capacity, which will help the central and western regions to alleviate poverty. In 2017, the National Development and Reform Commission issued the 13th Five-Year Plan for the Development of a Modern Comprehensive Transport System, emphasising that the construction of a modern comprehensive transport system is an objective requirement to support a well-off society in an all-round way. Based on the extension and adjustment of the five north–south and five east–west comprehensive

Fig. 2.3 The national high-speed rail network consisting of eight north–south and eight east–west high-speed railways

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transport corridors, the 10 north–south and 10 east–west comprehensive transport corridors came into being. In detail, the north–south comprehensive transport corridors are as follows (see Fig. 2.4): 1.

Coastal transport corridor. Starting in Tongjiang, it passes through Harbin, Changchun, Shenyang, Dalian, Qinhuangdao, Tianjin, Yantai, Qingdao, Lianyungang, Nantong, Shanghai, Ningbo, Fuzhou, Xiamen, Shantou, Guangzhou, Zhanjiang and Haikou to Fangchenggang and Sanya (extended). 2. Beijing–Shanghai transport corridor. Starting in Beijing, it passes through Tianjin, Jinan, Bengbu and Nanjing to Shanghai and Hangzhou (extended). 3. Beijing–Hong Kong–Macau–Taiwan transport corridor. Starting in Beijing, it passes through Hengshui, Heze, Shangqiu, Jiujiang, Nanchang, Ganzhou and Shenzhen to Hong Kong (Macau). Its branch lines pass through Hefei, Huangshan and Fuzhou to Taipei (adjusted). 4. Heihe–Hong Kong–Macau transport corridor. Starting in Heihe, it passes through Qiqihar, Tongliao, Shenyang, Beijing, Shijiazhuang, Zhengzhou, Wuhan, Changsha and Guangzhou to Hong Kong (Macau) (adjusted). 5. Erenhot–Zhanjiang transport corridor. Starting in Erenhot, it passes through Jining, Datong, Taiyuan, Luoyang, Xiangyang, Yichang and Huaihua to Zhanjiang (added). 6. Baotou–Fangchenggang transport corridor. Starting in Baotou (Mandula), it passes through Yan’an, Xi’an, Chongqing, Guiyang and Nanning to Fangchenggang (adjusted). 7. Linhe–Mohan transport corridor. Starting in Linhe (Ganqimaodu), it passes through Yinchuan, Pingliang, Baoji, Chongqing and Kunming to Mohan and Hekou (added). 8. Beijing–Kunming transport corridor. Starting in Beijing, it passes through Taiyuan, Xi’an and Chengdu (Chongqing) to Kunming (added). 9. Ejin–Guangzhou transport corridor. Starting in Ejin (Ceke), it passes through Jiuquan (Jiayuguan), Xining (Lanzhou), Chengdu, Luzhou (Yibin), Guiyang and Guilin to Guangzhou (adjusted). 10. Yantai–Chongqing transport corridor. Starting in Yantai, it passes through Weifang, Jinan, Zhengzhou, Nanyang and Xiangyang to Chongqing (added). The east–west comprehensive transport corridors are as follows: 1. 2. 3.

Suifenhe–Manzhouli transport corridor. Starting in Suifenhe, it passes through Mudanjiang, Harbin and Qiqihar to Manzhouli (added). Hunchun–Erenhot transport corridor. Starting in Hunchun, it passes through Changchun, Tongliao and Xilinhot to Erenhot (added). Northern Northwest transport corridor. Starting in Tianjin (Tangshan, Qinhuangdao), it passes through Beijing, Hohhot, Linhe, Hami, Turpan, Korla and Kashgar to Torugart, Irkeshtam and Khunjerab. Its branch line at the western end starts in Hami and passes through Jiangjunmiao to Altay (Jeminay) (extended).

Fig. 2.4 Ten north–south and ten east–west comprehensive transport corridors

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2.1 History of China’s Comprehensive Transport Corridor

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4.

Qingdao–Lhasa transport corridor. Starting in Qingdao, it passes through Jinan, Dezhou, Shijiazhuang, Taiyuan, Yinchuan, Lanzhou, Xining and Golmud to Lhasa (unchanged). 5. Eurasian Land Bridge transport corridor. Starting in Lianyungang, it passes through Xuzhou, Zhengzhou, Xi’an, Lanzhou, Urumqi and Jinghe to the Dzungarian Gate and Khorgas (extended). 6. The transport corridor along the Yangtze River. Starting in Shanghai, it passes through Nanjing, Wuhu, Jiujiang, Wuhan, Yueyang, Chongqing, Chengdu, Nyingchi, Lhasa and Shigatse to Yadong and Zhangmu (extended). 7. Shanghai–Ruili transport corridor. Starting in Shanghai (Ningbo), it passes through Hangzhou, Nanchang, Changsha, Guiyang and Kunming to Ruili (unchanged). 8. Shantou–Kunming transport corridor. Starting in Shantou, it passes through Guangzhou, Wuzhou, Nanning and Baise to Kunming (added). 9. Fuzhou–Yinchuan transport corridor. Starting in Fuzhou, it passes through Nanchang, Jiujiang, Wuhan, Xiangyang, Xi’an and Qingyang to Yinchuan (added). 10. Xiamen–Kashgar transport corridor. Starting in Xiamen, it passes through Ganzhou, Changsha, Chongqing, Chengdu, Golmud and Ruoqiang to Kashgar (added). By 2018, the expressway networks in China had all been connected except for those in the Tibet Autonomous Region, Hainan Province and the Taiwan region, making a great contribution to supporting the rapid social and economic development of China. Unlike the expressways, the construction of the high-speed rail network, which began in the early 21st century, focused more on building a nationwide arterial network after the exploration and experimentation in the point-to-point construction stage. In 2013, the Beijing–Shanghai High-Speed Railway, the Beijing– Guangzhou High-Speed Railway and other national arterial high-speed railways took the lead in being fully opened to traffic. Subsequently, while further strengthening the nationwide arterial network, China made vigorous efforts to develop intercity railways around regional central cities such as Guangzhou, Nanjing, Chengdu, Wuhan, Changsha and Zhengzhou, to expand the high-speed rail network and to promote the construction of local railway networks. The differentiated expansion patterns of expressways and high-speed railways in China mainly stem from their differences in technical features, service objects and investors. Studies have shown that the competitive transport distance of expressways is within 300 km, and that they can be used for both passenger and freight transport to meet the regional transport needs of central cities. High-speed railways have a larger transport volume, higher speed, and longer transport distance, which are more suitable for serving the frequent travel demand in the areas along major national and regional development axes. In terms of the investment mechanism, localised management was first implemented for highways. Since 2003, self-raised funds by local governments have accounted for over 40% of investment in highways, and in some years the proportion has been close to 50%. However, self-raised funds by local

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governments accounted for less than 30% of investment in railways from 2008 to 2014, and the proportion was only 21.07% in 2014. Due to these differences, local governments play a more important role in the construction of highways. Therefore, expressways tend to extend from local areas to larger regions and to the whole country with provincial capital cities as centres, while high-speed railways mostly extend through the construction of interregional arteries. At this stage, China’s airport and port systems have undergone a transition from individual development to cluster development. Airport construction revolved around hub airports and arterial airports. In 2000, there were 119 civil airports in China altogether, including 57 airports with at least 4D ratings and 62 airports with no more than 4C ratings. Almost all of China’s municipalities and provincial capitals had airports with at least 4D ratings, constituting the basic structure of China’s airport system. In recent years, the construction of airports has been focused on two aspects. On the one hand, efforts have been made to increase the capacity of hub and arterial airports through the expansion, relocation and construction of airports. On the other hand, efforts have been made to optimise the spatial layout of airports through the construction of feeder airports around hub airports and arterial airports to expand the service range of civil aviation. By 2017, there were 229 civil airports in China, including 49 with at least 4E ratings in all municipalities and provincial capitals, 35 with 4D ratings, and 145 with no more than 4C ratings. The six airport clusters in North China, Northeast China, East China, South Central China, Southwest China, and Northwest China had already been formed.

2.2 Fixed-Asset Investment in Transport Facilities 2.2.1 Highway Transport In the 21 century, China’s fixed-asset investment in highway transport has maintained swift growth against the background of rapid social and economic development. Since 2001, the cumulative fixed-asset investment in China’s highway transport has grown steadily. With the continuous growth of GDP, the cumulative investment in highway transport has also represented a continuous rise, with the growth speed even greatly exceeding national population growth. According to the reported statistical data, the growth rate of China’s fixed-asset investment in highway transport has basically followed the growth rate of GDP (Figs. 2.5 and 2.6). Based on the national statistical data in recent years, changing trends of fixed-asset investment in highway transport in different regions of China have differed greatly from each other. The growth rate of fixed-asset investment in highway transport in the east and west regions has basically changed in line with the growth rate of regional GDP, which might be due to the relatively developed economy in East China and the relatively high financial expenditure in West China. Meanwhile, the growth of fixed-asset investment in highway transport in Central China has been

2.2 Fixed-Asset Investment in Transport Facilities

43

Fixed-asset investment (Billions of CNY)

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Year Fig. 2.5 Cumulative fixed-asset investment in highway transport. Data source Ministry of Transport of the People’s Republic of China Fixed-asset investment

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Fig. 2.6 Growth rate of cumulative fixed-asset investment in highway transport, total population and GDP. Data source Ministry of Transport of the People’s Republic of China

significantly faster than the growth of regional GDP, indicating that the government has attached greater importance to investment in highway construction. Northeast China is generally believed to have quite advanced transport infrastructure; however, its fixed-asset investment in highway transport has fluctuated to some extent due to the slowdown in economic and population growth (Figs. 2.7, 2.8, 2.9 and 2.10).

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Fixed-asset investment (Billions of CNY) GDP (Billions of CNY)

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Year Fig. 2.7 Fixed-asset investment in highway transport in East China. Data source Ministry of Transport of the People’s Republic of China

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Year Fig. 2.8 Fixed-asset investment in highway transport in Central China. Data source Ministry of Transport of the People’s Republic of China

2.2.2 Waterway Transport In recent years, the growth of China’s fixed-asset investment in waterway transport has gradually slowed down, which may be partly explained by the changes in

2.2 Fixed-Asset Investment in Transport Facilities

45

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Year Fig. 2.9 Fixed-asset investment in highway transport in West China. Data source Ministry of Transport of the People’s Republic of China

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Year Fig. 2.10 Fixed-asset investment in highway transport in Northeast China. Data source Ministry of Transport of the People’s Republic of China

international trade. According to the nationwide statistics, 2009 turned out to be the inflection point of growth. From 2002 to 2009, the fixed-asset investment in China’s waterway transport gradually increased. Since 2009, although China’s GDP has maintained rapid and continuous growth, there appears to have been a decline in the growth rate of fixed-asset investment in waterway transport. Statistical data has

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shown that the national fixed-asset investment in waterway transport has basically remained unchanged. Based on the nationwide statistical data for several years, the figures show that the growth rate of GDP has remained higher than that of China’s cumulative fixed-asset investment in waterway transport since 2006 (Figs. 2.11 and 2.12). In terms of the investment structure of China’s waterway transport in recent years, statistical evidence has shown that the proportion of cumulative fixed-asset investment in coastal waterway transport has remained higher than that in inland waterway transport before 2017. However, the gap seems to be narrowing. Since 2018, the Fixed-asset investment (Billions of CNY) GDP (Billions of CNY)

Total population (Millions)

80000

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Year Fig. 2.11 Fixed-asset investment in waterway transport. Data source Ministry of Transport of the People’s Republic of China Fixed-asset investment

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Fig. 2.12 Growth rate of cumulative fixed-asset investment in waterway transport. Data source Ministry of Transport of the People’s Republic of China

2.2 Fixed-Asset Investment in Transport Facilities Coastal waterway transport

47 Inland waterway transport

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%

60 50 40 30 20 10 0 20032004200520062007200820092010201120122013201420152016201720182019

Year Fig. 2.13 Structure of cumulative fixed-asset investment in waterway transport. Data source Ministry of Transport of the People’s Republic of China

proportion of cumulative fixed-asset investment in inland waterway transport has exceeded that in coastal waterway transport. Along with the rapid growth of GDP and people’s disposable income in China, household consumption has played an increasingly important role in boosting economic growth, and the economy has become less dependent on foreign trade. These facts may be among the reasons for the continuous extension of the transport network to the hinterland and significant improvement in the proportion of fixed-asset investment in inland waterway transport (Fig. 2.13). For the department structure of China’s waterway transport in recent years, statistical evidence has shown that investment in ports taken the largest share, but its share declined slightly from 2014 to 2016. The proportion of fixed-asset investment in the construction of waterways and seamarks kept increasing, while that of other fixedasset investment departments (such as investment in the purchase of ships) gradually declined (Fig. 2.14). In terms of the spatial distribution pattern, there are significant geographical differences in China’s cumulative fixed-asset investment in waterway transport, with the changing trends varying greatly across different regions. For example, the growth rate of cumulative fixed-asset investment in inland waterway transport in East and Central China appeared to go in line with that of the regional water freight turnover. Meanwhile, the growth rate of cumulative fixed-asset investment in inland waterway transport in West China was also quite consistent with that of the regional water freight turnover before 2013, but then dropped significantly. As for Northeast China, the fixed-asset investment in inland waterway transport appeared to grow faster than that of water freight turnover, but it has declined year by year while the regional water freight turnover has gradually increased to remain at a high level in recent years.

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Ports

Ships

Others

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%

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Year Fig. 2.14 Department structure of cumulative fixed-asset investment in waterway transport. Data source Ministry of Transport of the People’s Republic of China

In addition, there are also some geographical gaps in China’s cumulative fixedasset investment in terms of coastal waterway transport, but different regions have shown a quite consistent changing trends. Except for the central regions without coastlines, the total fixed-asset investment in coastal waterway transport in the eastern, western and northeastern regions of China all appeared to be gradually decreasing with time (Figs. 2.15, 2.16, 2.17 and 2.18). Fixed-asset investment

9000 8000 7000 6000 5000 4000 3000 2000 1000 0

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Year Fig. 2.15 Structure of cumulative fixed-asset investment in inland waterway transport in East China. Data source Ministry of Transport of the People’s Republic of China

2.2 Fixed-Asset Investment in Transport Facilities

49

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Year Fig. 2.16 Structure of cumulative fixed-asset investment in inland waterway transport in Central China. Data source Ministry of Transport of the People’s Republic of China

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Year Fig. 2.17 Structure of cumulative fixed-asset investment in inland waterway transport in West China. Data source Ministry of Transport of the People’s Republic of China

2.2.3 Railway Transport Along with the rapid development of the economy and society, China’s fixed-asset investment in railway transport has maintained high-speed growth in the 21st century. Since 2001, China’s GDP has generally increased year by year, and the cumulative fixed-asset investment in railway transport has also continued to rise even faster than population growth. After 2014, the growth in China’s fixed-asset investment

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2019

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Year Fig. 2.18 Structure of cumulative fixed-asset investment in inland waterway transport in Northeast China. Note The statistical criteria for highways and waterways in 2008 have been adjusted. Data source Ministry of Transport of the People’s Republic of China

in railway transport appeared to slow down, resulting in the total investment scale basically remaining unchanged in the following years. According to the national statistical data represented in Figs. 2.19 and 2.20, the growth rate of China’s cumulative fixed-asset investment in railway transport and the GDP growth rate appeared quite different before 2012, but have shown a relatively consistent trend in recent few years. Fixed-asset investment (Billions of CNY) GDP (Billions of CNY)

Total population (Millions) 120000

9000 8000 7000 6000 5000 4000 3000 2000 1000 0

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Year Fig. 2.19 Cumulative fixed-asset investment in railway transport. Data source Ministry of Transport of the People’s Republic of China

2.2 Fixed-Asset Investment in Transport Facilities

Fixed-asset investment

51

GDP

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40 %

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Fig. 2.20 Growth rate of cumulative fixed-asset investment in railway transport. Data source Ministry of Transport of the People’s Republic of China

Along with the promotion of a high-quality, comprehensive transport system in China, the fixed-asset investment in railway transport has gradually shifted from mass implementation of new projects to equal emphasis on implementation of new projects and adjustment of existing projects. According to the national statistical data about fixed-asset investment in railway transport from 1997 to 2014, different types of fixed-asset investment in railway transport, including basic construction, purchase of rolling stock, and upgrading, showed a similar changing trend of slow growth before 2005. After that, the investment in basic construction and the purchase of rolling stocks surged, while that in upgrading grew slowly, resulting in wider gaps among different types of investment. However, the investment in upgrading began to increase rapidly after 2014 to reach a high level almost equal to that of basic construction (Figs. 2.21 and 2.22). From 1997 to 2005, the proportions of the three types of investment were relatively stable, with only small changes according to the statistical results. Since 2005, the proportion of investment in basic construction has first shown an increase and then gone through a continuous decrease. The proportions of investment in the purchase of rolling stocks and upgrading reduced at first but then increased. The fixed-asset investment in basic construction mainly covers investment in new railways and investment in line reconstruction. Statistical data have shown that the proportion of investment in new lines gradually increased, while that of investment in line reconstruction appeared to reduce gradually. With the large-scale construction of high-speed railways in China, investment in new railways is expected to play an important role for the next few decades. In addition to the changing balance between different investment types, China’s fixed-asset investment in railway transport has gradually shifted from mass implementation of new projects to equal emphasis on implementation of new projects and optimisation of existing projects (Fig. 2.23). Furthermore, statistical data released by Ministry of Transport of the People’s Republic of China has also shown that the share of operating revenue of national

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Purchase of rolling stock

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Year Fig. 2.21 Changing trends of different types of fixed-asset investment in railway transport. Data source Ministry of Transport of the People’s Republic of China

Upgrading

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Year Fig. 2.22 Proportions of different types of fixed-asset investment in railway transport. Data source Ministry of Transport of the People’s Republic of China

railways was higher than that of local railways from 2005 to 2010. In recent years, the proportions of the have two fluctuated but have gradually stabilised, resulting in the gap narrowing significantly. In terms of the source type, the operating revenue of the railway transport sector has been dominated by revenue from freight transport, followed by that from passenger transport. At the same time, the proportion of revenue

2.2 Fixed-Asset Investment in Transport Facilities

53

New railways

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Year Fig. 2.23 Proportions of different types of fixed-asset investment in the basic construction of railway transport. Data source Ministry of Transport of the People’s Republic of China

from freight transport in the operating revenue of government-owned railways has slightly reduced, while that from passenger transport has increased (Fig. 2.24). Operating revenue of local railways 1.6 1.4 1.2 0.8

%

1 0.6 0.4 0.2 2013

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Operating revenue of national railways 99.2 99.1 99 98.9 98.8 98.7 98.6 98.5 98.4 98.3

Year Fig. 2.24 Proportions of operating revenue of national and local railway transport. Data source: Ministry of Transport of the People’s Republic of China

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2.2.4 Air Transport With the rapid growth of GDP, China’s fixed-asset investment in basic construction in the aviation industry has also increased. Meanwhile, fixed-asset investment in the purchase of aircraft has fluctuated considerably, declining in 2006 and 2009 but gradually rising after 2010 (Fig. 2.25). In recent years, with a move from the construction of new transport facilities to the optimisation of existing facilities, China’s fixed-asset investment in civil aviation has declined from previous years. In 2019, China’s total fixed-asset investment in civil aviation was 181.99 billion CNY, a decrease of 13.79 billion CNY from 2018. In 2020, the total fixed-asset investment in civil aviation was 162.759 billion CNY, a decrease of 19.231 billion CNY from 2019. As proposed in the outline of the 14th Five-Year Plan, it is necessary to accelerate the construction of world-class port and airport clusters, steadily build feeder airports, general airports and cargo airports, and actively develop general aviation. According to statistical data reported by the People’s Daily, in the first half of 2021, fixed-asset investment in the entire civil aviation industry reached 43.5 billion CNY, showing a year-on-year increase of 8.5%. At the end of June, 23 airports were under construction. From strengthening the reconstruction and expansion of hub airports to speeding up the construction of small and medium-sized airports in remote areas, a well-functioning modern national airport system with a much more complete spatial layout is being formed at an accelerated pace. Purchase of aircraft

Basic construction

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Fig. 2.25 Fixed-asset investment in air transport. Data source Ministry of Transport of the People’s Republic of China

2.2 Fixed-Asset Investment in Transport Facilities

55

2.2.5 Logistics In the logistics industry in China, the total fixed-asset investment increased slowly from 1991 to 2002, followed by a significant decline. Since 2008, the rapid development of e-commerce and online shopping has greatly increased the scale and speed of product flow across regions, and more and more people tend to rely on online shopping instead of physical stores. A dramatic change in lifestyle has taken place. The informatisation and networking of commodity transactions has been a strong driver for the rapid rise of fixed-asset investment in China’s logistics industry. China’s fixed-asset investment in logistics is mainly composed of fixed-asset investment in warehousing and in the postal service. With the rapid development of the logistics industry and the continuous growth of total fixed-asset investment, fixed-asset investment in warehousing has increased continuously, and even faster than the growth of total fixed-asset investment in logistics. Meanwhile, new fixedasset investment in warehousing has reduced since 2016. It is speculated that gradual saturation may occur in China’s warehousing construction in the future. China’s fixed-asset investment in the postal service has shown a slightly different changing trend from that in warehousing. From 2004 to 2011, fixed-asset investment in postal service fluctuated greatly, with the total investment scale being relatively small. Along with the rapid development of e-commerce in China, there is an increasing demand for cargo transport. In line with this, China’s fixed-asset investment in the postal service has risen sharply since 2011 and gradually stabilised at a quite high level (Figs. 2.26 and 2.27). Fixed-asset investment in warehousing

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Fig. 2.26 Fixed-asset investment in warehousing and its growth rate. Data source Ministry of Transport of the People’s Republic of China

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Growth rate of investment in postal service

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Fig. 2.27 Fixed-asset investment in postal service. Data source Ministry of Transport of the People’s Republic of China

2.3 Conclusion In this chapter, we have summarised the development history of China’s comprehensive transport system and the spatial characteristics of the fixed-asset investment in the transport infrastructure. The first section has presented the development achievements of China’s transport in the past few decades. In the second section, we analysed the temporal and spatial changes in China’s fixed asset investment in the 21st century to take a comprehensive look at China’s building progress in transport fields. Based on integrated sources of nationwide statistical data, the findings in this chapter have provided an important basis for further exploring the relationship between population structure and travel behaviour as well as transport demand. The formation of China’s comprehensive transport system has taken place in three stages: preliminary development, development into a complete system, and optimisation. In the first stage, the main transport corridors in China were five north–south and seven east–west national arterial highways and two north–south and three east–west waterways. The construction of transport corridors was guided and promoted through the implementation of national-level development strategies for different regions. During this stage, China gradually formed a comprehensive transport network linking the east, the middle and the west. In the second stage, the main transport corridors in China were eight north–south and eight east–west railway transport corridors and five north–south and five east–west comprehensive transport corridors. Development strategies were formulated considering the actual development status of different regions. In the third stage, China’s transport system was characterised by 10 north–south and ten east–west comprehensive transport

2.3 Conclusion

57

corridors and a rapidly improving transport corridor network. The highways and high-speed railways have had different spatial patterns for expansion, while China’s airport and port systems have experienced a shift from monolithic development to cluster development. According to the statistical data, China has made a great achievement in building a modern and high-quality comprehensive transport system through these three stages. As the essential framework of the nationwide comprehensive transport system, transport corridors connect the key nodes with high population density and rapid development within the national territory. The 10 north–south and ten east–west corridors in China have already connected cities with dense populations and encouraged fast economic growth across regions. At the same time, a comprehensive transport system composed of four major pivot points, namely Beijing–Tianjin, Shanghai–Nanjing–Hangzhou, Guangzhou–Shenzhen and Chengdu–Chongqing, has formed. In terms of the fixed-asset investment in China’s transport infrastructure, statistical analysis of five transport sectors has provided empirical evidence of a continuous increase in the total scale of fixed-asset investment in the past few decades, stepping into a stage of stable optimisation recently. Before the reform and opening up, China’s transport infrastructure was in the restoration stage. Under the background of the planned economy system, transport infrastructure was regarded as the driving force of national economic recovery, and it received massive capital investment as well as strong policy support. Since the reform and opening up, transport infrastructure has gone through a stage of large-scale investment and construction, during which the scale, quality and technology of China’s transport infrastructure have been significantly improved. According to the statistical data, the growth rates of China’s fixed-asset investments in highways, waterways, railways, aviation and logistics have followed GDP growth rates, which is consistent with previous studies on the relationship between economic development and infrastructure construction. On the one hand, investment in transport infrastructure provides the basic conditions for inter-regional connection and division of production, which is vital for accelerating the socioeconomic development of a country or a region. On the other hand, rapid economic growth can provide financial guarantees for the increase and improvement of transport infrastructure. The two complement each other and jointly promote each other. Currently, the infrastructure construction in China is showing a trend of shifting from mass implementation of new projects to equal emphasis on implementation of new projects and optimisation of existing projects. Although the fixed asset investment in various transport sectors of China has achieved remarkable results, the coupling of incremental investment distribution and population spatial patterns still needs to be strengthened. Statistical analysis shows that the spatial distribution of railways and highways could match population growth better. Evidence has also shown that the centre of construction has moved to the central and western regions. For example, from 2011 to 2016, the provinces with higher differentiation indexes of graded road network and expressway networks relative to population and economic growth were mostly located in the central and

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western regions. In addition, the growth of graded road and expressway mileage in these provinces has been faster than that of population and economy. This difference in the changing trend of fixed asset investment in transport among different regions may be related to the significant gaps in economic development status, fiscal expenditure ratios and natural resource endowment of eastern China and western China. To sum up, China’s comprehensive transport system has already achieved the goals of filling in the gaps and making up for the shortcomings, and it should gradually transit to the focus of providing more efficient and high-quality transport services. Transport infrastructure is essential for regional production and socioeconomic development, as well as providing a basic guarantee for satisfying people’s new demands and expectations for transport efficiency and quality. With the continuous improvement in the social economy and the optimisation of fertility policy, China’s population has entered a critical period of transition. The temporal and spatial characteristics of population growth, especially in urban–rural structure, family structure, gender structure and quality structure, are certain to put forward new requirements for transport services. In future, the development of China’s transport system should be more closely related to the actual situation of the national population and adapt to changes in transport demand based on population growth to build a modern, high-quality and sustainable comprehensive transport system.

Chapter 3

Population Growth and Urban–Rural Structure

3.1 Population Growth From 1949 to 2019 (70 years), China’s total population grew from 540 million to 1.4 billion. With a large population base, the changing trend of China’s population is generally represented as a stable, linear growth. Meanwhile, the natural population growth rate seems to be gradually decreasing, which means that the speed of China’s population growth is slowing down (Fig. 3.1). Natural population growth rate refers to the ratio of the increased natural population (the number of births minus the number of deaths) to the average population (or mid-term population) during a certain period (usually a year), which is expressed in ‰. The natural population growth rate is determined by birth rate and death rate together. The calculation formula can be represented as: Natural population growth rate = (annual number of births − annual number of deaths) /annual average population ∗ 10000/00 = birth rate − death rate

.

Changes in birth rate and death rate since the founding of the People’s Republic of China in 1949 are in Figs. 3.2 and 3.3. Due to the great optimisation achievements in healthcare services and sanitary conditions, the quality of people’s life has improved significantly, resulting in rising average life expectancy. The death rate also fell sharply and then stabilised at a low level in a short period. Taking these facts into consideration, the change in the fertility rate is usually seen as the main driving factor for changes in China’s total population growth rate. Total fertility rate is an intuitive and commonly used indicator to measure the fertility level of a country. It refers to the average number of children born to women of childbearing age, and it can be used to represent residents’ behaviour, desire and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Zhao and D. Yuan, Population Growth and Sustainable Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-7470-0_3

59

60

3 Population Growth and Urban–Rural Structure Growth rate

%

2018

2015

0.00 2012

0 2009

2.00 2006

20000 2003

4.00

2000

40000

1997

6.00

1994

60000

1991

8.00

1988

80000

1985

10.00

1982

100000

1979

12.00

1976

120000

1973

14.00

1970

140000

1955

16.00

1949

10,000 persons

Total population 160000

Year Fig. 3.1 Total population and growth rate in China. Data source China’s statistical yearbook, published by the National Bureau of Statistics 40 35 30 25

‰

20 15 10 5 2019

2016

2013

2010

2007

2004

2001

1998

1995

1992

1989

1986

1983

1980

1976

1973

1970

1967

1964

1961

1958

1955

1952

-5

1949

0

-10

Year Fig. 3.2 Natural population growth rate in China. Data source China’s statistical yearbook, published by the National Bureau of Statistics

3.1 Population Growth

61 Birth rate

Death rate

50 45 40 35

‰

30 25 20 15 10 5 2019

2016

2013

2010

2007

2004

2001

1998

1995

1992

1989

1986

1983

1980

1976

1973

1970

1967

1964

1961

1958

1955

1952

1949

0

Year Fig. 3.3 Birth rate and death rates in China. Data source China’s statistical yearbook, published by the National Bureau of Statistics

intention to give birth. China’s total fertility rate remained at a high level in the early years after the founding of the People’s Republic, and it reached its historical peak (close to 8) in the mid-1960s. After that period, however, it plummeted and continued to decline to a quite low level. In addition to the effects of natural disasters, the historical characteristics and changes in China’s total fertility rate suggest that it has been significantly influenced by nationwide fertility policy. Before the 1970s, China’s total fertility rate was mostly stable at a high level of around 6. The 3-year famine in the 1960s caused the total fertility rate to plummet, but it quickly returned to a high level after that period. Since the 1970s, the strictly implemented family planning policy has led to the vigorously promoted fertility concept of giving birth later and less often. According to the policy, one couple was recommended to have only one child, which resulted in a sharp decline in China’s national total fertility rate. The long-standing 1-child policy gradually forced China’s fertility to fall, and it finally stabilised at a low level. Although great efforts have been made to adjust the fertility policy to fit the current population situation, such as allowing and even advocating for second and third births, people’s attitude towards fertility is far different from that in the 1950s and 1960s. As a result, the total fertility rate in China has remained stable and low in recent years. National statistics have also shown that there are significant spatial differences across regions in China in the fertility rate. It is generally believed that the fertility rate in the eastern region is significantly lower than that in the central and western regions,

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with the western region being at a relatively high level of fertility for a long time.1 The fertility rate in East China has shown an upward trend since 2000, while rates in West and Central China have tended to remain at a much higher level with little change. Along with steady and rapid socioeconomic development, gaps between the rich and the poor across different regions seem to be narrowing, and the fertility difference between eastern and central-western regions has gradually reduced. The spatial and geographical gaps in fertility rate may originate from the great regional differences in socioeconomic development, cultural traditions, attitudes towards childbearing, and modes of production and living. Since the reform and opening up in 1978, the eastern region has been taking the lead in China’s development, driving the progress of the national economy but exacerbating regional gaps at the same time. As a result, labour, capital and other necessary resources have been forced to transfer rapidly to more economically developed regions along the eastern coast. The imbalance of income has had a significant influence on healthcare conditions and fertility attitudes, which has definitely affected the birth rate of the population across different regions. Based on the spatial division of China’s eastern, central and western regions, Su et al. (2022) focused on the spatial heterogeneity of the birth rate in China from 1979 to 2019 to reveal the geographical evolution of China’s birth rate and the reasons for regional differences. The results showed that the spatial stratification heterogeneity appeared quite significant in terms of China’s birth rate. The differences in birth rates among regions had an expanding trend and then gradually converged. More specifically, the difference between the eastern and western regions and between the central and western regions seems much more significant than that between the eastern and central regions from 1986 to 2003. A possible reason may be that areas with more advanced economic development in the eastern and central regions paid more attention to the quality of the next generation, resulting in a lower birth rate than the underdeveloped western regions. From 1981 to 2015, the average life expectancy in China increased from 67.7 to 76.3 years. In detail, the average life expectancy for males increased by 7.4 years, and the average life expectancy for females increased by 10.2 years. This significant increase in life expectancy provides convincing evidence that the service quality of medical care, sanitary conditions, and social services as well as residents’ living standards in China have greatly improved in recent years. At the same time, however, this changing trend of population structure has also made population aging a social issue increasingly worthy of attention (Fig. 3.4). In 2019, the United Nations Department of Economic and Social Affairs released the 2019 Revision of World Population Prospects, providing an overall analysis of the future changing trends and prospects of the global population. It reported that China maintained its status as the most populous country from 1990 to 2019. In 1

According to the classification in the existing literature, the eastern region was the first region to implement the coastal opening policy, and it has a relatively higher level of economic development. It includes Beijing, Tianjin, Hebei, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan provinces. The central region includes Heilongjiang, Jilin, Shanxi, Anhui, Jiangxi, Henan, Hubei and Hunan provinces. The western region includes Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet and other provinces.

3.1 Population Growth

63 Total

Male

Female

85 80

Years old

75 70 65 60 55 1981

1990

2000

2005

2010

2015

Year

Fig. 3.4 Average life expectancy in China. Data source China’s statistical yearbook, published by the National Bureau of Statistics

1990, its total size of population was about 1,177 million, and it increased to 1,434 million at the end of 2019. India’s population also increased rapidly from 873 to 1,366 million in the same period, far ahead of the third-placed nation. In the future, due to the declining willingness of China’s population to give birth and the resulting slowing trend in population growth, India’s population is expected to surpass China’s in 2050, making it the most populous country in the world. In addition, the United Nations predicts that the total population in China will drop to 1,065 million in 2100, lower than the total population in the 1990s (Table 3.1). As predicted by previous studies, the total population of China may reduce after peaking around 2030. According to the forecast in the National Population Development Plan (2016–2030), the total fertility rate will gradually increase and stabilise at a moderate level. The population size of China may reach about 1.42 billion in 2020, then 1.45 billion in 2030, but it will later drop to 1.38 billion in 2050. The urbanisation rate had already exceeded 60% in 2020, and it is expected to reach 70% in 2030, when the urban population may grow to 1.015 billion. Wang Guangzhou, a scholar working at the Institute of Population and Labour Economics of the Chinese Academy of Social Sciences, has found that China’s total population is bound to keep dropping after reaching a peak of about 1.41 billion, but that the extent and rate of decline is uncertain. Table 3.1 Forecast of China’s population Year

1990

2015

2019

2030

2040

2050

2100

Total population (millions)

1,177

1,397

1,434

1,439

1,441

1,402

1,065

Data source United Nations Department of Economic and Social Affairs (2019)

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3 Population Growth and Urban–Rural Structure

Spatially, China’s population is distributed quite unevenly across different regions. Based on province-level statistical data released by the National Bureau of Statistics over the years, the spatial distributions of the population in 1990, 2000, 2010 and 2020 are in Fig. 3.5. Since the reform and opening up, the spatial centre of China’s population has gravitated to the southeastern coastal and the southern inland provinces. The populations of some provincial administrative units such as Tibet, Xinjiang and Gansu are relatively small, and the population spatial structure has been relatively stable in the 21st century, while provincial units including Guangdong, Shandong, Henan and Jiangsu are the most populous areas. According to the Seventh Census data, the total population of Guangdong Province was 126.24 million, and that of Shandong Province was 101.65 million at the end of 2020, while the total population in the Tibet Autonomous Region is only 3.66 million, the smallest provincial population size across China, followed by Qinghai Province with 5.93 million. It is generally believed that the spatial imbalance of population originates from multidimensional factors such as regional gaps in geographical resource conditions, employment opportunities, and economic development status. The push and pull forces from different areas will cause large numbers of people to migrate across different provinces and cities, resulting in an uneven spatial distribution of the total population. Based on the province-level statistical data of population and GDP from 1978 to 2018, Liang et al. (2022) explored the evolution trend of population and economic gravity centres in four regions in China. On the whole, the population gravity centres moved to the southwest of the eastern region, the northeast of the central region, the

Fig. 3.5 Total population of China’s provincial units in 1990, 2000, 2010 and 2020. Data source China’s statistical yearbook, published by the National Bureau of Statistics

3.2 Dual Urban–Rural Population Structure

65

northwest of the western region and the southwest of the northeastern region. The population and economic centres in the east have moved to the southwest, mainly because of the significant advantages for development in the southeast coast. For a long time, the southeast coast has been the core trade area for China. Supported by the national special economic zone policy, the vigorous development of small and medium-sized enterprises has promoted economic growth and population agglomeration. Liang et al. pointed out that the overall impact of socioeconomic factors on population density might be quite small, and the main fact is that the natural environment and geographical location factors have already set the basic pattern of population distribution. Huge gaps in population size among provinces are likely to result in significant regional differences in residents’ social lifestyles and travel behaviour, as well as the related transport demand. For areas with more concentrated population and higher density, which may cause fierce competition for public resources, ensuring adequate supply and improving quality should be the focus of optimising public services, especially transport services. For areas with sparse distribution of population and less competition for public resources, improving the efficiency of resource utilisation might be a more effective way of building a high-quality transport system.

3.2 Dual Urban–Rural Population Structure 3.2.1 Formation of the Urban–Rural Dual System The dual structure of urban and rural areas in China has gradually evolved and accompanied the uneven development of the social economy across regions. In the process of China’s transformation from a traditional society to a modern society, the relationship between urban and rural areas has also experienced great changes, from an integrated interaction to a more separate relationship. 1) Before the formation of the dualistic urban–rural structure: The integration of urban and rural areas in traditional society In the traditional era, the countryside was the foundation for the formation of Chinese culture, while the city was the most concentrated and prominent embodiment of this culture. The two are interdependent and form an integral whole. Thus, to a certain extent the foundation of urban culture may have originated from the countryside (Zhao, 2018). Under the influence of a close correlation in culture, urban and rural areas in China’s traditional society have had close interactions in both population (labour) flow and product (capital) flow. In terms of population, the labour flow between rural areas and urban areas in traditional Chinese society has usually been both two-way and significant. However, Chinese idioms such as returning to one’s home town after falling to one’s roots, returning to one’s home town after prosperity and returning to one’s home town

66

3 Population Growth and Urban–Rural Structure

when getting old embody the traditional ideal of returning to one’s home town after gaining fame and wealth in an urban living space. For a long time, it has been a significant embodiment of traditional Chinese social values. In terms of products, due to the relatively scattered productivity in traditional society, traditional Chinese villages are often communities that combine industry and agriculture (Fei, 1993). The prosperity of the local industry has given the village the economic resources that can be exchanged with the city. Generally, city and town maintain many stable exchange links based on an exploitative relationship. To conclude, the urban–rural relationship in traditional Chinese society is more embodied as interaction and mutual growth. 2) The formal formation of the dualistic urban–rural structure Since the founding of the People’s Republic of China in 1949, China’s social structure has undergone an earth-shaking reconstruction, and the relationship between urban and rural areas has experienced great changes. In 1955, the State Council promulgated the Directives of The State Council on the Establishment of a Regular Household Registration System and the Provisions of The State Council on the Standards for the Division of Urban and Rural Areas. In these documents, for the first time the agricultural population and the non-agricultural population were mentioned as two separate groups for demographic indicators. Since then, migration from rural areas to cities has been strictly controlled. China’s household registration system has strict criteria of distinction and makes urban and rural attributes an important indicator for social population. Relationships between the rural and the urban tend to be opposite. In later literature, the birth of the household registration system was regarded as a typical symbol of China’s development into a dualistic society. The formation of the dualistic urban–rural structure in China has three institutional backgrounds. Firstly, the strict household registration system divides the population into the agricultural group and the non-agricultural group. In addition, it stipulates that the agricultural population cannot freely move to urban areas and become non-agricultural. The original intention of the household registration system was to ensure the effective allocation of scarce materials, linking the allocation of livelihood resources and opportunities with the decisive regional attributes to form a hereditary identity (Zhao, 2018). Secondly, the differentiated public policies and services allocation between urban areas and rural areas in China has brought about inequality in social resources and development opportunities. As a result, the industrial development of cities and towns often takes precedence over that of villages, and urban residents often have access to much richer social welfare than rural residents, such as medical care, old-age care, and educational opportunities. Thirdly, there are also differences in the land property rights system between urban and rural societies in China. These differences can be found in land use rights, land transaction modes and prices, resulting in a lack of equal land rights between rural and rural residents and exacerbating the dual distinction between urban and rural society. 3) The evolution of the dualistic urban–rural structure Since China’s reform and opening up in 1978, the dualistic urban–rural structure has undergone a remarkable transformation. The relationship between urban and rural

3.2 Dual Urban–Rural Population Structure

67

areas has changed from strict division between urban and rural societies, separation between industry and agriculture, and a rigid urban–rural system to increasingly frequent urban–rural communications and fast development in rural industry (Zhao, 2018). During this transition period, China’s rural areas have received some development opportunities. However, the benefits seemed quite limited. The concept of economic-centred development promotes the planned flow of resource elements from rural to urban areas, causing the gap between urban and rural development to remain significant. Some scholars have named the urban–rural structure after China’s reform and opening-up a new dual structure, emphasising that it was different from the previous administration-oriented dual structure based on the household registration system. Under the market economic environment, Chinese society has gradually transitioned from the production and consumption of daily necessities to those of durable goods. Against this background, the market resources in cities were less likely to flow to the countryside, which caused a rupture between urban and rural society and formed a new market-oriented dualistic structure (Sun, 2003). The dualistic urban–rural structure caused significant inequality between urban residents and rural residents, in terms of income level, social welfare and opportunities for employment, which has become one of the key social issues in China.

3.2.2 Changes in the Urban–Rural Structure 1) Declines in the proportion of the rural population According to China’s Statistical Yearbook published by the National Bureau of Statistics, the urbanisation rate in China has gone through a steady rise in recent decades. In 2011, the national urbanisation rate exceeded 50% for the first time. In 2016 Premier Li put forward a great goal in the government work report: “by the year 2020, the population of permanent residents in the urbanisation rate of 60%.” However, this goal became reality even earlier than expected. By the end of 2019, of the 1.4 billion people in China, 848.43 million or 60.60% of total population were reported to live in urban areas. The rapid and continuous rise of the urbanisation rate is believed to have a close correlation with China’s economic development and institutional environment. Since 1978, the third Plenary Session of the 11th Central Committee of the CPC made a major decision on the implementation of reform and opening up. Since then, the reform focus of China’s development has shifted to urban areas, and the process of urbanisation has accelerated. In 1992, Deng Xiaoping made a great speech in southern cities and lauded the benefits of China’s reform and opening up. This great speech has played a key role in China’s economic reform and social progress. From the 1990s to the early 21st century, a large number of surplus labourers in rural areas moved to urban areas with highly centralised industries, becoming rural migrant workers. These migrant workers provided many labour resources and made great

68

3 Population Growth and Urban–Rural Structure Total population

Urbanization ratio

160000

70

140000

60 50

100000

40

%

10,000 persons

120000

80000 30

60000

20

40000

10

20000 0 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018

0

Year Fig. 3.6 Total population and urbanisation rate in China. Data source China’s statistical yearbook, published by the National Bureau of Statistics

contributions to the economic development in urban areas, as well as accelerating increase of the urbanisation rate in China. Data from China’s Statistical Yearbooks (Figs. 3.6 and 3.7) show that in the 1990s, both the size of urban population and the total urbanisation rate in China underwent rapid growth. By the end of 2011, people working and living in urban areas accounted for more than 50% of China’s total population, which is 33.35 percentage points higher than that at the end of 1978. In recent years, with the city scale expanding unceasingly in China, the economic development in large, medium and small cities all achieved remarkable progress. From an international perspective, the gaps in urbanisation between China and many developed countries in the world have been greatly narrowed. 2) Income gaps between urban and rural people According to the Statistical Yearbooks of China, over the years, disposable income per capita of both urban and rural residents have maintained a steady rise since 1990. There is no doubt that the rapid development of the social economy has comprehensively improved people’s quality of life in both urban and rural areas in the past few decades. However, there is still a significant gap between the economic levels of urban residents and rural residents. According to the National Bureau of Statistics, disposable income of residents refers to the income of residents for final expenditure and savings, comprised of cash income and in-kind income. Based on the different sources of income, disposable income is divided into four categories: income from wage and salaries, net business income, net income from properties and net income from transfers.

3.2 Dual Urban–Rural Population Structure

69

Urban population

Rural population

100000 90000

10,000 persons

80000 70000 60000 50000 40000 30000 20000 10000 2018

2016

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

0

Year Fig. 3.7 Urban and rural population in China. Data source China’s statistical yearbook, published by the National Bureau of Statistics

During the 1990s, the urban–rural gap in disposable income per capita was not that significant, as it was only 1.52 in 1990. However, with accelerated urbanisation all over the country, urban areas have achieved rapid economic growth, giving them a large advantage in development opportunities and richer labour resources. Disposable income per capita in urban areas grew steadily and rapidly, while that in rural areas grew quite slowly, resulting in the gap in income levels between urban and rural areas widening greatly. In 2010, the gap between urban and rural income levels was 2.3, with the disposable income per capita of urban residents being 19,109 CNY, while that of rural residents was only 8,120 CNY. By 2013, the gap had reached 2.81. From 2013 to 2018, it declined slightly with the implementation of China’s Rural Revitalisation Strategy for promoting poverty alleviation in rural areas. However, the urban–rural gap in income level still remained at a high level of around 2.70, indicating that the gap between urban and rural residents’ livelihood conditions remained significant. In addition to the huge gap in disposable income, residents’ expenditure on transport and communications per capita has also shown a significant urban–rural gap. The consumption expenditure of residents on transport and communications refers to expenditure on transport and communication and related services, maintenance and repairs, and vehicle insurance. According to the national statistical yearbooks, the expenditure on transport and communications per capita of urban and rural residents increased greatly from 2013 to 2018 with China’s rapid development of the social economy. In 2018, urban residents spent 3,474 CNY/year/person on transport and communications, while rural residents spent 1,690 CNY/year/person on transport and communications on average (Fig. 3.8). Although the gap between urban and rural residents has been gradually shrinking in recent years, the ratio was still as high as 2.06 in 2018. To promote the efficient use of transport service facilities in urban

70

3 Population Growth and Urban–Rural Structure Disposable income per capita (urban)

Disposable income per capita (rural)

Urban-rural gap 45000

3

40000

2.5

35000 CNY

30000

2

25000

1.5

20000

1

15000 10000

0.5

5000 0

0 1990 2000 2010 2011 2012 2013 2014 2015 2016 2017 2018 Year

Fig. 3.8 Disposable income per capita and the urban–rural gap in China. Data source China’s statistical yearbook, published by the National Bureau of Statistics

and rural areas, the charge standards for transport service in urban areas and rural areas should be different to meet the necessary travel demands of vulnerable groups such as low-income residents. Generally, the significant and persistent urban–rural income gap seems to be closely related to the unique characteristics of China’s basic economic system which represents a dualistic structure. On the one hand, the urban economy and rural economic sectors coexist, resulting the formation of an urban–rural dualistic structure (Wang, 2019a, 2019b). There are certainly huge gaps in the economic resources owned by the two groups of economic sectors and their ability to transform economic resources into available income. For example, urban residents are more likely to have access to various job opportunities than rural residents. Economic activities and employment in rural areas are often restricted by resource endowments, market scale and supporting facilities, forcing rural labourers to flow into urban areas. As a result, income gaps between the urban and the rural have widened. On the other hand, the state-owned economy and non-state-owned economy coexist in the urban sector, and their differentiated wage determination mechanism makes the income return per capital differ significantly from each other. In the economic system of China, the non-state-owned economy is biased towards the market principle; thus, the rate of return per capital is often higher than that in the state-owned economy. Since the non-state-owned economy mostly concentrates on urban areas to make contributions to accelerating the development of cities, the high rate of return on social capital may be a reason for the huge urban–rural income gap. During the 19th National Congress of the CPC held on October 18, 2017, President Xi explicitly proposed to implement the strategy of rejuvenating the country for the first time. The Fourth Plenary Session of the 19th Central Committee of the CPC

3.2 Dual Urban–Rural Population Structure

71

passed the Decision on Several Major Issues Concerning Upholding and Improving the Socialist System with Chinese Characteristics and Promoting the Modernisation of the National Governance System and Governance Ability. This important document further emphasised the goal of carrying out the rural revitalisation strategy, improving the systems and policies that give priority to the development of agriculture and rural areas as well as ensuring national food security, and improving the system and mechanism of urban–rural integration At present, the principal contradiction in Chinese society is the mismatch between the people’s ever-growing needs for a better life and unbalanced as well as inadequate development across regions. The unbalanced development is mainly reflected in the gap between the rich and the poor between urban and rural areas. Although China’s economic development has been greatly improved in recent years, the unceasingly significant urban–rural gap indicates that there is still a long way to go in implementing rural revitalisation to achieve integrated development. It is necessary to strengthen the two-way flow of urban and rural resource elements further, promote the equalisation of public facilities and services, and reduce the gap between the urban and the rural (Fig. 3.9). The income and expenditure data for residents from 2013 to 2018 are shown in Figs. 3.10, 3.11, 3.12, 3.13, 3.14, 3.15 and 3.16. They show that the shares of wage income and disposable income for urban residents stabilised at around 60%, with a slightly downward trend, while the shares of wage income and disposable income among rural residents remained at about 40%. Although those in rural areas showed a slightly upward trend, a significant gap between urban areas and rural areas remains. According to the National Bureau of Statistics, wage income (also called income from wages and salaries) refers to remuneration and benefits of all kinds of employed persons, including those employed by other units or individuals, freelance workers, Expenditure on transport and communications per capita (urban) Expenditure on transport and communications per capita (rural)

CNY

Urban-rural gap 3

4000 3500 3000 2500 2000 1500 1000 500 0

2.5 2 1.5 1 0.5 0 2013

2014

2015

2016

2017

2018

Year

Fig. 3.9 Expenditure on transport and communications per capita and urban–rural gap. Data source China’s statistical yearbook, published by the National Bureau of Statistics

72

3 Population Growth and Urban–Rural Structure

Disposable income per capita (urban)

Proportion of wage income (urban)

Proportion of wage income (urban)

Proportion of wage income (rural)

45000

70

40000

60

35000 50

25000

40

20000

30

%

CNY

30000

15000

20

10000 10

5000 0

0 2013

2014

2015

2016

2017

2018

Year Fig. 3.10 Disposable income per capita and its proportion in China. Data source China’s statistical yearbooks, published by the National Bureau of Statistics Eastern region

Central region

Western region

Northeast region

50000 45000 40000 35000

CNY

30000 25000 20000 15000 10000 5000 0 2013

2014

2015

2016

2017

2018

Year Fig. 3.11 Disposable income per capita of urban residents in different regions of China. Data source China’s statistical yearbooks, published by the National Bureau of Statistics

part-time workers, and sporadic workers. Compared with urban residents, rural residents have fewer employment opportunities for permanent jobs. Many rural residents lack the education and professional skills required for employment, making it more difficult to find suitable jobs. The family income of rural residents tends to rely

3.2 Dual Urban–Rural Population Structure Eastern region

73

Central region

Western region

Northeast region

20000 18000 16000

CNY

14000 12000 10000 8000 6000 4000 2000 0 2013

2014

2015

2016

2017

2018

Year Fig. 3.12 Disposable income per capita of rural residents in different regions of China. Data source China’s statistical yearbooks, published by the National Bureau of Statistics Wage income

Net business income

Net income from properties and transfer

30000 25000

CNY

20000 15000 10000 5000 0 1990 1995 2000 2011 2012 2013 2014 2015 2016 2017 2018 2019

Year Fig. 3.13 Disposable income per capita of urban residents in China. Data source China’s Household Statistical Yearbooks published by National Bureau of Statistics2

2 Since 2013, the National Bureau of Statistics has carried out a survey of household income and expenditure and living conditions for urban–rural integration. The survey scope, survey methods, and indicator calibres were all different from those in the urban and rural household surveys before 2013. Here, the income level of rural residents from operating activities reflects net business income from households before 2013 and net business income after 2013. The same applies to the next figure.

74

3 Population Growth and Urban–Rural Structure Wage income

Net business income

Net income from properties and transfer

7000 6000

CNY

5000 4000 3000 2000 1000 0 1990 1995 2000 2011 2012 2013 2014 2015 2016 2017 2018 2019

Year Fig. 3.14 Disposable income per capita of rural residents in China. Data source China’s Rural Statistical Yearbooks published by National Bureau of Statistics Wage income

Net business income

Net income from properties and transfer

90 80 70 60

%

50 40 30 20 10 0 1990

1995

2000

2011

2012

2013

2014

2015

2016

2017

2018

2019

Year Fig. 3.15 Composition of disposable income per capita of urban residents in China. Data source China’s Household Statistical Yearbooks published by National Bureau of Statistics

3.2 Dual Urban–Rural Population Structure Wage income

75

Net business income

Net income from properties and transfer

60.00 50.00

%

40.00 30.00 20.00 10.00 0.00 1990

1995

2000

2011

2012

2013

2014

2015

2016

2017

2018

2019

Year Fig. 3.16 Composition of disposable income per capita of rural residents in China. Data source China’s Rural Statistical Yearbooks published by National Bureau of Statistics

more on individual or family-based business activities, such as farming, primary processing and sales of agricultural products, operating restaurants, running shops and providing beauty salon services. Therefore, net business income usually occupies a larger proportion of the disposable income in rural areas, making the income source of rural residents less stable than that of urban residents (Fig. 3.10). In recent years, the disposable income per capita of urban and rural residents in China has grown steadily. However, the income gap between the four major economic regions is still significant. According to the National Bureau of Statistics, China has four major economic regions: (a) the eastern region, including Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan; (b) the central region, including Shanxi, Anhui, Jiangxi, Henan, Hubei and Hunan, (c) the western region, including Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang, and (d) the northeast region, including Liaoning, Jilin and Heilongjiang. These four regions have performed quite differently in terms of the income levels of urban residents and rural residents. For urban residents, the income per capita of the central, western and northeastern regions is relatively close, at around 31,000 CNY, while that of the eastern region is significantly higher and shows a relatively stable gap with other regions (Fig. 3.11). For rural residents, the income gap between different regions appears much more significant. The income level of rural residents in the eastern region is significantly higher than that of other regions, while that in western region is slightly lower. Meanwhile, the urban–rural income gap has been gradually narrowing in all regions of China. In 2013, the disposable income per capita of urban residents in the eastern region was about 2.63 times than that of rural residents. By 2018, it had reduced to

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2.54 times. The narrowing trend of the urban–rural gap in the western region appears the most obvious, with the gap reducing from 3.01 times in 2013 to 2.82 times in 2018 (Fig. 3.12). These changes in urban and rural residents’ income levels and the relative changes in their lifestyles make it necessary to pay more attention to their diversified transport demands. On the one hand, the gap between urban areas and rural areas is still significant, so the travel behaviour of rural residents should receive more consideration to help to promote urban–rural equality. On the other hand, more diversified and advanced requirements for living will certainly come with the rapid development of society and economy, especially in rural areas. For the future, new expectations for life make it important to improve transport services and to optimise China’s comprehensive transport systems based on the changes in population growth and its structure. 3) Changes in structure of income source According to the data reported by National Bureau of Statistics, disposable income of residents refers to the income of residents for final expenditure and savings. It includes income from wages and salaries, net business income, net income from properties and net income from transfer. Detailed definitions of each type of income are as follows. (1) Income from wages and salaries refers to remuneration and benefits of all kinds of employed persons, including those employed by other units or individuals, freelance workers, part-time workers, and sporadic workers. (2) Net business income refers to net income earned by households and their members engaged in production and business activities. It refers to the net income of operating revenue minus operating costs, depreciation of productive fixed assets, and production tax, which can be represented by the formula: Net business income = operating revenue − operating costs − depreciation of productive fixed assets − production tax. (3) Net income from properties refers to the net income received as returns by households or members through lending of their financial assets or non-financial assets such as housing to other institutions, households or individuals, minus relevant costs. Net income from properties includes net income from interest, bonus income, net income from saving insurance, net income from transferring management right of contract land, income from lending of housing, income from lending other assets, and net converted rents of self-owned housing. Net income from properties does not include premiums for transferring ownership of assets. (4) Net income from transfer refers to the income from transfer minus the expenditure from transfer. The formula is: Net income from transfer = income from transfer − expenditure from transfer. Income from transfer refers to the regular transfer received from governments, institutions and social organisations to households and between households. It mainly covers old-age and retirement pensions, disaster relief funds, regular donations and compensation,

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reimbursement of medical fees, supporting income between households, income from non-resident members of households, etc. Income from transfer does not include gifts in kinds between households. In addition, expenditure from transfer refers to regular or obligatory transfer paid to government, institutions, households or individuals. The expenditure mainly includes tax payments, expenditure on all kinds of social security, supporting expenditure, regular donations, compensation payments and other regular transfer expenditure. In the context of the dualistic urban–rural structure in China, there are certain differences in the characteristics of disposable income sources between urban and rural residents. Taking the statistical data of 2019 as an example, the disposable income per capita of urban residents was 42,358.8 CNY/year. In detail, wage income accounted for as high as 60.35% followed by property and transfer income, accounting for 28.22%, while operating income accounted for only 11.43%. Thus, wage income was the main source of income for urban residents. Some studies analysed the multiple income sources of urban and rural population and pointed out that the share and concentration rate of wage income appeared higher than any other types of income sources, as for the urban population (Luo et al., 2021). For rural residents, the disposable income per capita in rural areas was 16,020.7 CNY/year. In detail, wage income was 6,583.5 CNY/year, net operating income was 5,762.2 CNY/year, and the sum of property net income and transfer net income was 3,675.1 CNY/year, accounting for 22.93%. Although wage income has gradually increased to a more significant proportion and the absolute average value has exceeded that of others in recent years, operating income is still the main income source for a large number of rural residents. In terms of changing trends, the composition of income sources for urban residents remains relatively stable, with wage income always being in the key position, while the composition of rural residents’ income source has undergone significant changes in recent years. Some studies have found that the income sources of Chinese farmers are becoming more diversified, from a high dependence on family business income in the past to the coordination of various sources. The contribution of wages to farmers’ total income has already exceeded that of family businesses. In the future, wage income and operating income will be the main living sources for them, together driving the growth of farmers’ total income (Wang, 2019a, 2019b). Evidence can be found from statistical data in previous years. Since 1990, the share of wage income in rural residents’ disposable income has gradually increased, along with a gradual decline in the share of operating income. Before 2014, net operating income had been the main source for rural residents, accounting for more than 40%. Since then, wage income has surpassed net operating income and became the dominant income source. At the end of 2019, wage income accounted for up to 41.09% of rural residents’ total income, with net operating income accounting for 35.97%. The sum of net property income and net transfer income accounted for 22.32%, similar to that for urban residents. According to statistical data and previous studies, the urban–rural gap in labour income (including wage income and operating income) contributes more to the

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income gap between urban and rural residents than transfer income and property income (He, 2020). This gap in income sources, especially in wage income and operating income, seems closely related to the different labour markets and production modes in urban and rural areas. In general, urban and rural labourers in China engage in significantly different types of work. Urban residents are mainly engaged in industrial production and service industries, with the labour remuneration mainly paid through regular and relatively fixed wages. Many rural residents are engaged in agricultural production, where the returns come from operating income. Some researchers have pointed out that farmers’ dependence on agriculture to obtain income during the past several decades has made operating income the dominant source of income (Fang & Wei, 2021). Despite the significant urban–rural gap, the optimisation of farmers’ income structure is worthy of recognition along with the accelerated development of underdeveloped areas and the implementation of China’s Rural Revitalisation Strategy. In recent years, most rural residents have had more employment opportunities and higher wage incomes. The main income sources of many farmers have gradually changed to fixed employment or part-time work, and their dependence on operating income with its uncertainty has reduced as a result. Improving farmers’ income sources might be an effective way to narrow the urban–rural gap in income and to improve the economic level of farmers (Wang, 2019a, 2019b). In future, rural development needs to be further integrated with urban development, and surplus agricultural labourers should be encouraged to transfer to non-agricultural production to increase their total income and life quality. Figures 3.13, 3.14, 3.15 and 3.16 illustrate these statistics. In terms of net business income in rural areas, the proportion of primary industry continued to decline, and that of tertiary industries rose steadily. In 1990, rural residents’ business income from primary, secondary and tertiary industries accounted for 87.93%, 4.11% and 7.94%, respectively. That is, the production and business activities of primary industry were the main income source for rural residents. By 2000, the share of the primary industry had reduced, while that of the secondary and tertiary industries had increased significantly, being respectively 76.42%, 6.96% and 16.62%. From 2000 to 2018, the share of the net business income from primary industry in rural areas continued to decline, while that from secondary industry was basically stable and that from tertiary industry continued to rise. By end of 2018, the share of net business income of the production and business activities of tertiary industry has risen to nearly 30% (Fig. 3.17). Judging from the data, tertiary industry is an important source of income for rural residents. At present, the production and business activities of primary industry in rural areas of China mainly involve agricultural activities, forestry activities, animal husbandry activities and fishery activities, among which agricultural activities occupy a dominant position. In 2018, 74.48% of the net business income of rural residents in China came from agricultural activities, followed by 16.32% from animal husbandry. Income from forestry and fishery were less than 6% of the total (Fig. 3.18). The agricultural activities in China’s rural areas are quite diverse, including the production of all kinds of agricultural products and raw materials, such as the planting and processing of rice, wheat, cotton, soybeans, corn, and other crops. Traditional ways

3.2 Dual Urban–Rural Population Structure Primary industry

79

Secondary industry

Tertiary industry

100.00 90.00 80.00 70.00

%

60.00 50.00 40.00 30.00 20.00 10.00 0.00 1990

1995

2000

2011

2012

2013

2014

2015

2016

2017

2018

Year Fig. 3.17 Source structure of net business income of rural residents in China. Data source China’s Rural Statistical Yearbooks published by National Bureau of Statistics 3.35% 16.32% Agricultural activities Forestry activities

5.44%

Animal husbandry activities Fishery activities 74.48%

Fig. 3.18 Source structure of net business income from primary industry of rural residents. Data source China’s Rural Statistical Yearbooks published by National Bureau of Statistics

of farming are now being increasingly supplemented by modern farming equipment and new technology in China (shown in Figs. 3.19, 3.20 and 3.21), and this has made a great contribution to improving the efficiency of production. With the development of the social economy, modern agricultural production will certainly grow rapidly, and it may become an important path for promoting rural revitalisation in China. Compared with primary industry, tertiary industry has a much shorter development history in rural areas of China. Meanwhile, the development of tertiary industry in rural areas has grown rapidly and made remarkable achievements in recent years. In

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Fig. 3.19 Cotton production machinery at 2022 Xinjiang Agricultural Machinery Expo, China. Source http://www.news.cn/photo/2022-07/03/c_1128800341_6.htm, visited on July 10, 2022

Fig. 3.20 Modern machinery applied in Eco family farm, Jilin Province, China. Source http://www. news.cn/photo/2022-04/26/c_1128597740.htm, visited on July 10, 2022

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Fig. 3.21 Drones applied for wheat farming in Jiyuan, Henan Province, China. Source http://www. news.cn/photo/2022-03/30/c_1128517790_2.htm, visited on July 10, 2022

current China, the production and business activities of tertiary industries engaged in by rural residents serve not only the basic living needs of local residents (such as catering, hairdressing, shopping, etc.), but also the tourism needs of non-local people. With the fast-changing concepts of consumption and tourism combined with social transformation, those who have been living in big cities for a long time are often tired of the hustle and bustle of cities, and they are more inclined to go to rural areas to enjoy the idyllic scenery. Experiencing the countryside and returning to the idyllic have become more and more popular during holidays. Against the background of rapidly developing rural tourism, a large number of rural residents living close to tourist resources are participating in third industry business activities. For example, some rural residents sell a service called Happy Family Farm (“nongjiale” in Chinese) to give tourists from cities an opportunity of experiencing farmers’ life. Some rural residents have renovated their own houses and sell accommodation services called homestays (Figs. 3.22 and 3.23). In response to the consumption and entertainment demand of tourists, many residents sell agricultural products with local characteristics to tourists from other regions and cities (Fig. 3.24). The forms of commercial activities of tertiary industry in rural areas are becoming increasingly diverse. Accordingly, the lifestyles and travel behaviours of rural residents are also becoming more diversified. 4) New trends for rural migrants Since the formation of China’s dualistic urban–rural structure, urban and rural areas (also called cities and villages) have shown huge gaps in almost every aspect of

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Fig. 3.22 Handicraft shop run by local villagers in Penglai Fairy Cave Scenic Area, Anhui Province, China. Source Photo taken by the author, June 10, 2020

social and economic development. The income gap between urban and rural residents has led to significantly different lifestyles and cultures. In this context, citizens and villagers tend to be separated into two different social classes. The abundant employment opportunities, convenient public services and good living environment in urban areas have been very attractive to farmers. As a result, large numbers of rural labourers have transferred from agricultural activities to non-agricultural industries and migrated from rural villages to urban areas to gain more social resources. The appearance of early migrant workers in China has a complex historical background. In 1979, Shenzhen funded the Shekou Industrial Zone, which was a prelude to the second industrialisation and urbanisation after the founding of the New China. The old barren slopes were turned into industrial parks, and local farmers became workers, putting shoes into factories. The development of industrial parks not only drove local farmers to engage in non-agricultural production, but also promoted the transfer of rural labour between regions through cross-regional recruitment. The Shekou Model places migrant workers on the stage of history. Originally, migrant workers is mentioned to describe the group that is engaged in the work in factories with farmers’ registered permanent residence, whose occupation is workers and whose identity is farmers (Sheng, 2018). With a combination of a traditional identity and a new occupation, these workers had become a new group with the characteristics of both industry and agriculture.

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Fig. 3.23 Catering and accommodation services provided by local villagers in Penglai Fairy Cave Scenic Area, Anhui Province, China. Source Photo taken by the author, June 10, 2020

The number of China’s migrant workers experienced rapid growth in the 1980s and the late 20th century. Under the national wave of industrialisation and urbanisation, the rapid development of the industrial economy in urban areas drove the transfer of the surplus labour force from rural areas, resulting in a large-scale migrant worker wave in the 1980s. Cross-regional mobility appeared to be the key characteristic of migrant workers. In 1987, a report to the 13th National Congress of the CPC pointed out that enterprises in rural areas rose suddenly, with nearly 80 million farmers transferring to non-agricultural industries. In 1989, the mobile army composed of migrant workers reached up to 30 million and showed a significant trend of migration from inland to coastal, from north to south, and from rural to urban (Sheng, 2018). During this period, migrant workers became an important and special group in China’s social development. In the rapid development of Chinese society, rural migrant workers represent two special characteristics. Firstly, in the social environment of China, there are significant differences between urban and rural residents in people’s attitudes towards migrant workers. Most urban residents consider migrant workers as villagers or vagrants from the countryside from the perspective of household registration. Rural residents tend to consider migrant workers as those who enter the city for a job to earn more money. This difference weakens migrant workers’ sense of belonging in the countryside, while it is also difficult for them to establish a sense of belonging in

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Fig. 3.24 Agricultural products with local characteristics sold by local villagers in Penglai Fairy Cave Scenic Area, Anhui Province, China. Source Photo taken by the author, June 10, 2020

the urban society. Thus, they tend to become a third part in the dualistic urban–rural social structure. Secondly, although migrant workers are engaged in non-agricultural production, living in the urban space, with daily activities similar to urban residents, the actual rights and interests they receive (such as education, health care and other social welfare) are not equal to those of urban residents. As a result, a variety of livelihood needs of migrant workers cannot be satisfied, leaving them to look like outsiders in urban society. In the 21st century, with China’s urbanisation reaching a high level, the growth rate of urbanisation seems to have slowed down. Rural revitalisation has been incorporated into China’s vital development strategy and great changes have taken place in China. The flow of labourers between urban and rural areas has also undergone significant changes, particularly in the size, direction and employment situation of migrant workers. The first generation of migrant workers gradually withdrew from the labour market, followed by a new generation of migrant workers participating more in the development of urban and rural areas. Wang Chunguang, a researcher in the Chinese Academy of Social Sciences, first put forward the academic concept of the new generation of migrant workers in 2001; it mainly refers to the group of migrant workers born after 1980 or 1990. These workers grow up with the huge changes in China’s rural economic and social structure. Unlike the first generation of migrant workers, the new generation of migrant workers has experienced the stage

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of rapid urbanisation in China, and some have also participated in the flow of the first generation of migrant workers during childhood. The new generation tends to have higher expectations for the quality of life and stronger adaptability in urban society. According to the annual China Household Survey Yearbook published by the National Bureau of Statistics, the growth rate in China’s migrant workers has dropped sharply since the financial crisis of 2008. In 2008, the total number of migrant workers in China was 225.42 million, and this increased slightly to 229.78 million in 2009 and 242.23 million in 2010. In 2010, the growth rate of migrant workers reached a peak of 5.42%, but it began to decline year by year thereafter. By 2018, the growth rate was only 0.64%. This indicates that most of the surplus rural labour force has already moved into the urban labour market with the continuous increase of China’s urbanisation rate. Accordingly, the number of migrant workers now seems to be stable, and it may even experience a decrease in the future (Figs. 3.25 and 3.26). According to the distance rural labourers move, migrant workers can be divided into two categories in general: nonlocal migrant workers and local migrant workers. Nonlocal rural migrant workers are workers who move within their own provinces (but to other towns) and workers who move across provinces. Local rural migrant workers are those who move within their own towns. According to data from the China Household Survey Yearbook, rural migrant workers have shown a trend of localisation in recent years. More and more farmers choose to work nearby within the same township as their permanent residence or move to another township in the same province. Fewer and fewer migrant workers choose to move to another province from their permanent residence. In 2010, 31.86% of migrant workers in China had interprovincial mobility, and the proportion of within-province, cross-province and within-town migrant workers was not very different. By 2018, the proportion of within-province migrant workers Total number of rural migrant workers

Number of non-local rural migrant workers

Number of local rural migrant workers 35000

10,000 persons

30000 25000 20000 15000 10000 5000 0 2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Year

Fig. 3.25 Number of total, non-local, local rural migrant workers in China. Data source China’s Household Survey Yearbook 2019, published by the National Bureau of Statistics

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3 Population Growth and Urban–Rural Structure Growth rate of rural migrant workers

Growth rate of non-local rural migrant workers

Growth rate of local rural migrant workers 7.00 6.00 5.00

%

4.00 3.00 2.00 1.00 0.00 -1.00

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Year

Fig. 3.26 Growth rates of different types of rural migrant workers in China. Data source China’s Household Survey Yearbook 2019, published by the National Bureau of Statistics

had significantly decreased to 26.34%, while that of within-town migrant workers increased to 40.12%, and that of cross-province rural migrant workers also increased slightly. According to the push–pull theory of labour transfer, with the great achievements in China’s economic development, the pull force and the push force, which caused large-scale cross-regional transfer of rural labour in past decades are becoming smaller nowadays. On the one hand, the population of developed regions is becoming saturated and employment attractiveness is reducing, while city diseases may make the quality of life in big cities less attractive. On the other hand, the development of the local economy has brought increasing employment opportunities as well as more advanced local infrastructure. As a result, migrant workers tend to work close to their home towns or in the surrounding areas (Figs. 3.27 and 3.28). 5) Characteristics of employment Based on the Statistical Yearbooks of China, the number of employed workers and the proportions of workers among the total population in urban and rural areas at the end of each year are in Fig. 3.29. According to the National Bureau of Statistics, employed workers are above a certain age, have the ability to work, and engage in certain social labour to obtain labour remuneration or operating income. More specifically, they had reached the age of 16 and had engaged in more than 1 h of labour during the survey week to obtain remuneration or business profits. Those who were temporarily out of work during the investigation week due to study or holiday or for other reasons, but had workplaces were also included, as well as those temporarily out of work as a result of a temporary shutdown holiday or unit depression holiday, or for some other reason in the investigation week. Since 2000, the proportion of employed workers in urban and rural areas has been basically stable. Moreover, the

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31.45% Floating within his province

36.69%

Floating out of his province Floating within his town

31.86%

Fig. 3.27 The proportions of different types of rural migrant workers in 2010. Data source China’s Household Survey Yearbook 2019, published by the National Bureau of Statistics

33.54%

Floating within his province

40.12% Floating out of his province Floating within his town

26.34%

Fig. 3.28 The proportions of different types of rural migrant workers in 2018. Data source China’s Household Survey Yearbook 2019, published by the National Bureau of Statistics

proportion of employed workers in rural areas is higher than that in urban areas, with an urban–rural gap of more than 5 percentage points. This indicates that although rural areas are relatively disadvantaged in terms of economic resources, employment opportunities and supporting facilities, most rural residents can still earn their living income through various and flexible types of work for a better life. According to the China Rural Family Development Report 2018 (China Rural Family Research and Innovation Team, 2019), the proportions of employed people in the total number of rural working-age people in China differ somewhat across the eastern, central and western regions (Table 3.2). In 2017, the proportion of employed workers in the working-age population in rural China was 81.3%, and those in eastern,

3 Population Growth and Urban–Rural Structure Number of employed workers (urban)

Number of employed workers (rural)

Proportion of employed workers (urban)

Proportion of employed workers (rural)

60000

70

50000

60 50

40000

40

30000

%

10,000 persons

88

30

20000

20

10000

10 0 1978 1980 1985 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

0

Year

Fig. 3.29 Number of employed workers in urban, rural areas and their proportions in China. Data source China’s statistical yearbook, published by the National Bureau of Statistics Table 3.2 Proportions of the working-age population in rural areas of China in 2017 Proportion

China (%)

Eastern (%)

Central (%)

Western (%)

Working-age population/total population

65.9

67.5

65.2

65.1

Working-age population/total population (male)

64.7

66.4

63.6

64.4

Working-age population/total population (female)

67.0

68.6

66.8

65.9

Employed workers/working-age population

81.3

80.8

79.7

83.5

Employed workers/working-age population (male)

91.0

91.6

92.1

89.2

Employed workers/working-age population (female)

71.6

69.8

67.4

77.8

Agricultural workers/employed workers

54.3

45.1

54.5

62.5

Agricultural workers/employed workers (male)

47.5

38.8

47.8

55.8

Agricultural workers/employed workers (female)

62.7

53.3

63.6

70.1

Data source “China Rural Family Development Report 2018”, published by China Rural Family Research and Innovation Team (2019) of Zhejiang University

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central and western regions were 80.8%, 79.7% and 83.5%, respectively. This was significantly higher in the western region. In terms of employment type, agricultural workers accounted for 54.3% of the employed workers in the whole country, while those proportions were 45.1%, 54.5% and 62.5% in the eastern, central and western regions, respectively, indicating that the employment situation of rural workers may differ greatly in China. In addition, the proportion of female working-age people in the total female population is higher than that for males. Among the working-age population, the proportion of female workers engaged in agricultural work is significantly higher than that of males. This phenomenon may come from the huge outflow of rural labourers in China, with most of the migrant workers being male. In the past few decades, a large number of male working-age adults have migrated from rural areas to urban areas and engaged in non-agricultural work to obtain higher incomes to support their wives and children, while females often find it difficult to enter cities and find suitable jobs due to their responsibility for housework or lack of professional skills. As a result, female villagers are usually more likely to stay at their home towns in the countryside. Among them, 28.4% take care of older relatives or children and cannot engage in stable employment, or lack of the necessary education and/or professional skills. Even if some could get a job, up to 62.7% choose to engage in agricultural activities with relatively lower requirements for professional skills.

3.2.3 Spatial Characteristics of the Urban–Rural Structure 1) Regional gaps in the urbanisation process Since China’s reform and opening up over 40 years ago, the overall urbanisation rate has rapidly grown and great achievements have been made in urban–rural development. Meanwhile, the proportion of urban residents in the total population appears significantly unbalanced geographically. According to the 2018 Urban Statistical Yearbook and the statistical Bulletin of National Economic and Social Development published by prefecture-level cities, the spatial distribution pattern of China’s urbanisation rate at prefecture level is uneven (Fig. 3.30). It indicated that the urbanisation rate of China’s eastern coastal areas, inland provincial capitals and northern areas including Heilongjiang and Inner Mongolia is significantly higher than that of other regions. For example, the urbanisation rate in Shanghai, Xiamen and many cities in Guangdong Province such as Shenzhen, Foshan and Dongguan are quite high, with the highest rate being 99.74% in Shenzhen, while in western areas such as the Guoluo Tibetan autonomous prefecture in Qinghai province, Shigatse in Tibet autonomous region and Yili Kazakh autonomous prefecture, Hotan, and the Kashi area in western Xinjiang, the urbanisation rate is significantly lower at no more than 30% or 20%. In 2017, the urbanisation rate in Yili Kazakh autonomous prefecture was only 4.33%, the lowest in China. To conclude, there seem to be huge disparities in urbanisation rates among different regions and cities.

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Fig. 3.30 Urbanisation rates of prefecture-level cities in 2017.3 Data source China’s City Statistical Yearbooks published by National Bureau of Statistics

Those regional gaps in urbanisation development indicate the unbalanced allocation of social and economic resource among different regions. The eastern coastal areas have more advanced terrain conditions such as wide plains, more foreign trade ports and a more open environment for economy development. As a result, a large number of labour resources are attracted from inland areas, promoting the rapid advance of urban construction in eastern coastal areas. Meanwhile, due to their weaker resource endowment and insufficient high-quality labour for economic development, inland areas, especially rural areas in small and medium-sized cities, have become the outflow places for the labour force. The uneven distribution of natural resources and the generally one-way flow of labour resources may be a major cause of the widened regional gap in the urbanisation process. Currently, the major contradiction in China’s social development is between imbalanced, inadequate development and people’s increased expectations for better lives. To promote social modernisation, more attention should be paid to the huge differences between urban and rural areas. Targeted improvement of infrastructure, especially transport facilities, is even more necessary in areas with lower urbanisation rates. Narrowing the urban–rural gap in people’s quality of life will help to achieve the goal of common prosperity, which may be just around the corner. 3

Several cities lack data about urbanization rates, including Shannan, Naqu, the Ali area in Tibet and Beitun, and Shuanghe in Xinjiang.

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2) Spatial distribution of residents’ income According to the 2018 City Statistical Yearbook, the income gap among different regions remains quite significant. It shows that disposable income per capita in the southeast coastal areas (such as Shanghai, Zhejiang, Fujian and Guangdong) is much higher, while that in northwestern small and medium-sized cities, such as those in western Tibet and Xinjiang, is much lower (Fig. 3.31). In addition, this regional gap occurs in both the urban and the rural population. Both urban and rural areas are faced with imbalanced development across regions due to the unevenly distributed natural resources endowment. To promote sustainable development and social equality in China better, more advanced transport facilities and public transport services are needed in areas lacking in resources and therefore at a disadvantage. 3) Relationship between urbanisation and income gap Based on the statistical data on urban and rural disposable income per capita in each prefecture-level city, the income multiplier difference between urban and rural residents is calculated (Fig. 3.32). Currently, the urban–rural income gap in China seems much larger in inland and western areas, while that in eastern coastal areas is relatively smaller. By the end of 2017, the smallest urban–rural income gap appeared in Jixi, Heilongjiang, at 1.345. There, the disposable income per capita of urban residents was 22,607 CNY while that of rural residents was 16,808 CNY. Similarly, the urban–rural income gaps in Guangdong, Zhejiang and Jiangsu were all quite small, most of them between 1.5 and 1.8. For some cities in Gansu, Qinghai and Xinjiang, the urban–rural gaps in disposable income per capita have reached 3.3 and even beyond 4.0. In 2017, the disposable income per capita of urban residents in Xinjiang Kezilesukeerkezi autonomous prefecture was 26,467 CNY, while that of rural residents was only 6,524 CNY, with a huge gap of 4.057. The largest urban– rural income gap in China appeared in the Goluo Tibetan autonomous prefecture in Qinghai province, where the urban disposable income per capita was 30,678 CNY, while that of rural areas was only 6,625 CNY, a gap of 4.631. It seems that the income gap between urban and rural residents’ in China is also spatially imbalanced, as it is usually smaller in the eastern coastal region and larger in the central and western regions (such as inland non-provincial capital cities). Those characteristics are consistent with the findings of the existing literature (Peng, 2009). Based on the unique dualistic urban–rural economic structure in China, the urban– rural income gap has always been a hot topic in geography, sociology and economic research. In recent years, with the rapid progress of urbanisation in China, more scholars have paid attention to the temporal and spatial characteristics of the urban– rural income gap and the factors influencing it. Zhang et al. (2017) analysed the spatial pattern of the urban–rural income gap in the Yangtze River Economic Belt in China, and found it was often larger in western areas but smaller in central and eastern areas. According to the existing literature, the main factors affecting the urban–rural income gap include geographical conditions, agricultural modernisation, and characteristics of economic development, level of industrialisation and urbanisation, and relative development strategies. Infrastructure construction can also significantly affect the

Fig. 3.31 Disposable income per capita of urban, rural residents in prefecture-level cities in 2017. Data source China’s City Statistical Yearbooks published by National Bureau of Statistics (CNY)

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Fig. 3.32 Urban–rural gap in disposable income per capita of prefecture-level cities in 2017. Data source Calculated from China’s City Statistical Yearbooks published by National Bureau of Statistics

income gap between urban and rural areas (Song & Wang, 2019). Considering these facts, backward areas are expected to improve the service quality of the infrastructure, promote economic exchanges between urban and rural areas and provide rural residents with more employment opportunities to narrow the urban–rural income gaps. Several studies have proved that urbanisation rate has a significant effect on the scale of the urban–rural income gap (Ouyang & Wang, 2014; Xiang & Xu, 2016; Yang et al., 2015). In the eastern coastal areas of China, the level of urbanisation is relatively higher, and the economy is relatively more developed. Not only is the infrastructure provision in urban areas better, but also most rural areas can get access to better facilities and social welfare. The economic exchanges and social ties between urban and rural areas are closer than that in western regions, and many farmers can obtain employment opportunities in local companies. As a result, the income of both urban and rural residents living in eastern China is generally higher, and the income gap is smaller. However, the level of urbanisation in the central and western regions is much lower, and some rural areas are still quite remote and isolated. The employment opportunities and social welfare of farmers are more limited, which seems to correlate closely with the larger urban–rural income gap. In addition, the previous literature has shown that factors such as geographical environment, industrial structure in rural areas and fertility rate may also cause differences in the urban–rural income gap between eastern, central and western China. For example, a study based on the income data from 1980 to 2004 pointed out that eastern China has favourable geographical conditions such as being close to the sea and a rural

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society that is more closely linked with the urban society. Thus the urban economy and the rural economy are better integrated and able to grow together. However, the western provinces are mostly located in hilly areas or basins, which have more complex topography and landforms. The connection between urban and rural areas is often limited by geographical obstacles, which may widen the urban–rural income gap (Peng, 2009).

3.3 Conclusion Based on the current status of China’s society and economy, this chapter has focused on China’s population growth and the dualistic urban–rural structure of the population. Using national statistical yearbooks, local statistical yearbooks, and multiple sources of statistical data, the first section has summarised the overall characteristics and changing trends of China’s population growth and the second section has described and analysed both the temporal and the spatial characteristics of China’s urban–rural structure in recent years. The findings can help readers to understand China’s population growth better, as well as providing empirical evidence to guide the development of a high-quality, people-oriented and sustainable transport system based on changes in population. China is currently in a period of social transformation especially in terms of population size and structure, with the dualistic urban–rural structure changing greatly in the past few decades. Since the founding of the People’s Republic of China, there has been a stable trend of linear growth in total population. However, the overall natural population growth rate appears to be reducing and the population growth is beginning to slow down. A large amount of evidence has shown that the changes in total fertility rate are largely responsible for the changing trend of population growth rate. During the past few decades, the fertility rate in China has been closely related to natural disasters and fertility policies. Since 1970s, the widely implemented fertility policy (one couple was only allowed to have one child except in a few exceptional cases) has promoted the concept of giving birth later, sparser and fewer, which finally resulted in a sharp decrease in the total fertility rate and slower population growth according to the statistical evidence. Influenced by the trend of fewer children, the population aging problem has become more prominent and the quantitative demographic dividend is decreasing. The previously promoted one-child policy was no longer suitable for China’s social and economic development. Recently, the adjusted fertility policies in China have provided more opportunities for couples of the appropriate age to have more children. However, under the remaining impacts of historical, socioeconomic and cultural factors, people’s attitudes towards giving birth have changed greatly, and most ageappropriate couples are reported to be less willing to raise children. The long-term maintenance of a low fertility rate has had a profound impact on the future growth of China’s population, leaving China stepping into the demographic transition stage of slowing population growth and accumulating negative growth inertia. Domestic

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studies generally suggest that with the long-term decline of fertility rate and the significant trend of population aging, China’s population growth rate may further reduce until it enters the era of negative growth. At the same time, characteristics and changes in China’s dualistic urban–rural structure also deserve attention. With the proposal and promotion of the New Urbanisation concept (which means people-oriented urbanisation), China’s urbanisation process in the future may shift from an increase to a qualitative improvement, and the growth of the urbanisation rate is certain to slow down gradually. The disposable income per capita of both urban and rural residents has been steadily increasing, indicating that China’s economy has made overall progress and that people’s life quality have improved notably. Meanwhile, due to the influence of the dualistic urban–rural structure, the urban–rural income gap still appears significant. Employment in rural areas has undergone transformation, and the number of migrant workers in cities has stabilised. More and more migrant workers have become localised and are occupied around their local areas. The share of wage income in the total income of villages has risen, while the share of operating income has declined. Operating income is less frequently derived from primary industry, and increasingly from tertiary industry. In addition, the characteristics of urban–rural structure have huge regional differences. Although the overall urbanisation rate in China has risen significantly, there are still large differences among regions. At the same time, the spatial distribution of urban and rural residents’ income per capita remains uneven. The income gap in areas with higher urbanisation rates is smaller, while areas with lower urbanisation rates tend to have more significant gaps in income between urban and rural areas.

References China Rural Family Research and Innovation Team, Z. U. (2019). China’s Rural Family Development Report 2018. Zhejiang University Press. Fang, Y., & Wei, J. (2021). Ji yu shou ru shi jiao de wo guo cheng xiang shou ru cha ju ying xiang yin su yan jiu. [Research on the influencing factors of China’s urban-rural income gap from the perspective of income structure]. Zhejiang Social Sciences (07), 54–65+157. https://doi.org/10. 14167/j.zjss.2021.07.006. Fei, X. (1993). Vernacular China and vernacular reconstruction. Fengyun Times Publishing House. He, Q. (2020). Zhong guo cheng xiang ju min shou ru cha ju lai yuan de jie gou fen jie. [Structural decomposition analysis for the sources of urban-rural income gap in China]. Statistics & Decision, 36(20), 76–79. https://doi.org/10.13546/j.cnki.tjyjc.2020.20.016. Liang, L., Xian, Y., & Chen, M. (2022). Gai ge kai fang yi lai zhong guo qu yu ren kou yu jing ji zhong xin yan jin tai shi ji qi ying xiang yin su. [Evolution trend and influencing factors of regional population and economy gravity center in China since the Reform and Openning-up]. Economic Geography, 42(02), 93–103. https://doi.org/10.15957/j.cnki.jjdl.2022.02.011. Luo, C., Li, S., & Yue, X. (2021). Zhong guo ju min shou ru cha ju bian dong fen xi (2013–2018). [An analysis of changes in the extent of income disparity in China (2013–2018)]. Social Sciences in China (01), 33–54+204–205. Ouyang, J., & Wang, Y. (2014). Cheng zhen hua dui suo xiao cheng xiang cha ju de ying xiang. [Influences of urbanization on narrowing the gap between urban and rural income]. Urban Problems (06), 94–100. https://doi.org/10.13239/j.bjsshkxy.cswt.140617.

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Peng, Z. (2009). Zhong guo dong zhong xi bu di qu cheng xiang shou ru cha ju bi jiao fen xi. [A comparative study on urban-rural income gap in east, central and west regions of China]. Economic Geography, 29(07), 1087–1091+1074. Sheng, M. (2018). The 40 years of China’s migrant workers: 1978–2018. China Workers Publishing House. Song, J., & Wang, J. (2019). Qu yu cheng xiang shou ru cha ju de dong tai shou lian xing yu ying xiang yin su tan jiu. [Dynamic convergence of urban-rural income gap and its influencing factors]. Economic Survey, 36(01), 18–25. https://doi.org/10.15931/j.cnki.1006-1096.20181213.013. Su, L., Yu, H., & Guo, W. (2022). Zhong guo ren kou chu sheng lv de di yu cha yi yan jiu. [Research on the regional difference of birth rate in China]. Journal of Chongqing University of Technology (Natural Science), 1–6. Sun, L. (2003). Rupture: China’s society since the 1990s. Social Science Literature Press. United Nations Department of Economic and Social Affairs. (2019). The 2019 Revision of World Population Prospects. New York. United Nations. Wang, X. (2019a). Zhong guo nong min shou ru jie gou de yan hua luo ji ji qi zeng shou xiao ying. [The evolutionary logic of Chinese farmer’s income structure and measurement of their income growth effect]. Journal of Southwest University (Social Sciences Edition), 45(05), 67–77+198– 199. https://doi.org/10.13718/j.cnki.xdsk.2019.05.008. Wang, Y. (2019b). Analysis on the changes of China’s rural family structure since reform and opening. Social Science Research (4), 95–104. Xiang, S., & Xu, F. (2016). Zhong guo de cheng zhen hua he cheng xiang shou ru cha ju. [Urbanization and urban-rural income disparity in China]. Statistical Research, 33(04), 64–70. https:// doi.org/10.19343/j.cnki.11-1302/c.2016.04.009. Yang, S., Tang, F., & Wu, X. (2015). Wo guo cheng xiang shou ru cha ju yu cheng zhen hua lv de dao U xing guan xi yan jiu. [The inverted U-shaped curve between Chinese urban-rural income inequality and urbanization rate]. Management Review, 27(11), 3–10. https://doi.org/10.14120/j. cnki.cn11-5057/f.2015.11.001. Zhang, G., Wang, F., Kang, J., Yang, H., & Ding, Z. (2017). Chang jiang jing ji dai xian yu cheng xiang shou ru cha ju de kong jian ge ju ji qi ying xiang yin su. [Spatial pattern of urban-rural income gap and its influencing factors at county level in Yangtze River Economic Belt]. Economic Geography, 37(04), 42–51. https://doi.org/10.15957/j.cnki.jjdl.2017.04.006. Zhao, X. (2018). Cheng xiang zhong guo. Tsinghua University Press.

Chapter 4

Family Structure, Gender and Education

4.1 Family Structure 4.1.1 Changes in Family Structure 1) Smaller family size In the past few years, family structure in China has gone through huge changes. Unlike the previous family structure, which was mainly dominated by 3-person and 4-person households, 2-person households and 3-person households have become more common. Furthermore, there appears to be an increasing trend of single-person households. According to China Population and Employment Statistics Yearbooks, over the years, the average family size in China has shown a continuous decline since 1990. In 1990, the average family size was 3.96, and many households had two or more children. Since 2000, the average family size has dropped below 3.5. The strict implementation of China’s 1-child fertility policy and accelerated socioeconomic development seemed responsible for the decline of couples’ willingness to give birth. More and more families only wanted one child, and the phenomenon of onechild families became popular. In 2010, the national average family size had dropped below 3.10. Although the adjustments in China’s fertility policy since 2014 may have encouraged some couples to raise more children to an extent, the influence seems to be quite weak and limited. After 2016, the average family size continued to shrink. By the end of 2018, the average family size in China was only three persons per household (Fig. 4.1). According to the yearbooks published by the National Bureau of Statistics, the proportions of households of different family sizes in China have changed considerably since 2000. The proportion of 3-person households is decreasing year by year, and it has even dropped below that of 2-person households. Meanwhile, 2-person households are becoming increasingly common. One-person households have also shown a stable trend of increasing. In 2018, the proportion of 1-person households © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Zhao and D. Yuan, Population Growth and Sustainable Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-7470-0_4

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Fig. 4.1 Average family size in China. Data source China’s statistical yearbooks, published by the National Bureau of Statistics

surpassed that of 4-person households for the first time, becoming one of the three major family types in China (Fig. 4.2). More specifically, comparisons were made among the proportions of households with different sizes in the total number of households in 2000, 2010, and 2018. The

Fig. 4.2 Proportions of households of different sizes in China. Data source China’s statistical yearbooks, published by the National Bureau of Statistics

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proportion of households with more than three persons has declined significantly by 13.1 percentage points from 2000 to 2018. The proportion of 4-person households dropped by 6.51 percentage points, and that of 5-person households dropped by 5.04 percentage points. At the same time, the proportion of 2-person households increased by 11.25 percentage points, and that of 1-person households increased by 8.39 percentage points. Evidence shows that the proportion of small households has increased significantly, resulting in the trend of China’s families getting much smaller (Figs. 4.3, 4.4 and 4.5). According to nationwide statistical data in recent years, the average size of China’s families in different regions had similar downward trends before the implementation of the 2-child policy, but then slightly rebounded after 2014. Although the changing trend is relatively consistent, the absolute value of family size in different regions

Fig. 4.3 Proportions of households of different sizes in 2000. Data source China’s statistical yearbook, published by the National Bureau of Statistics

Fig. 4.4 Proportions of households of different sizes in 2010. Data source China’s statistical yearbook, published by the National Bureau of Statistics

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Fig. 4.5 Proportions of households of different sizes in 2018. Data source China’s statistical yearbook, published by the National Bureau of Statistics

seems to vary substantially. In general, large families are more common in the western region, and the average family size appeared much higher there than in other regions of China, followed by the central region and then the eastern region. The average size of families in the northeastern region remains at the lowest level. Based on prefecture-level statistical data, the spatial distribution of average family size in each province seems quite uneven. Larger family sizes are more common in the western, more underdeveloped, inland and minority areas. According to statistical yearbooks, provinces in the southwest and northwest tend to have larger family sizes than those in the east, especially the northeast. Tibet has always been at a high level, with the average family size being 4.77 in 2000 and dropping slightly to 4.01–4.25 since 2010. Beijing, Shanghai, Zhejiang and Northeast China remain at the lowest level in terms of average family size. In 2000, the average family size in those regions was about three, and it had dropped to below 2.75 by 2016. Compared with inland areas, coastal areas (except for Hainan) often have smaller average family sizes. Evidence also shows that economically developed areas (such as Beijing, Shanghai and Zhejiang) have significantly smaller family sizes, while the underdeveloped western regions and ethnic minority areas (such as Hainan Province) have larger average family sizes. There may be multiple reasons for the spatial differentiation of family size among regions. Generally, factors directly influencing family size include the following: (a) number of children born to the family. The more children born, the larger the family size; (b) whether the family has been separated. If brothers and sisters live separately after they get married, the original large family will be split into several smaller families. For families that do not separate immediately after the members’ marriages, the family size will be larger due to the joining of new members; (c) The mobility behaviour of family members. When some members move to another city and live there for a long time, the size of the family will be smaller than when there is no migration, since the family size reported by statistical data only takes members living

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together into consideration. Among these possible factors, the number of children born into the family seems to be the main and most direct one, and this has been significantly influenced by fertility policies in the context of China. In terms of the actual implementation of China’s fertility policy, specific measures are quite different in different regions and areas (Figs. 4.6 and 4.7). The Instructions on Seriously Advocating Family Planning issued by the Central Committee of the CPC and the State Council in 1962 emphasised the significance of “promoting birth control in cities and densely populated rural areas.” It implied that the implementation of fertility policy should be based on local conditions. The adjusted version of original fertility policy also pointed out that “the constraints for rural residents to have a second child could be relaxed appropriately. Minorities with a small population are allowed to have a second child, and in special cases a third child can be allowed.” In 2015, the universal 2-child policy (allowing one couple to have two children) also emphasised that different cities can formulate specific implementation measures based on actual needs for the sustainable development of population. In every stage during the evolution of China’s fertility policy, there have been differences in the specific implementation among different regions and for different ethnic groups. In rural areas with low population densities and ethnic minority settlements with small populations, political and social restrictions on the number of children are often fewer, and families are more likely to be allowed to have two or even more children. As a result, the average family size in these areas tends to be higher than that in high-density metropolitan areas, where restrictions on couples giving birth are much stricter. 2) Fewer generations Currently, 1-generation and 2-generation households have become the dominant family types in China, accounting for nearly 90% of households. The average number of generations in a family has decreased significantly, from 1.98 in 2000 to 1.85 in 2010. Both the proportion of households with more than three generations and that of 2-generation households have declined, while the proportion of 1-generation households has increased markedly. According to national statistics, the proportion of

Fig. 4.6 Average family size of different prefecture-level cities in 2005 and 2015. Data source The National 1% Population Sample Survey, released by the National Bureau of Statistics

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Fig. 4.7 Proportions of small households (no more than three members) in 2005 and 2015. Data source Calculated based on the National 1% Population Sample Survey, released by the National Bureau of Statistics

households with no more than two generations has remained high from 2000 to 2010, with 1-generation households rising from 21.70 to 34.18%, while the proportion of 2-generation households dropped by 11.49 percentage points. Since 2010, China’s generational family structure has been continuously changing. From 2010 to 2018, the proportion of 1-generation households increased by 6.13 percentage points, while that of 2-generation households decreased by 5.99 percentage points, resulting in a smaller gap between the numbers of 1-generation and 2-generation households. According to a survey report on the characteristics of China’s family structure,1 1-generation households2 have become a main type since 2014 (Fig. 4.8). In terms of the differences among regions, the proportion of 1-generation households in the eastern, central and western regions is about 45%, while that in the northeast is as high as 56.4%. In terms of the difference between urban and rural areas, the proportions of 1-generation households tend to have larger urban–rural gaps in eastern, central, and western regions. The gaps in these regions were around 3 percentage points, while that in northeastern region was only 0.3 percentage points. Generally, 2-generation households account for a much higher proportion in urban areas than in rural areas, while that of 3-generation and 4-generation households in urban areas appears lower than that in rural areas. The simplification of family generations and the reduction in family size might enable residents to have more flexible and personalised travel behaviour such as mode choice. As a result, new expectations and requirements for the convenience and privacy of transport services caused by changes in family structure need to receive more attention along with the future optimisation of the transport system. National-level statistical data also show some significant regional gaps in the number of household generations. According to the 1% Population Sample Survey 1

Data source: “China’s family size and structure characteristics survey report,” http://www.china. com.cn/guoqing/2015-12/29/content_37416225.htm, visited on September 30, 2020. 2 According to the report, the family size is the number of family members who live in the same household as the family head. The family head is the person who takes charge of the main affairs in a family. Family members do not include those who have no blood relationship or kinship with the family head, or those who have left the family and live in other places.

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Fig. 4.8 Number of generations in households in 2000, 2010, 2018. Data source China’s statistical yearbook, published by the National Bureau of Statistics

data for each province in 2005 and in 2015, the proportion of families with three or more generations has decreased significantly during the decade, especially in Inner Mongolia, Jiuquan City, Yushu Tibetan Autonomous Prefecture and Tibet. For example, the number of families with three generations or more in Tibet accounted for 31.42% in 2005, but fell by nearly eight percentage points to 23.96% by 2015. In Inner Mongolia, the proportion of families with three generations or more fell to less than 10% in 2015. However, while the proportions of multigeneration families in some areas have reduced significantly, those in more than half the areas (municipalities, provincial-level cities or autonomous prefectures) experienced slight increases of around 1–2 percentage points. The significant aging process in some areas in China may be a major reason for this phenomenon. Many older people prefer to live with their children and grandchildren to avoid the inconvenience or hidden risks of living alone, which may finally lead to a small increase in the proportion of 3generation families. Despite the slight increase, the proportion of households with three generations and above in most areas in 2015 was less than 25%. The areas with higher proportions of multigeneration families were mainly in the Gannan Tibetan Autonomous Prefecture in Gansu, Baoshan in Yunnan, and a few cities in Guangdong Province (Fig. 4.9). To conclude, the family with no more than two generations is still the dominant family type in the current context of China. 3) More diverse family types According to previous studies, family structure (sometimes called household structure) refers to the relationship between family members determined by natural and social attributes. In terms of the detailed definition of family structure, different scholars hold different points of view. One study on family sociology (Deng & Xu,

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Fig. 4.9 Proportions of households with at least three generations in 2005 and 2015. Data source Calculated based on the National 1% Population Sample Survey, released by the National Bureau of Statistics

2001) regards it as the combination of relationships among family members, which contains both horizontal relationships (such as the relationship between husband and wife, brothers and sisters) and vertical relationships (such as the relationship between father and son, mother and daughter). Some researchers concluded that the essence of family structure was the type or form of basic living units composed of members with blood, marriage and adoption relationships (Wang, 2009). There are also different views of the classification of family structure. Depending on the relationship between the family head (the one taking charge of main affairs, usually the husband) and other family members, there are several common types of families in China. (1) Nuclear family: a family composed of a couple or unmarried children, which can be divided into three subclasses. (a) A standard nuclear family, where the couple and at least one unmarried child live together and are economically integrated. (b) A nuclear family living together with separate wealth, where the couple and their unmarried children live together but at least one of them is financially independent from them. (c) A nuclear family with members living separately but sharing their wealth, where the couple and the unmarried children are economically integrated, but at least one of the couple or all of the children do not live together (Ma et al., 2013). (2) Main family: also called direct family, which is composed of couples of more than two generations (with each generation less than one couple) and the members of each couple are both alive. Main family can be further divided into two subclasses: (a) a main family living together and sharing wealth, where couples of more than two generations live together and are economically integrated; and (b) a main family living together but not sharing wealth, where couples of more than two generations live together but at least one of them is financially independent (Ma et al., 2013). (3) Joint family: a family composed of at least one parent and several married children, or a family where the brothers and sisters have not lived separately since being married (Zhou, 2016).

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(4) Single-parent family: an incomplete family composed of only one parent and his or her children. Single-parent families are usually formed due to the couple’s divorce or the death of one parent. (5) Single family: a family with only one member, who is unmarried, divorced, or widowed. According to data from the existing literature (Table 4.1) and CFPS (Fig. 4.10), the nuclear family still maintains the dominant position in China’s family structure. However, its share has been gradually decreasing. In 2010, a significant drop in the share of nuclear families ended the stable percentage of nuclear families at around 70%, where it had been since 1982. According to CFPS, in 2014, the proportion of nuclear families in China had fallen to below 60%. At the same time, the proportions of main families and single families continued to rise. Before 2000, single families accounted for less than 9%, while the share rose to 13.67% in 2010 and continued to rise. In 2014, the shares of main family and single family had increased to 24.20% and 14.40%, respectively. Now, the nuclear family, main family and single family appear to be the three most common family types, accounting for more than 98% of families in China. These general changing trends of different family types are occurring in both urban and rural areas. Meanwhile, there is also some evidence of the urban–rural gap in terms of family structure (Table 4.2). The share of nuclear families in rural areas is lower than that in urban areas, while the share of main families is higher than that in urban areas. One reasonable explanation may be the reduction in the separation after children’s marriage phenomenon among rural families. After the introduction of the 1-child fertility policy in the 1970s, the number of children in a family dropped sharply in China. Children (especially only children) tended to be less willing to live separately from their parents after getting married. For females with permanent jobs, living with parents may help to reduce the pressure of raising little children and doing housework. As a result, some of them prefer just living together with their older parents. This change in people’s living preferences might have contributed to the increased share of main families in rural China (Wang, 2019). Currently, non-traditional types of families that differ from the traditional family types in China are gradually increasing. Non-traditional family types generally refer Table 4.1 Proportions of different family types in China from 1982 to 2010 (Unit: %) Family type

2010

2000

1990

1982

Nuclear family

60.89

68.18

70.61

68.30

Main family

22.99

21.72

21.33

21.74

Joint family

0.58

0.56

1.08

0.92

13.67

8.57

6.34

7.98

Single family Single-parent family

0.93

0.71

0.57

0.84

Other types

0.93

0.26

0.08

0.22

Data source Wang (2013)

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Fig. 4.10 Proportions of different types of family in 2014. Data source Calculated from China Family Panel Studies in 2014

Table 4.2 Proportions of different types of rural families in China from 1982 to 2010 (%) Family type

2010

2000

1990

1982

Nuclear family

57.02

66.27

69.88

67.95

Main family

28.52

24.83

22.46

22.82

Joint family

0.67

0.51

0.95

0.84

11.79

7.52

6.09

7.47

Single-parent family

1.18

0.74

0.56

0.71

Other types

0.81

0.13

0.06

0.21

Single family

Data source Wang (2019)

to family types including the pure old family (a family composed of members aged above 60 years old), empty nest family, intergenerational family, no-child family, older single family, and single-parent family. Those new types of family seem to have accompanied the new and diverse attitudes towards fertility and marriage in people’s modern lives. According to the 2017 White Paper on the Development of Beijing’s Aging Career and the Construction of the Elderly Care System, released by Beijing Municipal Office on Aging, the share of purely old families in Beijing has gone through a significant increase in recent years. At of the end of 2017, there were 521,400 older residents belonging to purely old families, accounting for 15.6% of the total number of older people. The total number of those older residents was 78,000 more than in 2010. In addition to the general form composed of one older couple, there are also some special forms of purely old family, including those composed of one older couple living with their parents, older people living with their brothers and sisters, or one older couple living together with their grandchildren. These days, those special forms of purely old families have become more common, especially in rural areas of China.

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107

Recently, no-child families have become much more common in China. In March, 2018, the Evergrande Research Institute investigated the willingness to give birth of 120,000 respondents through an online survey.3 The results showed that around 15% of respondents preferred not to have any children, indicating that there may be a large number of no-child families in the future. However, things are not the same in different areas since the willingness to give birth varies largely across regions. Recently, the National Bureau of Statistics Shanghai Survey Team organised a survey of 1,237 married residents aged between 20 and 49 in Shanghai.4 It found that over 60% of the respondents were willing to have more than two children, especially those with high education and income levels, while only 1.7% of the respondents preferred to have no child. In general, employment type and income level seem to be the main factors influencing their willingness to give birth. With the huge changes in China’s socioeconomic status and fertility policy in recent years, residents’ willingness to give birth is likely to change over time. Development of peopleoriented and sustainable transport in China needs to take account of these changing trends in family composition. In addition, there are obvious regional differences among cities, towns and villages, especially in terms of the family type including older members. According to China’s Sixth National Census in 2010, the shares of family types including at least one older member were all higher in villages and towns, and lower in cities (urban areas). The share of families with one member aged 60 or above represented the most significant urban–rural difference. In urban areas, the share of this kind of family accounted for only 13.31%, while it was up to 21.81% in rural areas. The share of families with two people aged 60 or above accounted for 10.67% in urban areas, but 13.85% in rural areas. Similarly, families with three people aged 60 or above accounted for 0.20% in urban areas but 0.37% in rural areas. Furthermore, among families with one member aged 60 or above, the share of those without adults’ support (such as single older people alone or one older person living with younger relatives) showed no such difference between urban and rural areas, which were both around 26%. However, among families with two members aged 60 or above, the share of those without younger adults’ support was as high as 45–50%. This indicated that almost half of older couples live without their children taking care of them; as a result they have to pay for living expenses on their own and may even have to raise young grandchildren (Fig. 4.11).

3

Data source: Evergrande Research Institute, http://finance.sina.com.cn/china/gncj/2018-08-13/ doc-ihhqtawy3345670.shtml, visited on Sept. 30, 2020. 4 Data source: Paper News. https://www.thepaper.cn/newsDetail_forward_10666674, visited on January 10, 2021.

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Fig. 4.11 Family structure of families with at least one older member in 2010. Data source China’s 6th National Census Data in 2010, released by the National Bureau of Statistics

4.1.2 Migrant Families and Family Employment 1) Migrant families and associated vulnerable groups In the past several decades, China’s migrant population has been increasing steadily. In 1982, the migrant population was only 11.54 million people, but then the number rapidly grew to 37.5 million by 1990. With the fast development of big cities and other urban areas, the scale of the migrant population in China continued to grow to 102 million in 2000 and 221 million in 2010. However, the overall trend of China’s migrant population has changed from a continuous increase to a slight decline since 2015. According to the results based on a 1% population survey held by the National Bureau of Statistics in 2015, the scale of China’s migrant population was about 247 million then, with a decrease of about 6 million since 2014, and it continued to drop to 244 million in 2017 (China Rural Family Research and Innovation Team, 2019). Some forecasts of China’s population indicate that by 2030, the number of migrants will fall significantly to 150–160 million (National Health and Family Planning Commission, later renamed National Health Commission, 2016a). Although the total number of migrants has entered a period of stability, the impact of migration on China’s family structure seems still quite significant. Family is regarded as the basic social unit for people’s daily lives and a harbour for migrants to meet their material needs and obtain spiritual sustenance. At the same time, migration brings opportunities and challenges to families, changes the economic status and lifestyle of the family, and influences the composition of the family. With people’s desire for high-quality life getting stronger, the migrant population tends to have more expectation of living together with families. As a result, more and more migrants

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prefer to move together with their wives and children, and then live together in the inflow cities. In May 2015, the National Health and Family Planning Commission of China (renamed the National Health Commission in 2018) produced the China Family Development Report. It pointed out that in 2014, migrant families accounted for up to 17.2% of families where the head member’s residence and registration were not in the same township (or street). Among 2-person migrant families, 81.7% were composed of one couple moving to work at another place together. Among 3-person migrant families, 84.7% were composed of one couple who move to another place with their children. It seems that migrant family has become a special but important family type in China. In 2016, the Migrant Population Department of the National Health and Family Planning Commission published the China Migrant Population Development Report 2016, and it pointed out that the trend of family migration is obvious. Family migration often refers to several family members flowing into a resettlement place at one time or in batches (Sheng, 2013), indicating that the migration behaviour is linked with the family instead of the individual as the basic unit. The share of families with two members migrating together has exceeded 25%, which means that a large number of migrants prefer to move with their families rather than alone. Among migrant families, most cases were one couple moving together. Thus, in the current period of social transformation in China, many migrants no longer move alone for employment, but more often move with their partners, children and even older relatives to realise holistic family migration. As a result, daily activities of migrant families in the inflow areas such as making a commuting trip or taking a holiday might bring more diverse demands and new requirements for the operation and management of the transport system. 2) Migrant children One major social issue triggered by family migration is the education of migrant children in inflow areas. According to China’s Fifth and Sixth National Census, 14.06 million migrant children moved with their parents in 2000, and the number grew rapidly to 28.8 million in 2010. The share of migrant children in the total number of children increased from 4.9% in 2000 to 10.3% in 2010. The challenges migrant children face in getting access to education became more severe. In most cases, it is difficult for migrant children to get the same education opportunities as local children, especially in cities with high-quality education such as Beijing. Data shows that in 2016, 2.94% of school-age migrant children in China could not receive compulsory education in time. The problem of younger migrant children entering school late is widespread, and the proportion of older migrant children receiving high-school education is low. Many older migrant children have to move back to their home towns for the college entrance examination, since the inflow city will not provide the necessary access. It is estimated that the annual number of migrant children who wanted to take the college entrance examination in inflow cities reached about 187,000 (China’s National Health and Family Planning Commission, 2016a). For people-oriented and sustainable development in the future, the daily needs of migrant children in inflow cities need to receive more consideration through

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optimising the provision and allocation of education resources, medical services, and transport facilities. 3) Left-behind children Like migrant children, left-behind children are also a unique group under the background of population migration in China. According to the Opinions of the State Council on Strengthening the Care and Protection of Left-behind Children in Rural Areas released in 2016, left-behind children are minors under the age of 16 whose parents both live in another city for work, or one of them lives in another city but the other is incapable of guardianship. Broadly speaking, left-behind children are divided into urban and rural left-behind children according to their household registration. However, narrowly defined left-behind children are children left in rural areas due to parents’ migration, and they are often mentioned in existing literature and policy documents. Data from the National Census show that in 2000, there were 23.09 million rural left-behind children living without both parents, and they accounted for 8.4% of the total number of children. By 2010, the number of rural left-behind children had increased to 61.03 million, accounting for 21.9% of all children. In recent years, the number of left-behind children in rural areas has gradually declined, for two possible reasons. On the one hand, more migrant workers are bringing their children with them while moving to a city for employment, with the living environment of migrants in inflow areas having been largely improved in recent years. On the other hand, China has made great efforts to encourage migrant workers to go back to their home towns to work. With more employment opportunities available, many parents have the chance and the willingness to work locally and to live with their children. In 2018, the Ministry of Civil Affairs released the National Information Management System for Left-behind Children and Children in Difficulties in Rural Areas. According to the statistical results, the total number of left-behind children in rural areas has dropped sharply since 2016, with a reduction of 22.7%. In 2018, there were around 6.97 million left-behind children. In terms of the spatial distribution pattern, Sichuan, Anhui and Hunan are the three provinces with the largest numbers of rural left-behind children. Each of the three has more than 700,000 left-behind children, accounting for 11.0%, 10.6% and 10.1% of the total number of children in China, respectively. Compared with migrant children, rural left-behind children generally have much more difficulty in completing the necessary education and growing up healthily with enough family support. Although governments in many rural areas have already noticed the great challenges left-behind children face and have made efforts to provide more convenience and improve their life quality, they are definitely still at a disadvantage in terms of accessibility to education as well as other facilities, and they deserve further attention. The data show that although rural left-behind children have had a quite high enrolment rate and compulsory education completion rate in recent years, the net enrolment rate of high school is still 20 percentage points lower than the national average level, and less than half of that in urban areas (China’s National Health and Family Planning Commission, 2016b).

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Fig. 4.12 Rural left-behind children learning to make dumplings with teachers. Data source http://ah.ifeng. com/c/8KnHVXt1N1x, visited on Nov. 10th, 2022

More than 50% of the left-behind children in rural areas are in the 6–13 age group, which is a critical age range for receiving compulsory education. Thus, the lack of education or low quality of education may leave left-behind children with fewer employment opportunities than others when they enter the labour market. In addition to this disadvantage in education, another challenge left-behind children face is their inability to maintain daily and close communication with their parents. According to data released by the Ministry of Civil Affairs in 2018, 96% of rural left-behind children live with grandparents, and 4% of rural left-behind children are taken care of by other relatives or teachers (Fig. 4.12). A lack of family support and psychological counselling from parents may have an adverse effect on children’s physical and mental health, which may further lead to them having withdrawn personalities, poor social skills, and insecurity. 4) Left-behind older people In addition to left-behind children, another disadvantaged group is left-behind older people, who live alone in rural areas. This situation has also become common with the large-scale migration of the rural population. The Report on the Development of China’s Migrant Population 2016 defined left-behind older people as “people aged over 60 whose children have left the district or county (mostly due to their jobs) for at least 1 month.” More specifically, those with all children living far away are called “completely left-behind,” and those with still one child living with them are called “half-left-behind.” In 2015, the proportion of older people who moved together with their migrant children was only about 3%. More than 90% of the families with older members have at least one left-behind older person. Since, these left-behind older people are usually faced with huge difficulties in daily life with no children looking after them, their livelihood needs and transport demands should be seen as urgent and receive actual attention from all sectors of society. In most cases, left-behind older groups may face various challenges with their physical and mental health. In 2015, the National Health and Family Planning Commission (later renamed the National Health Commission) held a survey called National Monitoring Survey of the Outflow about Health and Family Planning

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Services for the Migrant Population in 10 provinces with large-scale outflow population in China, namely Hebei, Liaoning, Jilin, Heilongjiang, Jiangsu, Zhejiang, Anhui, Henan, Guangdong and Sichuan. In total, 13,992 older people were investigated including the left-behind, the semi-left-behind and the non-left-behind. According to the survey results, more than 30% of older people in outflow areas suffered from a sense of loneliness. Among them, completely left-behind older people have larger likelihoods of feeling lonely than half-left-behind older people. As for physical health, about 30% of older people in outflow areas unhealthy in some way. More specifically, those who are unhealthy but can still take care of themselves accounted for 25.84% of the sample, while those who rated themselves as “cannot take care of themselves” accounted for 3.72%. In addition, the survey found a lack of channels for promoting health knowledge to older people in the outflow areas. More than 70% of older people can only obtain knowledge and information about health through TV. The proportion of older people who use new communication channels (such as WeChat) to learn about health issues was relatively low, which may leave the left-behind older people out of touch with society. To conclude, it is obvious that left-behind older people in China face several problems with psychological, physical, spiritual and cultural issues. The widespread existence of left-behind older people will certainly lead to more diverse demand and requirements for basic living services such as medical care, barrier-free facilities, and older people-friendly transport in rural areas. 5) Family employment According to China’s Household Survey Yearbook 2019 released by the National Bureau of Statistics, the average number of employed members per household in China has shown a slight decline from 2013 to 2018. In 2013, each household had an average of 1.8 employees, and the average number of family members (including the employees themselves) supported by each employee was 1.7. By 2018, each household had an average of 1.7 employees, and the average number of family members supported by each employee rose to 1.9. In recent years the average number of employees per household in China has reduced, and the proportion of dualemployed and multi-employed households has decreased, while the proportion of single-employed households has increased (Figs. 4.13 and 4.14). This changing trend of China’s family structure indicates that in many families, the employed member might be faced with larger and larger pressure to work to support the life of other unemployed family members. Based on the number of employed family members with fixed wage income, families can be divided into dual-employee families and single-employee families. Generally, in a dual-employee family, the couple both have permanent jobs and jointly support the living expenses of raising children and taking care of older family members through earning stable wage income. A single-employee family means that only one member is employed while the others are unemployed, having no income or only a little non-wage income. In a single-employee family, the living expenses mainly rely on the work of the employee, which may lead to significant economic pressure on the family. The factors that might affect the number of employees in

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Fig. 4.13 Changes in employed family members in China. Data source China’s Household Survey Yearbook 2019, released by the National Bureau of Statistics

Fig. 4.14 Proportions of employed family members in total members per household in China. Data source China’s Household Survey Yearbook 2019, released by the National Bureau of Statistics

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a family are both objective and subjective. On the one hand, the health status of family members (whether unable to participate in permanent jobs due to illness or disability), the education level and skills of family members (whether disadvantaged in education or lacking in professional skills for suitable jobs), and the age of family members (whether being too old to participate in the labour market) all significantly affect the employment status of family members. On the other hand, some family members may have little willingness to engage in permanent jobs for other personal reasons, such as the huge responsibility of taking charge of housework as a full-time mother. Generally speaking, full-time mothers (also called housewives or full-time wives) are married women who spend most of their time taking care of their children, husbands, and parents at home without participating in permanent jobs. According to a survey conducted in 2014 by the online community for women called Lamabang,5 the proportion of full-time mothers among married women is about 26%, and it has been on the rise in recent years. In most cases, married women in China are likely to become full-time mothers after their pregnancy or after giving birth, which forces them to pay more attention to raising babies. The survey showed that 46% of full-time mothers take pregnancy as the main reason and 34% choose to be full-time mothers due to giving birth. Only 12.8% of the respondents became full-time mothers for other reasons, such as “economic conditions permit.” While being full-time mothers, the focus of life for married women shifts from career to family affairs. With the huge changes in China’s fertility policy nowadays, including the promotion of the universal 2-child policy, balancing work and raising children as well as taking care of families may cause more pressure on married women in the future. Some women with good experience in their careers may prefer to give birth later, while others may choose to leave their jobs and become full-time mothers to have a better childbirth and raise a second child. Therefore, the livelihood needs and travel demands of full-time mothers require special attention to help to release the pressure by building a people-oriented transport service system for them.

4.1.3 Fertility Policy in China and Its Impacts on Family Structure 1) Previous fertility policy in past decades Fertility policy (or the policy of birth) refers to the guidelines for regulating the reproductive behaviour of couples of childbearing age, designated by or under the guidance of the government. In the context of China, the most typical practice of fertility policy is called family planning. Family planning means that to protect the rights and interests of society, families and couples of childbearing age should 5

Data source: http://news.changsha.cn/html/538/20140826/1847699.html, visited on October 20, 2020.

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devise a plan for raising a reasonable number of healthy children at an appropriate age. Through setting this kind of plan about fertility, families are more likely to have a high-quality life and help to promote the coordinated and sustainable development of population, economy, society, resources and the environment. As a basic national policy and a typical practice of fertility policy in the context of China’s unique cultural environment, family planning has had a profound impact on people’s reproductive behaviour and attitudes towards giving birth, and it may have a long-term influence on China’s population growth and population structure. The proposal and implementation of China’s fertility (family planning) policy has a specific historical background. Data from China’s First National Census conducted in 1953 showed that, after the founding of the People’s Republic of China, there was a rapid decline in total death rate along with a persistently high total birth rate. In 1949, the total death rate in China was about 20%, but it dropped to about 13% in 1953, while the birth rate at that time remained as high as 37%. This huge gap between birth rate and death rate prompted the rapid growth of China’s population to about 600 million people. The shortage of job opportunities, high pressure on food production, and inadequate social welfare became serious social problems, resulting in a contradiction between population and resources that urgently needed to be dealt with. It was necessary to control the growth of population and to improve the quality of life for newborns. In 1953, Deng Xiaoping, the then Vice Premier, first expressed support for contraception and birth control. Since then, China’s policy documents have successively advocated for contraception and controls on birth. The concept of family planning has been gradually deepened and clarified. In December 1962, the Central Committee of the CPC and the State Council jointly issued “Instructions on Seriously Advocating Family Planning” and for the first time family planning was put forward as a policy. It suggested “[promoting] birth control in cities and rural areas with high population density, and appropriately keep[ing] the natural growth rate of the population under control” to lead China’s fertility problem gradually from an unplanned and even disordered state to a planned state. The policy for family planning was regarded as of great necessity for the sustainable development China’s society in the actual context at that time. From the early 1970s to the 1980s, China’s family planning policy was implemented quite strictly based on the concept of later, sparser and fewer. To be specific, later refers to later marriage, which means that men should be at least 25 years old and women at least 23 years old when getting married. Sparser refers to lengthening the birth interval, and the suggested interval between two births is about 4 years. Fewer refers to fewer births, which means one couple should have no more than two children. After 1980, China’s family planning policy was gradually tightened towards a 1-child policy. In 1980, the Central Committee of the CPC issued an Open Letter to All Communists and Communist Youth League Members on the Issue of Controlling China’s Population Growth and pointed out that one couple should only have one child. In September 1982, the 12th National Congress of the CPC made family planning a basic national policy, which was later written into the newly revised Constitution of the People’s Republic of China in November. The Population and

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Family Planning Law of the People’s Republic of China stipulates that the government encourages couples to give birth to no more than one child, and only a few couples with certain needs or in special cases listed in the law are allowed to have a second child. Since then, the 1-child policy has been widely promoted across China. However, due to people’s dissatisfaction or unwillingness in many areas, especially in rural villages, the actual implementation faced great difficulties for a long period, calling for some adjustment. In 1984, the Central Committee of the CPC transferred to the National Family Planning Commission’s Report on the Status of Family Planning Work and decided to optimise the original policy further. It recommended promoting the family planning policy while considering the actual rationality and people’s willingness along with further optimisation while continuing to advocate the concept of only one child. The suggested specific measures included (a) relaxing requirements for rural residents to have a second child according to the actual situation, (b) allowing couples of ethnic minorities with small populations to have second children, and (c) in some special cases, allowing three children if necessary. At that period in China, the optimised family planning policy appeared more appropriate for the actual development of population, and the total birth rate rebounded from 1986 to 1988. 2) Changes in recent years: The new 2-child and 3-child policy The fertility policy in China was not formed overnight; instead, it has undergone lots of adjustments and optimisation based on the actual situations in different periods of socioeconomic development. There is no doubt that fertility policy determines the characteristics and changing trends of a country’s population growth and family structure. Thus learning about the changes in fertility policy is of vital importance for understanding the development of national population. In recent years, a large number of scholars have examined the evolution of China’s fertility policy. Due to differences in research purposes and classification criteria, there have been different opinions on the division of evolution, including 4-stage theory (Wang & Zhao, 2012), 5-stage theory (Li, 2013), 7-stage theory (Ma & Sun, 2011) and 9-stage theory (Feng, 2000). For example, Zhou (2018) divided the development process of China’s fertility policy into one period of ideological budding and experimentation (1949–1970), one of late, sparse and few (1970–1980), one of only one child (1980–2015), one of the selective 2-child policy (2013–2015) and finally, the universal 2-child policy. In 2013, after a long period of advocating one child only, China’s fertility policy underwent important reforms. On November 15, 2013, the Third Plenary Session of the Eighteenth Central Committee of the CPC released a Decision on Several Major Issues of Comprehensively Deepening Reform and proposed to “adhere to the basic national policy of family planning, and initiate the implementation of policy that couples who only have one child can have a second child.” It also suggested gradually optimising the fertility policy to support the long-term balanced development of the national population. In December of the same year, the Central Committee of the CPC and the State Council issued Opinions on Adjusting and Improving the Fertility Policy, officially launching the implementation of the selective 2-child policy, which allowed couples with only one child to have a second child.

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In 2015, China made further revisions to the fertility policy due to the low-level fertility rate and the prominent aging problem. In October 2015, the Fifth Plenary Session of the 18th Central Committee of the CPC proposed several suggestions for future policies, including promoting balanced population development, adhering to the basic national policy of family planning, improving the population development strategy, and fully implementing the policy that couples can have two children. In December 2015, the 18th meeting of the Standing Committee of the Twelfth National People’s Congress deliberated and approved the decision to revise the Population and Family Planning Law of the People’s Republic of China and clearly advocated for couples to have two children. It also emphasised that localities can formulate specific implementation measures according to the actual situation. The revised Population and Family Planning Law of the People’s Republic of China came into effect on January 1st , 2016. On that date, the policy that couples could only have one child, which had lasted for decades finally withdrew from the stage of China’s fertility policy. To deal with the social problems caused by the significant aging trend of China’s population better, fertility policy has continued to be adjusted further and revised in recent years. On May 31, 2021, the Political Bureau of the CPC Central Committee held a meeting to make a careful consideration of the Decision on Optimising the Fertility Policy to Promote Long-term Balanced Population Development. It pointed out that to optimise China’s fertility policy, a couple should be allowed to have three children, and that supporting measures should be implemented in time. On July 20, 2021, the CPC Central Committee and the State Council officially announced that policy,6 and on August 20, a meeting of the Standing Committee of the National People’s Congress voted to revise the Law on Population and Family Planning. It advocated marriage and birth at an appropriate age, and couples were permitted to have three children. This great change in fertility policy should be conducive to the sustainable development of China’s population, as well as giving full play to China’s endowment advantages in human resources. To promote the 3-child policy effectively, many cities and provinces have introduced practical measures to encourage people to give birth. For example, in January 2022, Beijing released the Implementation Plan on Optimising Fertility Policy to Promote Long-term Balanced Population Development.7 It proposes to provide basic healthcare services throughout the childbirth process, vigorously develop inclusive childcare services, and carry out both online and offline scientific childcare guidance services. In addition, Beijing aims to reduce the total cost of raising children by promoting education equity, increasing the supply of high-quality educational resources, and safeguarding women’s legal rights in the labour market.

6

Data source: Central People’s Government of the People’s Republic of China, http://www.gov.cn/ zhengce/2021-07/22/content_5626517.htm, visited on April 20, 2022. 7 Data source: Beijing Government, http://www.beijing.gov.cn/zhengce/zhengcefagui/202202/t20 220209_2606802.html, visited on April 20, 2022.

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In the future, China’s fertility policy is certain to be optimised further through various adjustment measures. According to the current policy, by 2025, a comprehensive policy system for supporting people in giving birth will be established with the aim of significantly reducing the cost of raising children. By 2035, the national policies and regulations should maintain China’s achievements in promoting the long-term balanced and sustainable development of its population. People’s willingness to give birth may be greatly improved and the population structure may be further optimised in the near future. 3) Impacts of the changing fertility policy Among the various factors affecting China’s family structure, fertility policy occupies an important position. Since the 1980s, China’s fertility policy for family planning, large-scale migration of population and the traditional custom of living separately after brothers’ marriages seem to be the main determinants of China’s family structure (Wang, 2009). It is foreseeable that the resolution of universal 2-child policy made by the Fifth Plenary Session of the 18th Central Committee of the CPC will have a long-term impact on family structure. While exploring and discussing the changing trend of family structure and its relationship with transport demand, much attention should go to the evolution of China’s fertility policy and its potential impacts. In the past few decades, China’s fertility policy of only one child has led to a large number of 1-child families. The overall characteristics of family structure are small size, simple composition and close relationships. However, there also seem to be many social problems relating to 1-child families. For example, 1-child families may face a heavy burden looking after older members, there may be a lack of important family relationships (no brothers or sisters), and the social problems related to aging trend may be more serious. Since 1960, China’s total fertility rate has fallen from a high level to a low level compared to other countries and regions, and it has even been lower than Europe and Central Asia after 2000 (Table 4.3). It is commonly agreed that the fertility policy for family planning and birth control has played a powerful role (Table 4.4). In recent years, the implementation of the selective 2-child policy in December 2013 and the later universal 2-child policy in October 2015 both influenced the family Table 4.3 Fertility rates in China and other areas, from 1960 to 2017 Countries/areas

1960

1970

1980

1990

2000

2010

2017

Sub-Saharan Africa

6.64

6.75

6.77

6.33

5.77

5.25

4.78

Middle East and North Africa

6.88

6.71

6.23

4.92

3.20

2.87

2.73

Latin America and the Caribbean

5.92

5.31

4.22

3.25

2.62

2.19

2.04

South Asia

6.05

5.80

5.12

4.29

3.47

2.75

2.44

East Asia and the Pacific

5.39

5.22

2.99

2.50

1.79

1.80

1.80

Europe and Central Asia

2.84

2.56

2.17

1.96

1.56

1.73

1.75

China

5.75

5.65

2.63

2.35

1.50

1.59

1.63

Data source World Bank, https://data.worldbank.org.cn, updated on July 10, 2019

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Table 4.4 National gender ratio in China, from 1953 to 2020 Year

1953

1964

1982

1990

2000

2010

2020

Gender ratio

107.56

105.46

106.30

106.60

106.74

105.20

105.07

Data source National Bureau of Statistics in China, http://www.stats.gov.cn/tjsj/tjgb/rkpcgb/qgr kpcgb/202106/t20210628_1818824.html, visited on February 20, 2022

Fig. 4.15 Total number and proportion of children aged 0–14 in China. Data source China’s statistical yearbooks, published by the National Bureau of Statistics

structure of national population to some degree, as the statistical data show. Since 2013, China’s birth control has been gradually relaxed, and then the proportions of 4-person households and 5-person households rose slightly in the following years 2014 and 2015. At the same time, the proportions of 1-person households and 2person households fell slightly. The changes in fertility policy may affect family size by changing couples’ willingness to have more children. It is generally believed that China’s population growth and family structure are likely to experience changes in the near future due to ongoing adjustments in fertility policy, such as higher fertility rates, larger family sizes, more diverse family types, and more complicated family relationships. In the context of China’s fertility policy, the total population of children aged 0–14 years and the child dependency ratio have remained quite low since 2000 (Fig. 4.15). According to the existing literature, the child dependency ratio (Fig. 4.16, also called the child support coefficient) refers to the ratio of the number of children to the number of working-age people, which represents how many children need to be supported by every 100 working-age adults. Because of the proposal of the selective 2-child policy in 2013 and the universal 2-child policy in 2016, the total number of children aged 0–14 in China has rebounded slightly and represented an

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Fig. 4.16 Changes in the child dependency ratio and old age dependency ratio in China. Data source China’s statistical yearbooks, published by the National Bureau of Statistics

increase of 0.3 percentage points from 2014 to 2017. At the same time, the child dependency ratio showed a significant increase of nearly 1 percentage points after experiencing stagnation. According to the National Family Planning Commission, 2016 was the year that China’s birth population reached its peak level since 2000. There were up to 18.67 million newborns in 2016, with an increase of 11 percentage points over 2015. Some 45% of newborns were the second children in their families. In the future, implementation of the 2-child policy and the 3-child policy may have a more profound impact on China’s family structure. Another important issue related to the impacts of China’s fertility policy on population growth and family structure is population aging. According to the commonly used international standard, a population over the age of 60 of 10% of the total population, and a population over the age of 65 of 7% of the total population is a sign of a country entering an aging society. In general, it is believed that aging is one of the special stages in population growth. In fact, it is bound to occur when the population reaches a certain historical stage. It is also an inevitable result of changes such as decreasing birth and death rates, increasing average life expectancy, and population migration (Yang, 2020). In terms of China, the long-term implementation of the 1child fertility policy might have been responsible for the formation and aggravation of the population aging problems. Based on previous national censuses and nationwide sampling surveys on population, the proportion of people aged over 60 in China was 10.33% in 2000, exceeding 10% for the first time, resulting in the age structure of the population officially transforming from the previous the adult type to the older type. As the proportion of the older population has increased year by year, social problems due to population aging

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have gradually become prominent. Data from the Sixth National Census showed that the population aged over 60 was 177.66 million and accounted for 13.26% of the total population, which was nearly 6 percentage points higher than the 7.65% in 1982 (Yang, 2020). According to the newest data from the Seventh Census in 2020, the number of people over 60 years old in China had reached 264,018,766, accounting for 18.70% of the total population. The population aged 65 and above was 190,635,280, accounting for 13.5%.8 Compared with the results of the Sixth Census in 2010, the proportion of the population aged over 60 had increased by 5.44 percentage points, and that of the population aged over 65 had increased by 4.63 percentage points. It seems the trend of population aging and the declining fertility rate are both significant now. Some studies in China have predicted that by the middle of the 21st century, China’s population aging will be graver than developed countries such as the United States and Britain, as well as populous countries such as India and Russia (Liu, 2021). The aging population is becoming a critical demographic issue that cannot be ignored if high-quality and sustainable development in China is to continue, as well as enhanced competitiveness on the international stage. Among the factors affecting the population aging process, China’s fertility policy plays an important role (Yang, 2017). The policy of family planning has led to artificial intervention in the reproductive behaviour of couples at childbearing age by strictly limiting the number of children, and finally led to a sharp drop in the birth rate over a long period. The problem of population aging, which was not supposed to appear for several years, has emerged much earlier. There is evidence that China’s population aging is faster and shorter than that of developed countries due to its fertility policy. According to data released by the United Nations, the proportion of the population aged over 60 in China increased from 7.46 to 14.3% during the 3 decades from 1982 to 2012, reached 18.70% in 2020 and is likely to arrive at 19.3% in 2025. In developed countries such as Norway and Sweden, this increase will take about 100 years, while it will take less than 45 years in China (Yang, 2020). In recent years, the potential impact of the gradual liberalisation of the 2-child policy on the birth rate and the aging trend has received widespread attention. A study based on data from the National 1% Population Sample Survey in 2015 showed that the current adjustments in China’s fertility policy will certainly have significant impacts on population growth and population structure for a long time to come (Zhai et al., 2017). Specifically, the size and proportion of children will usher in an obvious small peak in the short term with the revised fertility policy, and then the fluctuations will slowly reduce. The size of the older population should reach to a new level around 2053 and may enter a period of negative growth thereafter. Prediction analysis shows that proportion of older people may represent a sharply upward trend in the first half of the 21st century, and then slowly increase along with fluctuations in the second half of the century. However, some studies argue that the revised fertility policy has not yet effectively increased the fertility rate. According to an empirical analysis based on macro-statistical data, the implementation of the universal 2-child policy 8

Data source: National Bureau of Statistics in China, http://www.stats.gov.cn/tjsj/tjgb/rkpcgb/qgr kpcgb/202106/t20210628_1818824.html, visited on February 20, 2022.

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in 2015 only led to a rebound in the natural population growth rate in 2016, while the national natural population growth rate began to decline in 2017 (Yang, 2017). Yang (2017) summarised that there may be five reasons for the effect of the revised implementation of the fertility policy (such as the 2-child policy and the 3-child policy) not being that significant. Firstly, the rapid advancement of industrialisation and urbanisation has challenged the concept of fertility developed from the previous farming society (X. Feng, 2021). At the same time, the mandatory family planning policy has prompted changes in the traditional concept of raising sons for old age (yang er fang lao), and modern fertility concepts such as having fewer and better births have gradually become popular (Shen & Wang, 2019). Secondly, the increasingly fierce competition in employment has forced women to delay childbearing or reduce the number of children to avoid an excessive impact on their occupational competitiveness. Thirdly, as the time and money involved in raising a child continue to rise, many employed couples are taking a wait-and-see attitude towards giving birth, and some would rather spend the time and money to improve their own lives (Gao, 2021). Fourthly, although relevant finance policy has provided support for the implementation of some aspects of the 2-child and 3-child policy, systematic and adequate supporting measures to reduce the cost of raising children are still missing. Fifthly, the previous fertility policy aimed at controlling the number of newborns may have not been adjusted in time in a few areas. If the remaining punitive measures are not completely removed, the positive effect of the newly revised 3-child policy will certainly be limited (Yang, 2017). To conclude, China’s fertility policy has gone through great changes and revisions in the past few years, which will certainly play an important role in various aspects of the future population growth and family structure. However, there seem many difficulties in changing people’s traditional mindsets and attitudes towards giving birth, which formed in the family planning era, within a short period. To cope with the ever-increasing aging problem caused by previously implemented family planning, relevant supporting measures and appropriate development strategies are also necessary in addition to continuously adjusting the national fertility policy based on population growth. For the future, it is reasonable to believe that the family structure of China’s population will be more diverse, more balanced and more sustainable under the long-term influence of a revised fertility policy.

4.2 Gender Structure As another important factor in population growth, gender structure also represents the basic characteristics of a country or region’s population composition. A sustainable gender structure and coordinated gender relationship are basic guarantees for building a fair, harmonious and sustainable society. On the one hand, gender structure presents some main characteristics of population in the past, through multiple indicators involving gender, such as gender-specific fertility rate and gender-specific death rate. On the other hand, gender structure plays an important role in predicting the

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future changing trend of population growth and its structure. Exploring the temporal and spatial characteristics of a country or a region’s gender structure is certain to make great contributions to social and economic development, especially in issues about the provision of facilities, the supply of public services and the planning of transport.

4.2.1 General Characteristics of Gender Structure 1) Total gender ratio is high but within the normal range The gender ratio of the total population is the most intuitive basic indicator representing the overall gender structure of a country or a region, which is measured by the ratio of the total male population to the total female population. According to demographic data in most areas of the world, a gender ratio between 96 and 106 is balanced. Countries with gender ratios within this range are more likely to have normal and sustainable gender structures, while those outside this range might have abnormal or unsustainable gender structures. The general formula for calculating the gender ratio is as follows: Gender ratio of the total population = (total male population/total female population) ∗ 100 Evidence has shown that the total gender ratio in China went through an overall reduction from 1953 to 2020. According to statistical results of the First Census, the national gender ratio was as high as 107.56 in 1982 and then remained between 106 and 107 until 2000. Since the start of the 21st century, the total gender ratio in China has dropped to within the normal range. The survey results in the Seventh Census in 2020 showed that the total number of males was 723,339,956, which accounts for 51.24% in the total population. The total number of females was 688,438,768, accounting for 48.76%.9 According to the previous census data, China’s gender ratio for the total population has remained at a high level for a long period. Since the reform and opening up in 1978, the proportion of males has always been higher than 51.00%. In fact, many studies have raised concerns about the relatively high gender ratio since the Third National Census data in 1982 was released. Most discussions focused on the imbalance of the gender ratio at birth. In traditional Chinese culture, backward productivity and the patriarchal tradition of surnaming a child after its father in Han has led to the popularisation of boy preference when giving birth (Liu & Feldman, 2021), which means that couples may prefer to have a boy rather than a girl when they are ready to have babies (Qiao, 2004). Aird and John (1990) believed that the 1-child policy in 9

Data source: National Bureau of Statistics in China, http://www.stats.gov.cn/tjsj/tjgb/rkpcgb/qgr kpcgb/202106/t20210628_1818824.html, visited on February 20, 2022.

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China might be a cause for the abandonment of female infants, leading to an increase in the gender ratio. Hull and Terence (1990) considered the application of induced abortion as one of the primary reasons for the high l gender ratio. In 1989, the gender ratio at birth reached 111.3, significantly higher than the normal level. In addition to the possible influence of statistical error, most Chinese scholars believe that selective abortion after learning the sex of the foetus may be the most important factor in this high sex ratio at birth (Qiao, 2004; Zeng et al., 1993). However, with rapid improvements in social productivity and the promotion of gender equity, the concept of gender preference (usually boy preference) appears to have become less popular in recent years. Two main reasons have been widely discussed. On the one hand, subjectively choosing the sex of the foetus has been forbidden much more strictly recently, which will certainly help to reduce the appearance of sex-selective abortions. On the other hand, the revision of China’s fertility policy has allowed couples to have two or three children, which may significantly reduce people’s gender preference when giving birth. A survey conducted among rural residents found that when only one child is allowed, couples show a very prominent gender preference for boys, with 72.16% of respondents reported preferring a boy. When two children are allowed, 94.79% of them showed no preference in terms of the babies’ gender (Mo, 2005). As a result, China’s gender ratio has reduced from a high level beyond the normal range to a relatively high but normal level within the range of 96–106, and it is likely to remain in an equilibrium status in the future along with the continuous optimisation of fertility policy and social welfare (Fig. 4.17). 2) Older age groups have lower gender ratios Gender ratio by age is based on the number of males and females in each subgroup of population divided by age. The level of gender ratio by age is mainly determined by

Fig. 4.17 Gender structure of population in China. Data source China’s statistical yearbooks, published by the National Bureau of Statistics

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the gender difference in the birth rate and death rate, as well as that in the migration rate, which represents the gender structure of different age groups. The formula for calculating the gender ratio by age is as follows: Gender ratioby age = (male population of a certain age group/female population of the same age group) ∗100

According to the national statistics in 2019, China’s gender ratio by age has a reducing trend with increasing age. All the gender ratios of all age subgroups under 20 years old were higher than 113, among which the gender ratios for groups aged 5–9, 10–14, and 15–19 were as high as 118, significantly out of the normal range. Among the age subgroups over 65, the gender ratios were all below 100, among which the gender ratios for groups aged 80–84 and 85–89 were below 80. The gender ratio of the 90–94 and 95-year-olds was less than 50 in 2019. According to the national statistical data for the past few years, the proportion of women in groups aged above 65 has exceeded that of men since 2013, which means that females have occupied a dominant position among senior people (Figs. 4.18, 4.19 and 4.20). According to the common views in demography, the main causes of gender imbalance are gender selection before birth and gender difference after birth. Before birth, fertility policies, cultural concepts and other social environments affect people’s willingness to reproduce, and they are often accompanied by certain preferences for male or female babies. Thus, there has been artificial interference in the gender of

Fig. 4.18 Gender structure of different age groups in 2018. Data source China’s statistical yearbook 2019, published by the National Bureau of Statistics

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Fig. 4.19 Gender ratios (male to female) of different age groups in 2018. Data source China’s statistical yearbook 2019, published by the National Bureau of Statistics

Fig. 4.20 Gender ratios (male to female) of older age groups. Data source China’s statistical yearbooks, published by the National Bureau of Statistics

newborn babies for the past few decades in China. In addition, the natural environment can affect the survival rates of male and female babies in different ways. After birth, different living environments may also lead to men and women having different survival opportunities. For example, the possibility of dying due to the influence of

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disease, war, and/or famine are often higher for men than for women. All these facts have made contributions to the significant gap in gender ratio among different age groups. Currently in China, the share of boys in infant age group is significantly higher than that of girls, while the share of women in the older population is significantly higher than that of men. Due to the significant imbalance in the gender structure among different age groups and the differences in living habits, psychological needs and travel patterns between older males and females, barrier-free transport facilities and public services should be provided with more consideration for the special needs of older female residents. 3) Men being dominant in the migrant population Against the background of rapid urbanisation in China, the migrant population has become a special group attracting social concerns. Migrants are people who live in their own township/county, while having their registered permanent residence in other township/county, and have been away from their registered residence for more than half a year. This meaning of migrant population is generally equivalent to the separated population in the broad sense, while in the narrow sense it only refers to the separated population but does not include the separated population in the same prefecture-level administrative unit. It is of vital necessity to pay attention to migrant population in China. On the one hand, the large-scale movement of the migrant population has an important impact on the socioeconomic development in the outflow and inflow areas. On the other hand, the daily needs and psychological status of the migrant population are vitally important for the sustainable and harmonious development of the inflow areas. Domestic studies in China have pointed out that the migrant population often faces challenges, including increasing unemployment, lack of social security, being disadvantaged in children’s education, and lack of social communication (Duan et al., 2013). As an important and unique component of the total population in China, the migrant population deserves special attention in discussions of the gender structure of the population. According to the statistical yearbooks published by the National Bureau of Statistics, the proportion of men in migrant population has been higher than that of women since 2013, ranging between 51.00 and 52.00%. The fact that men are dominant in China’s migrant population is likely due to the significant gender difference in labour division as well as individual preferences for working. Affected by traditional culture and family concepts in the context of China, the husband usually has more responsibility for earning money and supporting the life of his wife and children. It is often the male family members who move from their original residence to find better jobs and earn higher salaries in other cities or regions. Some of those migrant workers may move along with their wives and children, but there are a large number of migrant workers who just move to the inflow areas alone. As a result, women’s mobility often appears lower than men’s in terms of migration, leaving men dominant in China’s total migrant population (Fig. 4.21).

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Fig. 4.21 Gender structure of the migrant population. Data source Summary based on China’s statistical yearbooks, published by the National Bureau of Statistics

4) Women have higher illiteracy rates than men Based on China’s total gender structure and the gender structure of certain subgroups, the socioeconomic attributes of the male and female population need further discussion. There is evidence that the illiteracy rate of the female population in China is significantly higher than that of male population. According to the statistical yearbooks published by the National Bureau of Statistics, illiteracy rate refers to the proportion of illiterate population in population aged 15 above. Since 2013, the illiteracy rate of the female population in China has remained above 6.70%, while that of the male population has remained below 3.00% (Fig. 4.22). In recent years, both the female illiteracy rate and the male illiteracy rate have fallen slowly after reaching a peak in 2015, along with a significant and stable gender gap. The gender difference in terms of receiving education indicates that there are more illiterate people among the female population, who are often in disadvantaged situations for acquiring livelihood opportunities. Paying more attention to those groups’ daily needs and travel patterns should be a major focus while developing a people-oriented and sustainable transport system in China. The significant gap in the level of illiteracy rates between men and women indicates the gender inequality of educational opportunities in the context of China (especially in some areas). Generally, equality of educational opportunity means that anyone, regardless of race, colour, gender, language, religion, political background,

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Fig. 4.22 Illiteracy rates10 of the total population and the male, female population. Data source Summary based on China’s statistical yearbooks, published by the National Bureau of Statistics

family background or social status, should be treated equally while receiving education, and should not be restricted by any differentiation and specialisation. In many areas and regions, the issue of equal educational opportunities between men and women has always been the focus of social concern. In 1960, UNESCO solemnly declared in the Convention against Discrimination in Education that depriving women of opportunities for receiving education equally to men is a restriction on human rights and a serious violation of human dignity. In 1967, the United Nations General Assembly proposed the Convention on the Elimination of All Forms of Discrimination Against Women, calling on global society to eliminate the unfair treatment of women, such as gender discrimination in education. The 1990 World Education for All Conference issued the World Education for All Declaration, calling governments and international organisations to fight for three important goals: to promote universal primary education globally, to eradicate illiteracy, and to eliminate the inequality of educational opportunity between men and women. In September, the United Nations World Summit for Children adopted the World Declaration on the Survival, Protection and Development of Children, which emphasised that “universal improvement of women’s social status and protection of their equal rights will benefit the children all over the world,” and that “girls must be given equal treatment and opportunities from the very beginning of their life.” In 1995, Beijing held the Fourth World Conference on Women and proposed the

10

Here, the illiteracy rate refers to the proportion (%) of the illiterate population in the population aged 15 and over.

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Beijing Declaration, calling for the elimination of gender discrimination in education. In addition, girls’ and women’s education issues have been listed as among the 12 major areas of concern for women’s issues. With the rapid improvement of social and economic development, great achievements have been made in China’s education sector and the problem of unequal educational opportunities has largely been eased. However, in some underdeveloped areas, girls still have difficulty in acquiring educational opportunities and completing the 9-year compulsory education. According to the 2010 national census data, among the population who had dropped out during primary school, girls accounted for a significantly higher numbers than boys (Fig. 4.23), while the dropout population at higher education levels (such as those dropping out during junior high school or senior high school) was dominated by boys. The data show that girls’ termination of studying at school at the beginning of education was more common than boys’. Some girls even entered society without enrolling or receiving any formal education, becoming totally illiterate. In the context of China, there seem to be complicated reasons for the lack of gender equality of educational opportunity, such as the contempt for girls’ education in some areas, the feudal attitudes from their parents or the surrounding social environment, and the underdeveloped economy and disadvantaged or inadequate educational resources. In 1996, a randomly sampled survey took place in eight remote counties in western China (Kaijiang County in Sichuan Province, Tianyang County and Mashan County in Guangxi Province, Rongjiang County and Leishan County in Guizhou Province, Yanshan County, Yuanyang County and Zhenxiong County in Yunnan Province), and several main reasons emerged for girls dropping out of school in the context of western China, in which their parents’ attitudes towards education

Fig. 4.23 Gender structure of the dropout population (divided by the educational stage when they dropped out). Data source China’s 6th National Census Data in 2010, released by the National Bureau of Statistics

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played an important role. Firstly, some parents believe that attending school and receiving education are much more necessary for boys than girls. Secondly, some parents take the opinion that that if there is not much possibility of completing a decent education, it is better for girls to get married early. The third point of view is that the expectation for girls’ educational level is lower than that of boys. The fourth point of view is that boys will receive great priority to go to school if the income is not enough to cover the education fees for all the children. The fifth point of view is that girls may not support the family when it is getting old. Sixthly, some parents reportedly believed that girls can still make money without an educational qualification. In addition, it in more economically underdeveloped areas (such as rural areas), it is more common for people to underestimate the education of girls and to value that of boys (generally called preferring boys to girls). The gender inequality in people’s attitude towards education has resulted in girls being less likely to acquire equal educational opportunities than boys and leaving them at quite a disadvantage in terms of education. 5) Labour force participation rates are lower among women According to statistical data released by the World Bank, the labour force participation rates of men and women in China have shown a significant gap in recent years, with that of women being significantly lower. Taking the period from 2010 to 2018 as an example, the gender gap in the labour force participation rate has remained as large as about 15 percentage points. In 2010, 78% of the male working-age population was engaged in economic activities, while that rate of female participation was only 64%. In 2018, the rate of male participation dropped slightly to 76%, while the female labour force participation rate showed a slight downward trend and only 61% of the female working-age population was engaged in economic activities (Fig. 4.24). This suggests that women in China are participating less in employment, which might be the result of both economic and social factors. Among them, the social role performed by females is changing. These years, due to the reform of fertility policy, increasing pressure has been put on women to support a second or third child. As a result, some of them may have to leave their permanent jobs and finally leave the labour market to become full-time mothers. In the traditional model of Chinese families, women are the main undertakers of unpaid housework. Taking care of the growth of young children and the daily lives of the older members of the family usually places a higher load on women than on men, which leads to huge limitations on women participating in the labour market. In 2010, a survey on the social status of Chinese women focused on whether the respondents were then engaged in paid labour and the main reasons for not participating in labour. The results showed that most (75.9%) married women in urban areas under the age of 40 were engaged in labour with stable salaries. Among those who were not involved in the labour market, nearly 80% of them were too busy with housework. More specifically, 91.9% said that there are children at home who need full-time care. None of the unemployed men said their reason for not working was taking care of children (National Health and Family Planning Commission, 2016b). The results indicated that the number of children had a significantly larger impact on women’s

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Fig. 4.24 Labour force participation rates of the male, female population aged above 15. Data source World Bank, https://data.worldbank.org/indicator/SL.TLF.CACT.MA.ZS?locations=CN, visited on October 20, 2020

labour participation than on men in China, which is consistent with evidence from previous studies. With the gradual relaxation of birth control and the revision of China’s fertility policy, couples are allowed to have two or even three children. In families expecting to have a second child, women will certainly be put under greater pressure due to raising more children. In addition, the responsibility for supporting older people also has set up huge obstacles to women’s participation in labour market. Considering the great pressure of taking charge of housework faced by many women and their disadvantages in earning money, it is essential to give them more attention and more patience. On the one hand, companies and institutions could provide young women with more flexible jobs to allow them to acquire more employment opportunities while taking care of children. On the other hand, society as a whole should show more concern and respect for full-time mothers and recognise the great contributions they have made to families and communities. It is vital to encourage men to share the responsibility of raising children and supporting older people to promote a more equal social environment.

4.2.2 Spatial Characteristics of Gender Structure In terms of differences among provinces, the gender ratio was higher in the northwest and southern inland provinces (Guangxi, Yunnan, etc.) than in the northeast, coastal provinces (except Hainan Province) and Tibet in 2000. In 2010, the gender ratio of all regions in China had seen some decline, and the spatial distribution appeared a little

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Fig. 4.25 Gender ratio (male to female) in 2010 and 2018. Data source China’s statistical yearbooks, published by the National Bureau of Statistics

different from 2000. The gender ratio of each province showed an increasing pattern from the northeast and central coastal areas (except Beijing and Tianjin) to the inland and western areas. High gender ratios occurred mainly in Inner Mongolia, Qinghai Province, and the southern coastal provinces, especially Hainan. The highest gender ratio in China was 114.52 in Tianjin. In 2018, the gender ratio in China had dropped further in most provinces, but areas including Tianjin, Guangdong and Hainan still had quite high gender ratios of over 110.00 (Fig. 4.25). The regional differences in gender structure have complex social backgrounds. On the one hand, gender ratio is directly affected by the difference in birth rates and death rates between males and females. Since different areas have different characteristic birth rates and death rates, it is reasonable for gender ratio to be slightly different. On the other hand, gender ratio is also significantly affected by the mobility of the population. As the previous sections have shown, men often occupy the dominant positions of migrant workers and the gender ratio of the migrant population tends to be quite high, which influences the gender ratio of outflow areas and inflow areas. In China, more developed cities have a higher demographic pull and are more likely to attract large influxes of labour, most of whom are male workers. As a result, the gender ratio in those inflow areas often appears higher than that in outflow areas. Lastly, people’s attitudes towards giving birth and traditional concepts on gender preference seem to vary considerably across different regions and areas in China. According to the traditional attitudes towards giving birth influenced by Chinese cultures and customs, many families, especially those in rural areas, prefer to give birth early, more and male. Here, male means that many couples are more willing to give birth to boys than girls in some areas. The formation of this concept for giving birth has a complicated historical background. First of all, in traditional feudal society, the self-sufficient small-scale peasant economy makes the available labour a vital determinant of a family’s income level and life quality. Due to backward production tools and low productivity, the family’s economic status depends entirely on manual labour which requires a lot of physical effort. Compared with women, men tend to have significant advantages in agricultural production and can earn more money to support the family (Tang, 2014). Thus,

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Fig. 4.26 Gender ratio (male to female) in 2005 and 2015. Data source National 1% Population Sample Survey, released by the National Bureau of Statistics

parents are more willing to raise boys to improve the productivity of family agricultural production. Secondly, the small-scale peasant economy with the family as the basic unit has formed a system centred on paternity. Male members of a family can inherit the property and determine most of the important family affairs. The inequality of the social status of men and women strengthens people’s preference for giving birth to boys. In the past few decades, some people have believed that only having a boy could continue the family line, and some even regarded it as a point of principle (Li, 2013). In addition, filial piety has been emphasised in China’s traditional culture, which means that children should take the responsibility of supporting their older parents when they grow up. Under the influence of the patrilineal family system, this responsibility is mainly borne by the male offspring. As a result, society gradually formed a belief that only having sons could prevent people from becoming lonely without relatives to rely on, and this belief is usually called raising sons for old age (yang er fang lao). In some areas of China, passing on family property to sons, as well as sons being responsible for the end of older parents, has been a kind of self-evident phenomenon (Liu et al., 2015). Now, with the rapid development and modernisation of China’s society, people’s attitudes towards giving birth are becoming increasingly open, and the gender of their babies is regarded less important. However, it is undeniable that the traditional attitudes of preferring sons over daughters and raising sons for old age still exist in some areas, especially in remote rural areas. From the perspective of prefecture-level cities, the gender ratio in most such cities in China rose slightly from 2005 to 2015, to around 100.00 (Fig. 4.26). Meanwhile, the difference in gender ratio among regions seems to be more significant. The highest levels of gender ratio in China are mainly in Tianjin, Jiuquan, Hai Tibet and the Mongolian Autonomous Prefecture. The lowest levels are mainly in the Greater Xing’an Mountains in Heilongjiang Province, at less than 90. Since women and men tend to maintain quite different living habits and travel preferences, the spatial heterogeneity in the gender ratio may lead to huge regional differences in terms of transport demand and travel pattern. Therefore, while improving the transport system and adapting to population characteristics, it necessary to take gender structure into

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full consideration based on local conditions to conduct a comprehensive forecast of regional transport demand.

4.3 Education Structure In terms of the population growth of a country or a region, population quality also occupies a vital position in addition to the urban–rural structure, family structure and gender structure. In general, the concept of population quality represents the qualitative prescriptive nature of the population as a whole. Liu (1986) pointed out that, as the main body of social life, the population has many qualitative prescriptive properties. From different perspectives, there are different manifestations. In his point of view, the population quality in demography generally refers to the overall physical fitness, cultural quality, and ideological quality of the population, which can be applied as an indicator for the population’s overall understanding of the conditions and ability to transform the world. Meanwhile, different scholars have different definitions of population quality. Liang (1986) believed that population quality is the sum of various components of the population. Mu (1997) believed that population quality represents the various social functions and influences displayed by the structure and combination of the population under certain historical conditions. Populations of higher quality are generally more likely to maintain a balanced and sustainable structure. According to the National Education Development Research Centre, population quality is an important part of population characteristics, and the modernisation of population quality is the core context of population modernisation. Although there have been various interpretations of the definitions and concepts of population quality, previous demographic studies in China have generally agreed that population quality plays an important role in the development of population and society. In terms of the classification of population quality, there are generally two commonly used approaches in China, called dichotomy and trichotomy. According to traditional trichotomy, population quality can be divided into three aspects: physical fitness, cultural (educational) quality, and ideological quality. More specifically, physical fitness is regarded as the primary condition and vital basis for population quality, while cultural (educational) quality and ideological quality are the central content. Under the classification framework of the trichotomy, each type of quality has rich connotations. Zhang (1980) stated that the physical quality of the population refers to whether an individual has grown up healthily, whether his or her intelligence is intact and the situation of his physical strength, endurance, and agility, etc. The cultural (educational) quality of the population represents information about the population’s culture (educational) ability and achievements, including the knowledge, science and technology level, production experience, and professional skills. The ideological quality of the population refers to the status of people’s outlook on life, moral outlook, ideological qualities and traditional habits. Unlike the trichotomy, dichotomy holds that the ideological quality of population lacks a

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scientific method for measurement and that population quality only includes two main contents: physical fitness and cultural (educational) quality. Although previous studies have disputed the dichotomy and trichotomy accounts of population quality, there is no doubt that cultural quality, which is usually measured by people’s education level, has always been regarded as important. With the rapid development of China’s economy and society, the population’s cultural quality has received more attention for further modernisation. In recent years in China, the employment market has been increasingly competitive and the fierce pressure for finding a good job has led to education becoming widely regarded as a necessary key to better life. Therefore, this section describes an analysis of the quality structure of China’s population with a focus on the temporal and spatial characteristics of education structure.

4.3.1 Basic Characteristics 1) Total education level keeps improving For the total population in China, the school attendance rate of the 20-year-old group was 45.27% in 2015, nearly 10 percentage points higher than the 36.08% in 2010. Wang (2017) pointed out that this significant increase in age-specific school attendance rate beyond compulsory education was a signal of China’s great achievements in improving education. According to the national Statistical Yearbook of China in each year, the proportion of the population who did not go to school was 12.290% in 2000, but it dropped to 5.279% in 2017. At the same time, the share of population with no more than primary education dropped from 35.701 to 25.232%, while that having completed high-school education increased from 11.146% in 2000 to 17.554% in 2017. In addition, the share of the population with a bachelor’s, master’s or Ph.D. degree increased significantly from 3.611% in 2000 to 13.875% in 2017, indicating that most people can now receive a high level of education due to the rapid development in China (Figs. 4.27, 4.28, 4.29 and 4.30). Compared to other countries and regions across the world, the overall indicators for adults and youth in China based on the literacy rate11 are all at an upper-middle level and the illiteracy rate is quite low. In the 21st century, the overall literacy rate of China’s youth has basically reached 100%, which is on par with that of developed regions (Figs. 4.31 and 4.32). 11

According to the UNESCO Institute for Statistics, literacy refers to the ability to understand, read and write short essays about daily life, including understanding numbers, that is, the ability to perform simple arithmetic.

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Fig. 4.27 Proportions of total/males/females with primary education in the total population. Data source China’s statistical yearbooks, published by the National Bureau of Statistics

Fig. 4.28 Proportions of total/males/females with junior high education in the total population. Data source China’s statistical yearbooks, published by the National Bureau of Statistics

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Fig. 4.29 Proportions of total/males/females with senior high education in the total population. Data source China’s statistical yearbooks, published by the National Bureau of Statistics

Fig. 4.30 Proportions of total/males/females with college education in the total population. Data source China’s statistical yearbooks, published by the National Bureau of Statistics

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Fig. 4.31 Literacy rate of populations aged above 15 in different regions of the world. Data source World Bank, https://data.worldbank.org, visited on October 20, 2020

Fig. 4.32 Literacy rate of populations aged 15–24 in different regions of the world. Data source World Bank, https://data.worldbank.org, visited on October 20, 2020

2) The total illiteracy rate continues to reduce The illiteracy rate of the population over 15 years old is commonly used as a measurement of the popularisation of basic education. In recent decades, the illiteracy rate in China has been decreasing, along with differences across regions. The illiteracy rate in the northeast has remained at the lowest level in China, and it dropped to about 2% in 2018. The eastern region came in second, and it also dropped by 2 percentage points from 6 to 4%. The western region had a significantly higher illiteracy rate than other regions, but it has also dropped from 12 to 10%. In 2018, the provinces with the lowest illiteracy rates were Liaoning, Jilin, Shanxi and Guangdong. The illiteracy

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Fig. 4.33 Illiteracy rate of the total population in different provinces in 2018. Data source China’s statistical yearbook 2019, published by the National Bureau of Statistics

rates in Tibet, Qinghai and Guizhou were relatively higher, reaching up to 8% and above in a few remote areas (Fig. 4.33). Although there were still regional gaps in the illiteracy rate across different provinces and cities, due to the huge regional differences in natural conditions, economic development, social environment, as well as education resources, it deserves recognition that the total illiteracy rate in China has reduced significantly in most regions, and the promotion of basic education has made great achievements in the past few decades.

4.3.2 Gaps in Education 1) Regional gaps in educational opportunities still exist In terms of the highly educated population (those with a bachelor’s, master’s or Ph.D. degree), the share and its increase rate appeared to be the highest in the eastern region followed by the northeast region, while that in the central and western regions appeared much lower over many years. Until 2018, there were obvious gaps in the proportion of highly educated population across different regions. The proportions of highly educated population in Beijing, Tianjin and Shanghai were significantly higher than those in other provinces (Fig. 4.34). In terms of the illiteracy rate at prefecture-level city level, the statistical evidence showed significant spatial differences in education. There were higher illiteracy rates in Tibet and Yushu Tibetan Autonomous Prefecture, with the average illiteracy rate of Yushu Tibetan Autonomous Prefecture being as high as 72.28% in 2005. Gender gaps also existed in the illiteracy rate, with the female illiteracy rate reaching up to 83.96% and the male illiteracy rate at 60.13%. With the overall improvement in education

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Fig. 4.34 Proportions of highly educated population (with a college degree or above) in 2018. Data source China’s statistical yearbook 2019, published by the National Bureau of Statistics

across China in recent years, the illiteracy rate in Yushu has dropped significantly to 45.58%, but it remained higher than other prefecture-level cities. Areas with lower illiteracy rates are generally those with rapid economic development on the southeast coast, such as Beijing, Shanghai, Guangdong, Shenzhen and their surrounding cities. At the same time, there were huge regional differences in the proportions of highly educated people. In 2005, Beijing and Shanghai had the highest proportions of highly educated people, and the overall distribution pattern of that proportion could be summarised as slightly higher in the northeast and lower in the southwest. By 2015, many cities in China appeared to have a quite higher proportions of highly educated people, while that in some cities still remained at a low level. As a result, regional gaps in the proportions of highly educated population among continue to widen, and the range in China even have risen by nearly 10%. That significant regional imbalance in education level may lead to huge differences in regional transport demand in terms of travel mode, travel purpose and travel expectations (Figs. 4.35 and 4.36). 2) The gender gap in education is tending to shrink According to recent statistical data, there is still a huge difference between the educational achievements of the male and female population in China. On the whole, the proportion of women with no more than elementary education is higher than that of men, while the proportion of men with at least junior high school education appears higher than that of women, indicating that men are more likely to have completed their education in junior high school and continued to study further. Among the population with at least junior high school education, the gender structure seems to be most imbalanced for the group with high-school education, while the gender structure for the group with bachelor’s, master’s or Ph.D. degrees is relatively normal

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Fig. 4.35 Illiteracy rate in different prefecture-level cities in 2005 and 2015. Data source Calculated based on the National 1% Population Sample Survey, released by the National Bureau of Statistics

Fig. 4.36 Proportions of highly educated people (with a senior high degree or above) in different prefecture-level cities in 2005 and 2015. Data source Calculated based on the National 1% Population Sample Survey, released by the National Bureau of Statistics

and balanced. In terms of the illiteracy rate, the total illiteracy rate and the gender gap have significantly reduced since 2011. By 2017, the gender gap in the illiteracy rate had dropped to around 5 percentage points, and the spatial patterns of men’s illiteracy rate and women’s illiteracy rate across prefecture-level cities were quite similar. To our pleasure, the gender gaps in all of the education groups appear to have shrunk continuously in recent years, indicating that more women can get the same access to educational opportunities as men (Fig. 4.37).

4.4 Conclusion

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Fig. 4.37 Illiteracy rates of the male/female population and the gender gap (male minus female). Data source China’s statistical yearbooks, published by the National Bureau of Statistics

4.4 Conclusion Based on the current status of China’s society and economy as well as its population growth, this chapter has provided a comprehensive look at the temporal and spatial characteristics of population structure by focusing on family structure, gender structure and quality (education) structure of the population. National statistical yearbooks, local statistical yearbooks, and multiple sources of survey data have been applied for the analysis and discussions. The findings of this chapter can help readers to understand China’s demographic issues in relation to family, gender and education structure better, as well as providing empirical evidence for building a high-quality, people-oriented and sustainable transport system that adapts to the changing transport demand caused by the changing population structure. In recent decades, China’s fertility policy has gone through great changes and optimisation, and the total population and the family structure have also undergone a series of important transformations. On the whole, the scale of families in China has tended to become smaller. With the nuclear family dominating and migrant family, single-elder and other unique types of family existing at the same time, the family types in China have become much more diverse than before. For many reasons, including the newly revised fertility policy and people’s changing attitudes towards giving birth, the total number of children and the child support ratio have both increased. In addition, indicators of family structure such as family size and family type have shown significant spatial differences, such as the differences between urban and rural areas, and those between different regions of China. Since family is the

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basic unit of people’s daily lives, the characteristics and changes in family attributes are likely to have great influence on the family members’ travel behaviour. Learning about a country’s population growth and its structure from a perspective of family attributes is essential for analysing the relationship between population growth and transport demand, as well as for providing an empirical basis to promote refined and customised transport services. In terms of gender structure, the results have shown that the total gender ratio of the population in China remains at a high level in recent years, along with significant spatial imbalance. For different groups of people, the proportion of women in the older group is higher than that of men, and the proportion of men in the migration population is higher than that of women. For the female population, the illiteracy rate appears to be higher, while the labour force participation rate remains relatively lower than that of the male population, which is consistent with the significant gender difference in the social division of labour emphasised by previous studies. In terms of the quality (education) structure of China’s population, the average education level has been rising and the illiteracy rate is gradually reducing. With great efforts taken by China’s society for gender equality, the gender gap in educational opportunities has greatly narrowed. Meanwhile, a regional imbalance in education still exists for complicated reasons such as spatially uneven natural conditions, economic development, the social environment, and educational resources. In this context, it is necessary to give more care to vulnerable groups based on the temporal and spatial characteristics of the population’s gender and education structure. Due to the differences in the living habits and travel demand among different groups of people, investigating and discussing the current characteristics and changing trends of population structure provides important empirical evidence for the development of people-oriented and sustainable comprehensive transport systems in developing countries such as China. In the following chapters, this book moves forward to explore the relationship between population structure and transport from the perspectives of transport service level and people’s travel behaviour based on the temporal and spatial characteristics of China’s population structure.

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Wang, Y. (2017). Zhong guo jia ting jie gou bian dong yu te zheng. [Changes and characteristics of family structure in China]. Population and Family Planning, 9, 32–34. Wang, Y. (2019). Analysis on the changes of China’s rural family structure since reform and opening. Social Science Research (4), 95–104. Yang, C. (2020). Xin zhong guo ren kou sheng yu zhi du bian qian. Southwestern University of Finance and Economics Press. Yang, L. (2017). Ying dui ren kou lao ling hua tiao zhan de sheng yu zheng ce yan jiu. [Research on fertility policy to cope with the challenge of population aging]. Social Security Studies, 78(5), 39–48. Zeng, Y., Gu, B., Tu, P., Xu, Y., Li, B., & Li, Y. (1993). Wo guo jin nian lai chu sheng xing bie bi sheng gao yuan yin ji qi hou guo fen xi. [Analysis on the reasons and consequences of China’s rising sex ratio at birth in recent years]. Population & Economics, 1, 3–15. Zhai, Z., Chen, J., & Li, L. (2017). 2015–2100 nian zhong guo ren kou yu lao ling hua bian dong qu shi. [Future trends of China’s population and aging: 2015–2100]. Population Research, 41(4), 60–71. Zhang, H. (1980). Shi lun wo guo ren kou fa zhan zhong de shu liang yu zhi liang wen ti. [Discussion on the quantity and quality problems of Chinese population development]. Academic Journal of Zhongzhou, 2, 46–52. Zhou, F. (2016). Wo guo jia ting jie gou bian qian yan jiu. Economic Management Press. Zhou, Y. (2018). China’s demographic changes and socio-economic development (1980–2016). China Population Publishing House.

Chapter 5

Population-Based Service Level and the Accessibility of Transport

5.1 Spatial Match Between Population and Transport 5.1.1 Railway Transport Service Level Evaluation A visualised analysis on the distribution of population and the spatial service range of railway services in 2015 showed that there was still a spatial mismatch between the coverage of railway station services and population agglomerations in some areas. In agglomerations where population density appeared higher, such as the Beijing– Tianjin–Hebei region, Chengdu–Chongqing region, Shandong, Henan, Hunan and Hubei provinces, there were still some areas that were not covered by direct railway services according to the spatial analysis. Meanwhile, some population agglomerations in Northeast China, Central China and Northwest China had basically been covered by direct railway station services (Fig. 5.1). The spatial distribution of the population and the service quality of railway services in 2010 indicated that regions with less advanced railway services were primarily in Xinjiang, Tibet, Northeast China, Yunnan and other regions, and that no railway services were offered in some distant areas. Only a small number of people lived within the scope of direct railway services in a small minority of prefecture-level cities in East China. Compared with the analysis results for 2010, the visualised analysis of the spatial distribution of population and the service range of railway services in 2015 indicated that the spatial characteristics of service quality basically remained unchanged (Fig. 5.2). Still, regions with lower levels of railway services were mainly in Xinjiang, Tibet, Northeast China, Yunnan and other regions, in which no railway services were offered in some distant areas. At the same time, in a small minority of prefectures in East China, only a small number of people lived within the scope of direct railway services. Meanwhile, in the majority of prefecture-level cities with higher population densities, the proportion of people located within the service range of direct railway services was relatively lower. Compared with some © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Zhao and D. Yuan, Population Growth and Sustainable Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-7470-0_5

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Fig. 5.1 Distribution of population and spatial service range of railway services in 2015. Data source Peking University’s Geographic Data Platform

Fig. 5.2 Railway service level evaluation based on the spatial distribution of population in 2015. Data source Peking University’s Geographic Data Platform

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densely populated regions, some prefecture-level units with lower population densities, such as those in Northwest, Northeast and Central China, seemed to enjoy more adequate services. Such results demonstrate that railway services still need to take better account of the spatial distribution characteristics of population and transport demand across different regions in the future high-quality development. A comparison of the spatial distribution of population and the quality of railway services between 2010 and 2015 indicated that the improvement or degradation of the service quality did not represent any significant or continuous characteristic in terms of spatial distribution. In East China, some cities have experienced improvement while others have experienced degradation in the quality of service from 2010 to 2015. On possible reasons, the improvement could be attributed to the increase in the service population caused by infrastructure construction, while the degradation may result from the population size within the scope of direct services growing at a slower pace than that within the boundaries of prefecture-level administrative units, which requires the service range of transport services to be expanded further. In contrast, in most prefecture-level cities in Northeast China, Northwest China and Central China, there was a general degradation in the quality of services. The different level and the changing characteristics of transport services’ quality in recent years require a different focus to improve the national transport system further among different regions and areas. From an international perspective, the punctuality rate of China’s railways has been relatively high compared with other countries and areas. It is almost at the same level as that of Japan, and it appears to be significantly higher than that of many developed countries such as the United States and Germany. The railway punctuality rate is a measure of the efficiency and quality of railway transport services. Countries with strong transport power usually maintain high railway punctuality rates. In general, high punctuality cannot be maintained without a sound transport infrastructure and efficient operation management. For example, Japan has been actively using Shinkansen technology to maintain a high on-time performance for decades. In the case of China, the national comprehensive transport system has been optimised, modernised and computerised by the Strategy for a Powerful Country in Transport in recent years, and the capacity and quality of transport services have been greatly improved. An online survey on the quality of railway passenger services conducted by the State Railway Administration in 2021 collected a total of 398,471 questionnaires. The survey results showed that 93.1% of the passengers were satisfied with the ticketing service. Up to 92.8% of the passengers thought it was convenient to get in and out of the station, 92.7% of the passengers were satisfied with the service of the staff, and 93.7% of the passengers believed that the train was basically on time. In addition, 62.0% of the passengers believed that the station is convenient enough to transfer to other travel modes. This clearly shows that the quality of China’s railway service has achieved remarkable improvement.

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5.1.2 Expressway Transport Service Level Evaluation The distribution of the population and the spatial coverage of expressway services in 2010 led to a spatial mismatch between the coverage of expressway services and population agglomerations in some areas. In population agglomerations such as the Beijing–Tianjin–Hebei region, Chengdu–Chongqing region, Shandong, Henan, Hunan and Hubei provinces, some areas were not covered by directly provided expressway services. Meanwhile, some areas with much lower population density in Northeast China, Central China and Northwest China were also not covered by direct expressway services. It seems that a few areas still need to improve the quantity and quality of expressway transport services. The visualised results in 2015 (Fig. 5.3) indicated that the spatial mismatch between the coverage of expressway services and population agglomerations still existed in some areas, but that the mismatch had been mitigated overall. In population agglomerations such as the Beijing–Tianjin–Hebei region, Chengdu–Chongqing region, Shandong, Henan, Hunan and Hubei provinces, there were only a small number of areas not covered by directly provided expressway services. Meanwhile, some areas with much lower population densities in Northeast, Central, and Northwest China were basically covered by direct expressway services. It seems that interregional equality has been greatly enhanced in terms of the quantity and quality of expressway transport services in recent years. In terms of the relationship between the spatial distribution of population and the quality of railway services, spatial analysis in 2010 revealed that regions

Fig. 5.3 Distribution of population and spatial service range of expressway services in 2015. Data source Peking University’s Geographic Data Platform

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Fig. 5.4 Expressway service level evaluation based on the spatial distribution of population in 2015. Data source Peking University’s Geographic Data Platform

with less advanced expressway services were mostly in Inner Mongolia, Northeast China, Yunnan, Sichuan and other regions with poor natural conditions, where no expressway services could easily be accessed in some distant areas, as well as some prefectures in East China where probably only a small number of people lived within the scope of direct expressway services. According to the visualised results of the spatial distribution of population and the quality of expressway services in 2015 (Fig. 5.4), the spatial structure of expressway service quality has remained quite similar in recent years. Regions with lower quality or less advanced expressway services were still mainly in Tibet, Inner Mongolia, Northeast China, Yunnan, Sichuan and some areas with poor natural conditions where the cost in human and financial resources of building more expressway services would be quite high. At the same time, in East China there are also some prefecture-level cities with quite low quality expressway services, indicating that probably only a small number of people lived within the direct service range of expressways. In addition, compared with densely populated regions in the southeast coastal areas, some prefectures with much lower population densities, such as those in Northwest, Northeast and Central China, enjoyed higher levels of expressway services. From the spatial perspective, such a distribution pattern of expressway service quality still represents a mismatch with the nationwide population. To build a high-quality and comprehensive transport system, the future development of expressway services will need to give the temporal and spatial characteristics of the population more consideration. For example, in areas with high density but without adequate quality services, an appropriate increase

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in provision and improvement in the service quality should be the main focus for optimising expressway services. There have also been great achievements in some areas in terms of improving the quality of expressway services from 2010 to 2015, with more areas able to get easy access to advanced expressway services. A comparison of the spatial distribution of population and the quality of expressway services between 2010 and 2015 indicates that there were both improvements and declines of the quality of services in eastern areas of China. The improvements might be attributable to the increase in the service capacity caused by infrastructure construction, while the declines might result from the population growth within the scope of direct services being slower than that within the boundaries of prefecture-level administrative units, which requires a further expansion in the service range of transport services. Meanwhile, in most prefecture-level cities in Northeast China, Northwest China and Central China, there was a general decline in the quality of services, which might have links with the outflow of permanent residents from areas with direct services. In addition, the absolute value of changes in service quality was mostly less than 1%, indicating that the changes were subtle in recent years.

5.1.3 High-Speed Rail Transport Service Level Evaluation The evaluation for the service quality of high-speed railway services used a slightly different method. Since high-speed railways are of a higher grade than normal railways and often involve multiple transfers, the services are usually not designed to meet the demand for door-to-door trips, such as the transport services provided through expressways and national highways. Instead, the main advantage of highspeed railways is connecting regional core cities or hub cities. Therefore, it is better to measure the quality of high-speed railway services based on provincial-level administrative units rather than prefecture-level units. To be specific, the service quality of high-speed railway services is measured by calculating the proportion of the total population in prefecture-level cities with high-speed railway stations within each provincial-level administrative unit in the total population of that province. By comparing the service quality of high-speed railway services in each provincial-level administrative unit in 2010 and 2016, we conducted an analysis to explore the development rules and regional differences across China in terms of high-speed railway services. There have been some important changes in the construction sequence of highspeed railway stations. In the early stage of building the high-speed railway system, stations were mainly distributed in East China, based on the Beijing– Kowloon Railway, the Beijing–Guangzhou Railway, the Beijing–Shanghai Railway, the Harbin–Dalian Railway and other north–south arterial railways. Then, the Hangzhou–Shenzhen Railway, the Lanzhou–Lianyungang Railway, the Lanzhou– Xinjiang Railway, railways along the Yangtze River and other east–west arterial railways were expanded, strengthening the construction and improvement of the rapid

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Fig. 5.5 China’s high-speed railway network and the increase in stations from 2010 to 2016. Data source Peking University’s Geographic Data Platform

transport network in East, Central and West China, as well as enhancing the connection between various regions. Before 2016, the spatial distribution characteristics of China’s high-speed railway network and high-speed railway stations appeared to be consistent with the Hu Huanyong Line pattern of population distribution, and the overall pattern was dense in the eastern regions and sparse in the western regions (Fig. 5.5). Based on the spatial distribution of the population and high-speed railway stations in 2016, the service quality of high-speed railway services was calculated, and it is shown in Fig. 5.6. Compared with the results for 2010, the service quality of highspeed railway services has significantly improved nationwide in recent years. Areas such as Guangdong, Fujian and Hunan Province have witnessed rapid improvement in the quality of high-speed railway services. Western provinces have constructed a large number of newly built high-speed railways. However, the spatial pattern of service quality has remained very similar to that in previous years. In 2010, the service quality of high-speed railway services appeared to be highest in the eastern coastal areas, and it gradually decreased westward. The provincial-level administrative units with the highest service quality included Zhejiang, Hainan, Beijing, Tianjin, Hebei, Liaoning, Jilin, Heilongjiang, Jiangsu, Anhui and Jiangxi. Other provincial-level administrative units had relatively lower levels of high-speed railway service, requiring further adjustment to the services provided by high-speed railway according to the actual demand determined by population characteristics. In 2016, the eastern coastal areas still seemed to have more advanced high-speed railway services and the service quality gradually decreased westward (Fig. 5.6). The provincial-level administrative

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Fig. 5.6 High-speed railway service level evaluation based on the spatial distribution of population in 2016. Data source Peking University’s Geographic Data Platform

units with the highest service levels included Zhejiang, Hainan, Beijing, Tianjin, Hebei, Liaoning, Jilin, Heilongjiang, Jiangsu, Anhui and Jiangxi. It seems that there were still great regional gaps among different areas, especially between the western region and the eastern region, as the quality of high-speed railway services in underdeveloped areas of West China were still at a relatively low level. Such continuous and significant regional differences require further optimisation of the high-speed railway services, taking the spatial pattern of population into more consideration.

5.1.4 Air Transport Service Level Evaluation The spatial pattern of airports and population in China (Fig. 5.7), indicates that there has been a well-matched relationship between airport services and population distribution in China in recent years. The distribution of China’s airports seems dense in the east and sparse in the west, with the Hu Huanyong Line from Tengchong in Heilongjiang province to Aihui in Yunnan province as the dividing line. The high density of population in Southeastern China has brought about a significant travel demand and more requirements for advanced transport services for long-distance and cross-regional trips. Matched with this, the construction of airports has been mainly focused in Southeastern China, especially in coastal areas, such as Shanghai, Zhejiang, Jiangsu, Tianjin and Guangdong provinces. Airports with annual capacities

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Fig. 5.7 Spatial pattern of airports in China in 2016. Data source Peking University’s Geographic Data Platform

in the tens of millions1 were mostly built in areas southeast of the Hu Huanyong Line. In areas northwest of Hu Huanyong Line, airports appeared to be fewer, where the population density is relatively small and mostly scattered in clusters. At the same time, airports were mostly built in areas of population concentration, with only three airports in total with annual capacities in the tens of millions. From 2010 to 2016, the spatial pattern of airport services in China basically remained the same, with the coverage of airport services experiencing a slight increase in the northwest of China. From a nationwide perspective, the spatial distribution pattern of airports in China has been relatively balanced in recent years, based on the spatial pattern of the population. At the same time, construction has gradually shifted to the central, western and northeastern parts of the country with the great achievements and continuous effects of the development strategy giving more priority to Western China. In addition to the improvement and optimisation of the spatial pattern, the total number of airports has been increasing quite rapidly. Since the 12th Five-Year Plan, the number of airports has risen from 178 in 2011 to 238 in 2017, including 229 licensed airports,2 representing an increase of 11 over the previous year. What is more, for international comparison, the punctuality rate of airports in mainland China is among the highest in the world. The airport punctuality rate is 1

In general, an airport with an annual capacity in the tens of millions refers to an airport with a cumulative passenger throughput of more than or equal to 10 million in one calendar year. 2 In general, a licensed airport refers to an airport with a Civil Aviation General Airport Licence issued by China’s Civil Aviation Authority to prove that all aspects of the airport have the met necessary standards and it is allowed to operate.

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the ratio of the number of flights whose actual departure time is consistent with the planned departure time to the number of all flights while the air passenger transport department executing its transport plan, and it is often used to evaluate the general quality of airport services. According to the Global Airport & Airline Punctuality Report 2019 released by an app for checking flight information called Fei Chang Zhun, the actual number of departures from airports in mainland China reached 4,802,300 in 2019, with a departure punctuality rate of 75.57% and an average delay time of 28.11 min on departure. This places China in fifth place in the global departure punctuality ranking, a little higher than that of some developed countries in Europe and America. According to the statistical data, although there is a gap between China’s airport punctuality and Brazil, Japan and other countries, with the continuous modernisation of transport system, China’s air transport services have achieved remarkable results in recent years, and air travel is increasingly becoming a convenient and efficient way for people to choose for intercity and cross-regional long-distance travel.

5.2 Population-Based Transport Accessibility in China Regional accessibility is an important factor affecting the social and economic development of a region. It is measured by the average value of transport cost (time and monetary cost) from one node to every other node in a transport network. This method for measurement takes the relationships between different nodes into account, and it is convenient for calculation and interpretation at the same time. The calculation process can be represented by the following formula: n Ai =

j=1 Ti j

n

Here, Ai represents the accessibility of node i (average value of transport cost), and T ij represents the transport costs (generally measured by travel time) from node i to economic centre j using the route with the shortest travel time. This calculation method can plainly display the degree of accessibility of one node to every other node within a transport network. However, it fails to account for the differences in importance between various transport relationships since no consideration is given to the different scale of nodes. As opposed to the average value of transport cost, the weighted average value of transport cost is currently the most important and commonly used method to characterise regional accessibility. This index focuses on characterising the accessibility of a city or a region for generating transport links with other cities or regions on a large spatial scale. The formula can be represented as follows:

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n Ai =

j=1 (T i j ∗ M j ) n j=1 M j

Here, Ai represents the accessibility of node i, T ij represents the shortest travel time from node i to economic centre j, and M j represents the basic characteristics of economic centre j, the destination, which can be characterised by GDP, the number of jobs, or the number of permanent residents. This method has been applied to the assessment of trans-European highways (Gutiérrez Javier et al., 1966; Javier, 2001) and the regional effects of railway networks. Calculations of the differences in travel time before and after the construction of new transport infrastructure can indicate the changes in accessibility level. Compared with the average value of transport cost, this index includes a weighting factor that can more accurately measure the reduction of transport costs caused by the construction of transport infrastructure as well as effectively evaluating the socioeconomic benefits. Taking the extremely uneven spatial distribution of population in China into full consideration, the same type of travel mode may cause quite different degrees of improvement in the regional accessibility between densely populated regions and sparsely populated regions. Using population as a weight factor is generally regarded as an effective way of evaluating the impacts of transport infrastructure on regional accessibility. Therefore, we adopted the population-based weighted average value of transport costs to measure the impacts of different travel modes on regional accessibility, with prefecture-level administrative units as the basic units of analysis. The two main steps of calculating can be represented as follows. Firstly, the weighted average travel time of each 1 km × 1 km grid within the territory of China was calculated and its spatial distribution was observed in the context of a comprehensive transport network. This process was mainly based on the ArcGIS software. Considering the variety of travelling speed and efficiency of different types of transport networks, different values were assigned for them separately. For example, high-speed railways were set as 250 km/h, regular railways were set as 140 km/h, expressways were set as 90 km/h and other highways were set as 50 km/h. The shortest travel time between different cities based on existing road networks was calculated, and finally we could obtain the average value of the shortest travel time from one city to all the other cities. To be more specific, the shortest travel time between different cities based on grid data was calculated from one grid to a desired grid (or a grid set) using the shortest path method, which could be called a cost-weighted grid algorithm. The key to this method is that it abstracts raster data and makes it into a graphic for calculation, which has been applied to many previous studies on accessibility (Lu & Jie, 2008; Zhang & Yuqi, 2006). When applied to the calculation of accessibility, this method significantly improves the precision of areal spatial data in faceted form. For example, the process of calculation consists of five main steps, supposing there are N cities in total to be analysed after completing preliminary data preparation. In the first step, we use the ArcGIS data module to rasterise and overlay features to generate a basic cost raster, and we use the network analysis module topologically to

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calculate the cities’ information about routes to determine their spatial relationships, establish logical relationships and build network datasets. In the second step, we use the cost-weighted approach to calculate the accessibility of the origin, generate the accessibility map (M 0 [A]), and obtain the shortest time from the origin to N cities (TOi (i = 1 … N)). In the third step, we use the origin–destination (OD) method in vector network analysis to calculate the shortest time from each city to all the other N − 1 cities (TSij (i, j = 1 … N)). Then we add TSij and TOi together, compare the time between different cities, and finally select the minimum value (C min (j) = TSij + TOi ). In the fourth step, we calculate the transport cost of each city, generate the accessibility map (M i [A]), and add together M i [A] and C min (j) from the network analysis (M i ’[A] = M i [A] + C min (j)). In the fifth step, we combine Mi ’[A] and M0 [A], select the minimum value, and finally obtain the accessibility cost raster of each city. Secondly, the average travel time of each 1 km × 1 km grid within the territory of China and the population were multiplied together to obtain the total travel time for the 1 km × 1 km grid. Taking the prefecture-level cities as the spatial analysis units, we summed the total travel time and population of the grids within the administrative unit. Through dividing the total travel time by the total population, we could obtain the average travel time within the prefecture-level city, which represents the level of regional accessibility. To conclude, we adopted the weighted average value of transport costs to measure the impacts of different travel modes on regional accessibility, with the population of each grid taken as the weight. The average travel time of a prefecture-level unit was obtained by dividing the total travel time by the total population, namely transport accessibility. Based on the results, we analysed the spatial pattern of China’s regional accessibility. Figure 5.8 shows the weighted average travel time based on population in 2015, which represents the regional accessibility of each prefecture-level city. The smaller the value, the shorter the average travel time from the city to other prefecture-level cities across China, and the higher the regional accessibility. The results show that the cities with relatively higher regional accessibility in China are mainly in the eastern coastal areas, as well as some provinces in Central and West China. Those areas include Liaoning, Jilin, Beijing, Tianjin, Hebei, Shandong and Shanxi in Northeast China; Henan, Hubei, Hunan and Anhui in Central China; southern Shaanxi and parts of Sichuan in Northwest China; Shanghai, Jiangsu and Zhejiang in East China; and Fujian, Guangdong and Guangxi in South China. Cities with relatively lower regional accessibility were mainly distributed in western provinces or around the border areas such as Inner Mongolia, Xinjiang, Tibet, Qinghai, Yunnan, Chongqing, Hainan and Heilongjiang provinces. To illustrate the impacts made by the construction of comprehensive transport system on regional accessibility from 2010 to 2015 further, we subtracted the average travel time of all prefectures in 2015 from the average travel time in 2010 to explore the changes in regional accessibility over these years. The difference could represent the improvement of regional accessibility in these prefecture-level cities, on the basis of which the actual effect of building the comprehensive transport system in

5.2 Population-Based Transport Accessibility in China

159

Fig. 5.8 Population-based weighted average travel time for prefecture-level cities in China in 2015

China could be then evaluated. As Fig. 5.9 shows, from 2010 to 2015 the prefecturelevel cities with larger improvements in regional accessibility were mainly those with poor original regional accessibility, chiefly located in less developed provinces of West China, Central China, border areas in Northeast China, such as Xinjiang, Inner Mongolia and Tibet, and some other provinces in southwestern regions such as Chongqing, Guizhou and Yunnan. According to the statistical results, the weighted average travel time of prefecture-level cities in China has reduced by about 44.3 min on average, with the rate of reduction being up to 5.95%. At the national level, the regional accessibility in China has greatly improved. Based on the spatial pattern of regional accessibility, we further analysed the effects of the construction of China’s comprehensive transport system. Using the Gini coefficient, we made an overall analysis of the average travel time of all prefectures in China, except Hong Kong, Macau and Taiwan, to evaluate the spatial pattern and regional characteristics of China’s accessibility. The Gini coefficient, also called the Gini index, is a common indicator used internationally to measure the income gap of residents in a country or region. Italian statistician and sociologist Corrado Gini developed the Gini coefficient in 1912. In this section, the standardised coefficient of the proportion of the total travel time of each prefecture in the total travel time of the entire country, instead of residents’ income, is taken as the basic indicator to evaluate the imbalance in the spatial distribution of travel time (time cost for trips) on the national scale. Figure 5.10 represents the calculation method for the Gini coefficient based on the Lorenz curve, which is commonly used as a measure of the equality of distributions. Let A represent the area between the actual line depicting income distribution and the line depicting

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Fig. 5.9 Changes in the population-based weighted average travel time of prefecture-level cities in China from 2010 to 2015

1 Lorenz curve

Proportion of income

0.9

Line of equality

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0

0.2

0.4

0.6

0.8

1

Proportion of population Fig. 5.10 The Lorenz curve and the calculation method for the Gini coefficient

perfect equality of income distribution, and B represent the area under the actual line depicting income distribution. Then the quotient of A divided by the sum of A and B represents the degree of inequality. This value is called the Gini coefficient or the Lorenz coefficient. If A is zero, then the Gini coefficient will be zero, indicating that income is completely equal. If B is zero, then the coefficient will be 1, indicating that

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income is absolutely unequal. The more equal the income, the straighter the Lorenz curve and the lower the Gini coefficient. The more unequal the income distribution, the higher the Gini coefficient. The Gini coefficient of the population-based weighted average travel time of prefecture-level cities in China in 2010 was 0.3958, and it slightly declined to 0.3951 in 2015. This indicates that the distribution of regional accessibility on the national scale became more balanced in these years, but the magnitude of optimisation was not that significant. A comparison of the Lorenz curves of regional accessibility in 2010 and 2015 reveals that the two curves overlap, which once again proves that the distribution of regional accessibility was only optimised to a limited extent on the national scale. On the whole, regional differences in accessibility have persisted, and only small changes have taken place in the past few years. However, many regions in China have undergone quite significant changes in their regional accessibility. The accessibility rankings of major cities in China have shown that the rankings of cities in eastern coastal and northeastern regions dropped, while the rankings of major cities in central and western regions rose slightly. More specifically, in East China, the rankings of most major cities including Beijing, Shanghai, Tianjin, Guangzhou, Shenyang, Qingdao and Shijiazhuang all dropped to lower positions. Only the rankings of a few cities rose to higher positions, such as Shenzhen, Fuzhou, Nanjing and Hangzhou. In Central China, the rankings of some cities rose, including Wuhan, Hefei, Zhengzhou and other capital cities, and the rankings of others dropped, including Taiyuan, Changsha and other capital cities. For West China, most of the major cities had a marked rise in their ranking positions or at least remained unchanged, including Guiyang, Xi’an, Lanzhou, Urumqi and other capital cities (Table 5.1). From the perspective of regional fairness, the regional accessibility of Northeast and Northwest China was still relatively low. In terms of the increase in accessibility, the absolute amount of improvement in accessibility in Central China was similar to that in West China. The absolute value of shortened average travel time in Northeast China was similar to that in East China, lower than in both Central and West China. West China recorded the fastest increase in the proportion of shortened average travel time, and the growth rates of Central and East China were similar to each other. Northeast China lagged behind many other regions when it came to accessibility improvement. At the same time, although West China is characterised by poor accessibility, its accessibility has been rapidly improved with the large-scale construction of transport infrastructure, based on China’s development strategy which gives more priority to western areas (Table 5.2). Generally, promoting the construction of transport infrastructure is regarded as an effective path, as it makes great contributions to improving the accessibility of less developed regions, enhancing external relations, accelerating the inflow of production factors and promoting more balanced regional development. In light of the status quo of China, however, including the fact that population appears to be large dispersion, small concentration in Northwest China, while there is population shrinkage in Northeast China, large-scale construction of transport infrastructure may not be particularly efficient for further improving regional accessibility. Instead, the speed

18

Eastern

Guangzhou

16

17

Western

Northeastern

Chengdu

15

Western

Urumqi

Shenyang

13

14

Central

Central

Zhengzhou

12

11

Changsha

Western

Guiyang

10

Eastern

Eastern

Shijiazhuang

Beijing

9

Eastern

Tianjin

7

8

Central

Eastern

6

Nanchang

Central

Hefei

4

5

Jinan

Central

Eastern

Taiyuan

Xiamen

3

Eastern

Shenzhen

1

2

Eastern

Central

Shanghai

Wuhan

2010

Region

City

20

19

17

4

16

12

9

14

13

10

11

7

5

8

6

2

1

3

2015 Xi’an

Ningbo Dalian

−3 −3

Lhasa Chongqing Haikou

−1 −2 −2

Nanjing

+11

Harbin Kunming

+1 −2

Nanning

Xining

−1

+3

Hohhot Hangzhou

0 −3

Changchun

Fuzhou Lanzhou

−2 −3 +1

Qingdao

+1

Yinchuan

−2 +1

City

Change

Table 5.1 Ranking results for regional accessibility in major cities of China

Eastern

Western

Western

Eastern

Western

Northeastern

Western

Northeastern

Eastern

Western

Eastern

Western

Northeastern

Western

Eastern

Eastern

Western

Western

Region

36

35

34

33

32

31

30

29

28

27

26

25

24

23

22

21

20

19

2010

36

35

34

31

32

33

30

29

28

27

25

26

24

22

15

23

21

18

2015

0

0

0

+2

0

−2

0

0

0

0

+1

−1

0

+1

+7

−2

−1

+1

Change

162 5 Population-Based Service Level and the Accessibility of Transport

5.2 Population-Based Transport Accessibility in China

163

Table 5.2 Changes in regional accessibility in in different regions of China Region

Travel time in 2010 (Min)

Travel time in 2015 (Min)

Change (Min)

Change rate (%)

Northeastern region

730.7

699.0

35.6

4.87

Eastern region

566.0

531.2

37.0

6.53

Western region

905.0

847.8

58.2

6.43

Central region

577.9

536.7

41.9

7.25

and efficiency of each type of transport mode should receive more attention to shorten the travel time from one region to the other regions further. In terms of monetary costs, the accessibility of West China and some parts of Northeast China is still poor. The cost of rapid transport is quite high for some residents in these regions, and residents in less developed regions cannot afford rapid transport. The time and monetary costs of interregional travel are both relatively high. For example, travel by high-speed railway is not yet available to all, and the cost is relatively high. Calculations of the cost of a round trip by high-speed railway from each prefecture-level city to the provincial capital, and the proportion of this cost in monthly disposable income per capita of the provincial-level city indicate that the proportion of eastern provincial-level city high-speed rail travel is much smaller than that of western provincial-level cities. The proportion of high-speed rail travel in most provincial-level administrative units is basically above 10%, which means that high-speed railway trips cause a heavy economic burden. In terms of time costs, the population-weighted regional accessibility in West China is still poor, and the average travel time is still longer than that of other regions (Fig. 5.11). To lighten people’s travel burdens from the perspective of monetary costs, a subsidy system for the purchase of motorcycles, electric cars, and other cars should be implemented to enhance the ability of residents in less developed regions to make multiple and varied travel choices. A fare subsidy system should also be implemented to subsidise the fares paid by residents in less developed regions to travel by bus, railway, and high-speed railway, in a bid to relieve the economic pressure on them due to daily trips. In addition, the fiscal and taxation mechanism may be further improved to ensure the smooth implementation of public transport-related subsidies in less developed regions. In regard to time costs, due to the low density population in most areas of West China and some parts of Northeast China, shortening the travel time between less developed regions and other regions by setting up rapid transport hubs in population agglomerations and making more efforts to improve the operating speed and efficiency of rapid passenger transport services are recommended.

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Fig. 5.11 Proportion of travel cost from each city to the provincial capital by high-speed railway monthly

5.3 Conclusion By evaluating the service level of China’s transport infrastructure and analysing the population-based weighted regional accessibility, this chapter combines quantitative and qualitative methods to explore the spatial matching relationship between transport services and population. The results provide an analytical basis for the later chapters to explore the new requirements for sustainable transport development brought by the temporal and spatial changes of China’s population structure. The spatial characteristics of the transport service level and regional accessibility indicate that the spatial distribution of railroad network, expressway network and aviation hubs is consistent with population’s spatial distribution divided by the Hu Huanyong Line, with a pattern of dense in the east and sparse in the west. In recent years, with continuous construction and improvement of China’s transport system, the service level of each transport sector has significantly improved and the spatial distribution has been further optimised, leaving the promotion of regional equity as a priority for developing high-quality sustainable transport. At present, there are still large inter-regional differences in the direct service levels of various transport facilities across the country, and the mismatch between facility construction and the population’s spatial layout persists in some areas. For example, the results of service level evaluation for railways and highways show that the service level of transport facilities in some densely populated areas is lower than in some areas with lower population density.

5.3 Conclusion

165

In addition, the evaluated service level of transport facilities generally seems higher in developed areas and lower in underdeveloped areas. The service level in eastern areas appears relatively balanced, while there is a more significant regional gap in the service level in the central and western areas. In recent years, based on the expanding high-speed railway network of north–south railway trunk lines such as the Beijing–Kowloon Railway and Beijing–Guangzhou railway, and east–west railway trunk lines such as the Longhai Railway and Lanxin Railway, the transport links across the east, central and western regions have been effectively strengthened. In the future, the construction and optimisation of transport facilities should address the new travel demand brought about by the rapid development of the social economy and the changing trends of population structure in various regions. The service coverage of railway stations, high-speed railway stations, expressways, airports and other transport facilities and hubs should be good in population agglomerations, and the service level should be improved according to local conditions. Along with the efforts to narrow regional gaps, it is necessary to avoid spatial mismatches between facility construction and the population’s spatial pattern. The spatial pattern of regional accessibility is clearly higher in the east and lower in the west as well. We adopted the weighted average value of transport costs as a measurement for the impacts of different travel modes on regional accessibility, with the population of each grid taken as the weight. The average travel time of a prefecture-level unit was obtained by dividing the total travel time by the total population, namely transport accessibility. According to the calculated results of regional accessibility at the national prefecture level, cities with high regional accessibility are mainly concentrated in the eastern coastal provinces, as well as some central and western provinces in China. In detail, the areas with low regional accessibility mostly appear to be in western provinces or border provinces such as Inner Mongolia, Xinjiang, Tibet, Qinghai, Yunnan, Chongqing, Hainan and Heilongjiang. In recent years, under the guidance of China’s development strategy of giving more priority to western areas, the large-scale construction of transport infrastructure has improved the regional accessibility of western cities effectively. However, there is still room to improve the quality and efficiency of transport services in western China due to the characteristics of vast area with sparse populations and the constraints of natural conditions such as topography. It is certain that the population structure and land use characteristics differ widely from those of eastern China, and the regional accessibility of cities in western China to external transport still needs to be enhanced. Based on this analysis of the current service level of transport facilities and regional accessibility, the stage of filling in gaps and making up for shortcomings has basically finished nationwide, and most cities are now covered by multiple transport service facilities including railways, highways and airports. However, the quantity and quality of transport services in some economically developed and populated areas are still lower than those in central and western regions. The mismatch between the transport facilities and the spatial layout of the population may lead to the development of some areas from transport lagging to population lagging. In the future, the development of a comprehensive transport system should transform from filling in gaps and making up for shortcomings to providing services with higher quality and

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higher efficiency. While ensuring the development of regional facilities to meet the actual demand, moderate attention should also go to transport in economically developed and densely populated areas to improve service efficiency and the optimisation of the supply structure. As discussed in Chaps. 4 and 5, the current population structure of China is in continuous change in terms of urban–rural structure, gender structure, family structure and quality structure. Factors such as socioeconomic development, the transformation of production structures and lifestyles, and the adjustment of fertility policies will have ongoing impacts on the structure of China’s population. To build a modern, high-quality, people-oriented and sustainable comprehensive transport system, the future development of transport facilities and services should adapt to the new trend of travel demand caused by demographic changes, establish the concept of building a strong transport country with population development as the basis, and pay attention to the interactive and coordinated relationship between transport and population. Policies and development strategies should be implemented using more precise measures to promote the matching between the supply of transport facilities and services with demographic patterns, and to improve the efficiency, quality and sustainability of a national comprehensive transport system.

References Gutiérrez, J., González, R., & Gabriel, G. (1966). The European high-speed train network: Predicted effects on accessibility patterns. Journal of Transport Geography, 4(4), 227–238. Javier, G. (2001). Location, economic potential and daily accessibility: An analysis of the accessibility impact of the high-speed line Madrid-Barcelona-French border. Journal of Transport Geography, 9(4), 229–242. Lu, F., & Jie, C. (2008). Wuhan cheng shi quan qu wei yu ke da xing fen xi. [Location superiority and accessibility analysis on Wuhan metropolitan region]. Progress in Geography, 27(4), 68–74. Zhang, L., & Yuqi, L. (2006). Ji yu lu lu jiao tong wang de qu yu ke da xing ping jia. [Assessment on regional accessibility based on land transportation network: A case study of the Yangtze River Delta]. Acta Geographica Sinica, 61(12), 1235–1246.

Chapter 6

Travel Differences Between the Urban and Rural Population

6.1 Literature Review A large number of previous studies have emphasised the significant difference in the characteristics of travel behaviour between urban residents and rural residents, which is often called the urban–rural gap. Evidence for these differences is mainly found in aspects such as travel mode, trip frequency, travel distance and travel purpose. Potential reasons for the differentiation phenomenon in travel behaviour are diverse, are still not clear and need further investigation in developing countries. In this part, we review the previous studies relevant to the differential travel behaviour of urban and rural residents, and we discuss the main findings and the research gaps in the existing literature.

6.1.1 Urban–Rural Gap in Travel Mode Many previous studies have investigated the difference between residents living in different areas, such as comparing urban areas with rural areas and central areas with suburban areas. In most cases, residents in urban areas choose individual motorised vehicles more often than those living in rural areas. For example, Korzhenevych and Jain (2018) explored the area- and gender-based commuting differentials in India’s largest urban–rural region. Their results showed that urban residents used individual motorised vehicles more often for both short-distance and long-distance trips, while rural areas were characterised by the predominance of non-motorised travel modes. In most cases especially in developing countries, residents of rural areas had a greater preference and desire for using public transport (de Vos et al., 2020). However, evidence and conclusions from different countries sometimes differ significantly. In the context of the United States, Pucher and Renne (2005) compared the travel behaviour in rural and urban areas and found that rural residents used © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Zhao and D. Yuan, Population Growth and Sustainable Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-7470-0_6

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private cars more often than urban residents: almost every one of them would drive a private car as a first choice for most daily trips. Some studies put the focus on a certain type of travel mode to compare the usage habits of urban and rural residents. Tribby and Tharp (2019) investigated bicycling behaviour in urban and rural areas in the United States, using the 2017 National Household Travel Survey. Azmi et al. (2012) compared the walking behaviour of urban and rural residents, and they found slight differences in walking distance, walking time and walking speed. According to their findings, people living in urban neighbourhoods walk faster than those in rural neighbourhoods. Meanwhile, other studies put their focus on certain groups of people to investigate the urban–rural or centre-suburb gap in travel mode. Based on a face-to-face survey in Fuzhou, Fujian province in China, Du et al. (2020) explored and compared the travel patterns of older people living in the city centre and the suburban area. Their results showed that older people in the suburbs usually travel longer distances and are more dependent on public buses than those living in central area of the city. However, with different division standards for urban and rural, or centre and suburbs, findings about the group difference in mode choice seem to be more varied. A study using data from Dutch National Travel Survey conducted in the Netherlands from 1980 to 2010 compared the travel patterns of three groups of residents: those living in urban centres, suburbs and rural areas (Kasraian et al., 2018). Kasraian et al. (2018) found that suburban inhabitants travelled shorter distances by train and longer distances by bicycle, while rural inhabitants travelled further by car. In the context of China, where the division of urban–rural structure is generally seen as dualistic, Ao et al. (2020) found that rural residents may prefer walking for daily trips, especially those who have safe living environments. Some studies have attempted to determine the reasons behind those significant urban–rural gaps in the choice of travel mode. One widely believed reason for the difference in bicycling behaviour between urban and rural residents is the different distances between their homes and workplaces. A study focusing on urban areas in the Netherlands pointed out that, the home–work distance in urban environments makes it possible to use (e-)bicycles for commuting (Wiersma, 2020). However this explanation lacks statistical evidence. One possible reason for the urban–rural difference in driving behaviour may be the difference in car ownership. Several studies have pointed that rural residents (those holding rural Hukous) tend to have fewer cars than urban residents (those holding urban Hukous), even if they live in urban environments as migrant workers (Li & Zhao, 2017). In general, car ownership may be influenced by the location of people’s residences. Residents are less likely to own private cars as the distance from their residence to a central business district decreases (McCormack et al., 2001). Similarly, there are usually fewer household vehicles in residences that are close to a business centre (Ewing & Robert, 2010; Potoglou & Kanaroglou, 2008). In addition to central business districts and business centres, Potoglou and Kanaroglou (2008) found a significant relationship between the distance of travel and car ownership. According to studies in different countries, urban–rural gaps in car ownership seem quite common. A study based on data from the CFPS from 2010 to 2016 confirmed the existence of a significant gap in car ownership between urban and

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169

rural households. It also found that increasing income inequality between urban and rural areas may contribute to increasing this gap (Zhao & Bai, 2019). A study using a dataset from a consumer-expenditure survey in India also found that rural residents are more inclined to own private vehicle than urban residents, even with same economic conditions (Choudhary & Vasudevan, 2017). According to the existing literature on the urban–rural gap in mode choice, urban residents and rural residents hold quite different preferences for the same mode, and the potential reasons still need further investigation to permit effective promotion of public transport, reduce car dependence, and encourage sustainable travel behaviour across urban and rural areas.

6.1.2 Urban–Rural Gap in Trip Frequency and Travel Distance In terms of trip frequency and travel distance, there is evidence of a significant difference between urban and rural areas. Generally, residents in rural areas, especially in remote or lightly settled areas, often avoid taking trips whenever possible (Millward & Spinney, 2011). For most rural areas, the supply of livelihood facilities within easily accessible travel range is more limited than that in urban areas (Pucher & Renne, 2005). In areas dominated by resource-based employment in farming and fishing, many workers may have little or no commuting distance, since most of the livelihood activities take place at or near their residence. In addition to the differences in employment, the stark difference in leisure habits between urban and rural residents might be another reason why rural residents usually take fewer trips and travel shorter distances. According to a study focused on the urban–rural gap in Chinese older people’s leisure life, urban residents participated more in activities outside their home, such as attending senior school, while rural residents preferred other activities in the home, such as doing housework and taking care of grandchildren (Su et al., 2006). Similarly, Shirgaokar et al. (2020) found that rural seniors especially those who live alone or in low-density housing, are more likely not to take trips than those living in cities. However, this difference between urban and rural residents does not exist in every built environment. When local alternatives are unavailable, residents in rural areas may take more long-distance trips. In terms of journeys for shopping and communicating with friends or relatives, the dispersed characteristics of rural areas can cause rural residents to participate in trips with much longer distances than those living in city centres with nearby life services (Millward & Spinney, 2011). A study in India also confirmed that the share of long-distance trips is higher in rural areas. In addition to the need to access livelihood facilities and services, some studies also emphasised the important role of employment opportunities in the urban–rural difference in travel distance (Jain et al., 2018).

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6.1.3 Urban–Rural Gap in Travel Purpose and Other Relevant Aspects In terms of travel purposes, residents in urban areas take more regular commuting trips to fixed non-agricultural jobs, while the share of commuting trips in rural residents’ daily life is smaller (Korzhenevych & Jain, 2018). A study based on the 2005 Canadian time-use survey pointed out that rural residents spend less time and participate less often in journeys for shopping (Potoglou & Kanaroglou, 2008). For daily activities, residents in rural areas often engage in fewer leisure activities that require them to travel around. A study focused on comparing the major leisure activities of older residents between urban areas and rural areas in China found that rural seniors prefer activities indoor in their leisure time, such as playing with children and chatting with relatives, while urban residents prefer outdoor activities such as traveling and participating in community volunteer work (Su et al., 2006). What is more, differences between urban residents and rural residents have also been found in other aspects of people’s travel behaviour. For example, attitudes towards travel safety seem to differ a lot between urban drivers and rural drivers, which might lead to an urban–rural gradient in risk-taking behaviour while driving (Eiksund, 2009). A study in Norway investigated the differences in driving attitudes and behaviour among rural, periurban and urban areas (Nordfjærn et al., 2010). The results showed that rural residents are less likely to use seat belts or adjust their driving to difficult conditions than residents in urban areas. In addition, a similar significant urban–rural gap has also been observed in terms of the frequency of speeding, as generally rural drivers speed less often than those living and driving in urban areas.

6.1.4 Research Gaps To conclude, the differences in multiple aspects of travel behaviour between urban residents and rural residents have received much attention in previous studies. Both the characteristics of urban–rural gap and the possible reasons behind them have been discussed through examining empirical evidence. Meanwhile, several research gaps remain. Firstly, the existing relevant studies mainly focus on comparisons of the travel behaviour of urban residents and rural residents, while mature and clear conclusions have not been reached yet in terms of the reasons for those significant differences. More empirical evidence is needed to determine the causes of the significant gap, especially in the context of China with its complicated dualistic urban– rural relationship. Secondly, the ways of defining or dividing urban and rural areas vary greatly among different countries and areas. Thus the conclusions drawn from previous studies are usually based on specific local urban–rural systems, resulting in limited knowledge of cases in other countries or regions. In developing countries like China, the imbalanced urban–rural structure has long been a prominent issue with unique attributes; thus, more discussions based on Chinese cities are needed.

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171

Thirdly, according to existing literature on China, the focus has been mostly on urban residents or rural residents alone to analyse the travel behaviour and preferences of certain groups (Ao et al., 2020; Feng et al., 2010; Ta et al., 2015; Xiaoyun, 2006; Ye et al., 2019). Comparison analysis between urban residents and rural residents in a same context may be necessary to provide practical guidance for promoting transport equity across urban and rural areas. With China’s development strategies of a powerful transport country and rural revitalisation, it is necessary to conduct more comparative studies on the differences in travel behaviour between urban and rural residents. This study addresses the existing research gaps in three ways. Firstly, it not only describes the differences in travel behaviour of urban and rural from the common perspectives, including the number of trips, travel purposes, travel modes, and travel space ranges, but also explores other social urban–rural gaps relating to these travel differences, such as the urban–rural gaps in income level and vehicle ownership. Widening the range of the discussion on the urban–rural gap may help readers to understand the potential reasons behind the significant urban–rural differences in travel behaviour better. Secondly, this study is not limited by a single classification of urban–rural systems based on the household registration (Hukou), but it also takes the unique conditions of China’s cities into full consideration. The differences in residents’ travel behaviour have been comprehensively analysed based on three classification approaches to urban and rural. From the national perspective, areas are classified into urban and rural according to the definitions of the National Bureau of Statistics. From the perspective of different prefecture-level cities, areas are classified into city centre, township and village. From the perspective of certain case cities such as Beijing, areas are classified into central area, suburbs and outer suburbs based on the unique spatial pattern of the multi-circle urban–rural structure in China’s megacities. Through these three different perspectives, the travel characteristics of residents can be investigated in the diverse urban–rural contexts across different regions in China. Thirdly, instead of focusing only on one typical case area, this study examines multiple cities in China as typical cases to discuss the urban–rural gap in residents’ travel behaviour, including small cities, medium-sized cities, and large cities. Then, it examines typical evidence from to the megacity of Beijing, which has a very high population density, to make a detailed comparison of the travel behaviour of residents living in different areas. In the context of China’s building a strong transport system and revitalising rural areas, this study provides an empirical reference for how transport development could better serve the actual travel demand of the population when considering the temporal and spatial characteristics of urban–rural structure.

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6 Travel Differences Between the Urban and Rural Population

6.2 The National Level: Evidence from CFPS 6.2.1 Data This section focuses on the differences in travel characteristics between urban and rural residents from a national-level perspective using data from CFPS conducted in 2010. Details about sampling process and data collection in the survey are in Chap. 1. Among the multiple modules in the investigation program, the adult questionnaire module (answered by all eligible household survey members over the age of 16) was selected as the main data source for analysis, from which personal information including urban–rural attributes (based on the urban/rural classification by the National Bureau of Statistics), socioeconomic attributes and detailed information about people’s travel behaviour could be collected. According to the results, 33,600 valid respondents answered the questionnaire, of whom rural residents accounted for 53.62% and urban residents accounted for 46.58%. The main personal attributes of the rural and urban residents are in Tables 6.1 and 6.2, respectively. Table 6.1 Basic characteristics of rural respondents in the adult module of the 2010 CFPS

Attribute

Category

Percentage

Gender

Male

51.01

Female

48.99

Mean

45.41

Median

45

Standard deviation

16.38

Agricultural

93.38

Non-agricultural

6.45

No register

0.11

Not clear

0.06

Category

Percentage

Age

Household register

Table 6.2 Basic characteristics of urban respondents in the adult module of 2010 CFPS

Attribute Gender Age

Household register

Male

52.13

Female

47.87

Mean

45.64

Median

45

Standard deviation

16.44

Agricultural

44.14

Non-agricultural

55.60

No register

0.17

Not clear

0.09

6.2 The National Level: Evidence from CFPS

173

6.2.2 Methods To conduct a detailed comparison analysis on the travel behaviour and socioeconomic characteristics of the residents looking at the urban–rural gap, we used two methods. On the one hand, we used descriptive statistics to analyse the differences between urban and rural residents in income, education, employment type, time distribution mode, mode choice and travel distance so that we can understand how different the basic attributes and travel characteristics of urban and rural residents are. On the other hand, we used multiple ordinary least squares (OLS) regression and multinomial logit (MNL) regression to estimate the possible causal effects of various factors on residents’ travel behaviour including travel time allocation and choice of travel mode, with urban/rural attributes and other individual socioeconomic attributes as independent variables. Based on these empirical analyses, we can further discuss and better understand the relationship between the population’s urban–rural structure and regional travel demand. The statistical analysis and regression estimates were carried out using Stata 15.0. 1) Descriptive statistical analysis Descriptive statistics are a suite of statistics that summarise the characteristics and distribution of a set of data values (Lee, 2020). Descriptive statistical analysis requires statistical description of the variables related to the survey data, including frequency analysis, dispersion degree analysis, distribution analysis, comparison between groups and so on. In classical statistics, descriptive statistics of a data series include its minimum, maximum, range, percentile, mean, median, mode, mean deviation, standard deviation, variance, skewness and kurtosis. Through graphically representing data, the statistical characteristics of the data can be shown more clearly (Badawi & Farag, 2021). Understanding the characteristics of data is vital when conducting analyses and interpreting findings (Herbst et al., 2020). Descriptive statistical analysis is widely used in research and is a basic method for data summarisation and analysis (PinedaJaramillo, 2021; Tang et al., 2018). For the discussion on travel behaviour, some previous studies have also used descriptive statistical analysis to show the characteristics of some groups’ travel preference (Hamad et al., 2021), the gap between different groups or cases (Hesjevoll et al., 2021; Mendiate et al., 2022) and the change of travel preferences over time (Badawi & Farag, 2021). 2) Multiple OLS regression model Multiple OLS regression is widely applied based on the basic principle of least squares. By constructing a linear fitting model with multiple independent variables, the effect of each independent variable on the dependent variable is estimated, including the direction and intensity of the effect. We conducted the regression model as follows: y = β0 + β1 x 1 + β2 x 2 + · · · + βk x k + u

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6 Travel Differences Between the Urban and Rural Population

where x 1 , x 2 … x k is the independent variable (also called the explanatory variable) in the model and y is the dependent variable (also called the explained variable) in the model. The variable u, called the error term in the relationship, represents factors other than x that affect y (Wooldridge, 2009). In a multiple OLS regression model, the coefficient βi (i = 1, 2, … k) of an independent variable X i indicates the change of the dependent variable given a 1-unit increase of the independent variable, holding other factors fixed. β0 represents the constant term, which is rarely central to an analysis (Wooldridge, 2009). Multiple OLS regression analysis has some advantages. It is amenable to ceteris paribus analysis because it allows us explicitly to control for many other factors that simultaneously affect the dependent variable. Since multiple regression models can accommodate many explanatory variables that may be correlated, we can hope to infer causality in many usual cases. Compared with a simple regression model, multiple regression analysis has the added advantage that it can incorporate fairly general functional form relationships (Shin, 2021). This allows for much more flexibility (Wooldridge, 2009), although the validity of the model is based on certain assumptions. For example, the basic assumption of OLS is that the influence of independent variables on dependent variables can be fitted into a linear relationship, and the selection of independent variables is sufficient and reasonable. When these basic assumptions are satisfied, multiple OLS regression analysis is quite simple and easy to understand. Thus, it has been the most widely used vehicle for empirical analysis in various social sciences, and the OLS method is popularly used for estimating the parameters of the multiple regression model. For transport geography, previous studies have also used the multiple regression analysis to analyse people’s travel behaviour (Tortosa et al., 2021; Xu et al., 2021). 3) MNL regression model The MNL regression model refers to one of the discrete choice models (DCMs) based on logistic function. The DCMs have been used to tackle a wide variety of demand modelling problems in recent decades. This is due to their high interpretability, which allows researchers to verify their compliance with well-established behavioural theories (McFadden, 1974), and they can provide support for policy and planning decisions founded on utility theory. According to utility theory, travellers pursue the maximisation of their utility when making corresponding decisions (Ye et al., 2021). When there are multiple dependent variables and there is no inherent order relationship between the variables, the MNL model can be used for regression analysis (Ye et al., 2021; Zhang et al., 2021). The subject of behavioural selection (i.e., the resident in this study) is the decision maker, who has individual heterogeneity. The sum of all alternatives available for decision makers to select is taken as the selection set, namely the explained variable of the model. The attributes of each alternative provide a certain level of utility for decision makers. The MNL model analyses the influence of explanatory variables (such as built environment attributes, personal socioeconomic attributes, etc.) on decision makers’ behaviour by taking odds ratios of specific situations as the main focus (Zhang et al., 2021).

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If we assume that the set of alternatives for a traveller n is An , and the utility of scheme j for him or her is U jn , then the basic condition for the traveller to select scheme i from An is: Uin > Ujn , i /= j where j represents the other schemes from An . The utility function for individual n when choosing scheme i is assumed as: Uin (X in |βn ) = X inT βn + εin where X in represents a set of factors that contains the observed attributes of individual n, εin is the error term of the utility equation, and βn represents the parameter vector to be estimated. The value and the sign of each βi reflect the effect of attribute X i on individual travel choice. According to the logistic function, an individual’s probability of choosing scheme i can be represented as the formulation: exp(Uin ) pin = ∑ An j=1 exp(Ujn ) where U in represents the utility for individual n when choosing scheme i, and U jn represents the utility for individual n when choosing other schemes j. Compared with other regression models such as the linear OLS model, the MNL model has two main advantages. On the one hand, it can take multiple categorical variables as explained variables. This allows the explained variable not to be constant, such as the choices among several types of travel modes. On the other hand, the application is convenient, and the results are simple and easy to understand. This is very helpful for later qualitative analysis (Ye et al., 2021). According to previous studies, MNL analysis has been widely used for discussing the factors influencing travel behaviour (Mayo et al., 2021; Utsunomiya, 2020).

6.2.3 Analysis 1) Results of the Descriptive Statistics Analysis a. Urban–rural gap in personal income In 2010, urban residents and rural residents differed significantly in personal income level, with the average income level of urban residents being significantly higher than that of rural residents. On average, the personal income of urban residents in the previous year was as high as 12,751.26 CNY, with 39.00% of the urban respondents earning more than 10,000 CNY per year. Meanwhile, for rural residents the average income from the previous year was only 5,353.09 CNY, with only 15.32% of the

176 Table 6.3 Distribution pattern of residents’ annual personal income according to 2010 CFPS

6 Travel Differences Between the Urban and Rural Population Quantile (%)

Rural residents’ income (CNY)

Urban residents’ income (CNY)

10

0

0

25

0

0

50

1,800

6,600

75

7,000

18,000

90

15,000

30,000

95

20,000

43,200

99

40,000

100,000

rural respondents earning more than 10,000 CNY per year. More than two thirds (70.34%) of rural residents said that their personal income in the previous year was no more than 5,000 CNY. Table 6.3 shows the distribution pattern of earnings; the median value of rural residents’ annual personal income was only 1,800 CNY, which is significantly lower than that of urban residents. Various factors may be responsible for the significant differences in income levels between urban and rural residents in China, among which the different employment types seems to be a dominant reason for that difference. Residents’ employment type was either agricultural or non-agricultural. Urban residents engaged in agricultural jobs accounted for only 18.26% of the total number of employed urban residents. Among rural respondents, the share of agricultural workers appeared significantly higher, with 72.04% of them engaged in agricultural production, and only 27.92% of them having non-agricultural production as their main source of personal income. In most cases, urban residents are more likely to participate in service industries where the rate of return per unit labour is higher than that of agriculture and traditional manufacturing. Since the economic return from agricultural production is often limited and lower than that from non-agricultural production, the difference in the dominant employment type would certainly cause a significant urban–rural gap in terms of residents’ personal income. Furthermore, the share of unemployed women among urban residents (62.37%) appeared to be higher than that among rural residents (54.20%), while the share of unemployed men among urban residents (46.90%) appeared to be significantly higher than that among rural residents (43.00%). In China’s traditional family model, women tend to take more responsibility for caring for housework and raising children, while the employment market, which is dominated by non-agricultural activities in urban areas, often requires a relatively fixed and large amount of time investment. As a result, many women choose to withdraw from the labour market and become housewives (also called full-time mothers) who spend most of their time taking care of family affairs. According to the survey results, being too old to get a job (11.74%) and having to do housework (9.42%) were the two main reasons why residents were not employed at the time, followed by retirement or resignation (7.60%). Considering the differences in employment status, older people and housewives may have quite different travel habits from full-time workers, who commute between home and

6.2 The National Level: Evidence from CFPS

177

workplaces regularly. To adapt better to the actual travel needs of people, transport facilities and relevant services need to pay more attention to the daily travel patterns of those special groups. b. Urban–rural gap in education According to survey results of 2010 CFPS, there is also a significant gap in educational level between urban and rural residents. Figure 6.1 shows the distribution of educational qualifications among urban and rural residents who are no longer in school. It is obvious that the proportion of illiterate (including semiliterate) people was much higher among rural residents than that among urban residents, reaching 40.81%. For rural respondents, the proportions of primary school education and junior high school education were equivalent at 25.14% and 25.42%, respectively and fewer than 10% of them had educational qualifications of high-school level or above. However, for urban respondents, junior high school education seemed to account for a larger proportion than other education levels, with 31.73% of them having completed their study in junior high school. More than 30% of urban respondents had completed their study in colleges or universities and had got a college degree: a bachelor’s degree, a master’s degree or even a Ph.D., indicating that highly educated people occupied a larger proportion of the urban population than the rural population. In China, this significant difference between urban and rural residents in terms of education levels seems to have a close relationship with the urban–rural difference in

Urban

Rural

45 40 35 30

%

25 20 15 10 5 0 Illiterate

Primary Junior high Senior high school

College degree

Bachelor Master or degree PhD

Year

Fig. 6.1 Proportions of different types of the highest educational level of urban/rural residents

178

6 Travel Differences Between the Urban and Rural Population

access to education, quality of education, and attitude towards education. Due to the relatively underdeveloped economy in rural areas, the supply of educational facilities and services is often not as good as in urban areas. In some rural middle schools, teachers have to teach multiple courses or take charge of several classes at the same time, resulting in a reduced quality of education for young students. In some remote and economically underdeveloped areas, it is uneconomic to build and operate a public school, making it difficult for school-age children to receive education. At the same time, compared with urban families, rural families tend to pay much less attention to children’s education, and some teenagers living in the countryside may be forced to take responsibility for farming and housework after school. There are still some feudal attitudes towards education, such as getting an education is useless for girls and it is better for teenagers to go to work earlier in some remote rural areas, where the teenagers are more likely to enter the social labour market passively without receiving sufficient education. In addition, the difference in education levels between urban and rural residents seems to be related to the migration of people across urban and rural areas. Since there are often more abundant job opportunities and more inclusive environments for employment in urban areas, rural labourers with relatively higher education and more mature professional skills are attracted to move to urban areas seeking jobs with higher salaries. Many of them choose to settle down in urban areas, even though they are rural residents. Meanwhile, those who do not have sufficient education may stay in rural areas, and they are more likely to engage in agricultural production, along with older people and those unable to work due to physical disabilities. It seems that migration of highly educated people from rural areas to urban areas may have contributed to a more significant difference between the educational levels of urban and rural residents. c. Urban–rural gap in time allocation According to the questionnaire for adults in the 2010 CFPS, residents’ time allocation was reflected in their answers on the average time they spent on different activities during weekdays and weekends, including housework, leisure and entertainment, social communication, and daily trips (including all trips made within 24 h, especially those regular trips to or from schools and workplaces). Since there is a gender gap in residents’ time use, we separated the respondents into four groups to investigate the group difference in time allocation. The statistical results revealed significant urban–rural differences in residents’ time spent on housework, leisure, and daily trips. Urban residents appeared to spend less time on housework than rural residents. Taking weekdays as an example, urban female residents and male residents spend an average of 2.09 h and 0.91 h doing housework, respectively, while rural women residents and male residents spent an average of 2.36 h and 1.15 h on it, respectively. In terms of leisure, entertainment and social communication activities, the average time urban residents spend is significantly higher than that of rural residents. On weekends, urban female residents spend an average of 5.01 h and male residents spend 5.93 h, respectively. Rural female residents and male residents spend much

6.2 The National Level: Evidence from CFPS

179

less, only 2.64 h and 2.96 h, respectively, on leisure activities. In terms of taking daily trips especially to or from schools and workplaces, urban residents spend more time than rural residents, but the difference was not significant. On weekdays, urban female residents and male residents spend 0.42 h and 0.54 h on taking trips, respectively. The time spent by rural female residents and male residents on travel were less at 0.33 h and 0.46 h, respectively, showing an urban–rural difference of about 0.1–0.2 h. The urban–rural difference in time allocation patterns for daily activities indicates a difference in the living habits of urban and rural residents. In terms of housework, in addition to taking charge of it on their own, urban residents can share the pressure of doing housework by hiring housekeeping personnel and purchasing smart home appliances, which can help to reduce their time spent on housework. Rural residents are often more likely to be restricted by economic conditions, and they have to take charge of all the housework by themselves, resulting in a large time investment. In terms of activities for leisure, on the one hand, urban residents have access to more abundant leisure and entertainment facilities, and those diverse and accessible facilities provide urban residents with more options and more attractive leisure opportunities. On the other hand, evidence has shown that urban residents are mainly engaged in non-agricultural employment; therefore, a large number of them can enjoy free time for leisure and entertainment on weekends. Rural residents were more likely to rely on non-agricultural production for livelihood, which often requires continuous time investment without regular time off, such as weekends. As a result, the heavy pressure of making a living through agricultural production might be a major reason for rural residents lacking in time for leisure. On daily trips, although there is no significant urban–rural difference in the average time spent, the distribution pattern on weekdays and weekends still differs. According to the frequency distribution graph shown in Figs. 6.2, 6.3, 6.4 and 6.5, the time spent by urban residents on trips seem to differ a lot between weekdays and weekends, with less travel at the weekend. Meanwhile, there is almost no difference in the time spent on trips by rural residents between weekdays and weekends, which may result from the fact that agricultural production is rarely affected by the day of the week.

Fig. 6.2 Frequency distribution of time spent by rural males according to the 2010 CFPS

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6 Travel Differences Between the Urban and Rural Population

Fig. 6.3 Frequency distribution of time spent by rural females according to the 2010 CFPS

Fig. 6.4 Frequency distribution of time spent by urban males according to the 2010 CFPS

Fig. 6.5 Frequency distribution of time spent by urban females according to the 2010 CFPS

d. Urban–rural gap in mode choice In terms of mode choice for daily trips, the public transport utilisation rate of urban residents appears to be higher than that of rural residents. According to the interview answers, the most common travel mode of rural residents was motorcycle except for walking. Motorcycles were their main mode for 16.76% of rural residents, followed

6.2 The National Level: Evidence from CFPS

181

by buses (16.02%) and bicycles (13.55%). In addition, agricultural vehicles played a role in rural residents’ daily trips and accounted for 1.82% (Fig. 6.6). For urban residents, the most commonly used travel mode except for walking was bus, and nearly 22.27% of urban respondents said they take buses as their main mode, followed by bicycles (14.52%) (Fig. 6.7).

Fig. 6.6 Proportions of different travel modes commonly used by rural residents, according to the 2010 CFPS

Fig. 6.7 The proportions of different travel modes commonly used by urban residents, according to the 2010 CFPS

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6 Travel Differences Between the Urban and Rural Population

Consistent with the evidence in previous studies, public travel modes such as bus and subway are significantly more popular among urban residents than rural residents. There may be several possible reasons. On the one hand, the scale, the quality and the accessibility of transport facilities differ significantly between urban and rural areas. In general, urban areas have more advanced construction and operation of public transport systems, and the coverage of bus and subway lines is usually wider to meet the travel needs of urban residents across different destinations. Meanwhile, rural areas usually have fewer bus lines and smaller coverage since it is often less necessary for most rural areas, where the population density is lower and the economy is less developed, to build subway systems, which require large investments in labour capital and raw materials. On the other hand, the difference in travel mode choices may indicate a difference in travel habits and preferences between urban and rural residents. Since most residents in rural areas are engaged in agricultural activities, the time for going out and returning home is usually more flexible than that of urban residents engaged in non-agricultural permanent jobs, and the travel destinations may also be more varied. As a result, fixed-line and regular public transport may be not that suitable for meeting rural residents’ daily travel needs, while active and private vehicles such as motorcycles and agricultural vehicles are more popular. e. Urban–rural gap in travel opportunities The survey results of the 2010 CFPS also provide evidence that urban residents have more diverse travel opportunities than rural residents. Among the rural respondents in 2010 CFPS, nearly half (41.04%) said they had never taken a trip by train. However, less than a quarter (23.00%) of urban residents had not taken a train. Almost all rural respondents (96.42%) said that they had never had the opportunity to take an aeroplane, while the proportion who had taken an aeroplane was nearly a quarter among urban residents (22.68%). It is obvious that urban residents had more access to long-distance and more advanced travel modes such as trains and aeroplanes. In terms of travel experience, although the proportions of residents who had never been to Hong Kong, Macau, Taiwan or abroad approached 90% for both urban and rural respondents, there was still some urban–rural difference. Specifically, 6.84% of urban residents said they had been to Hong Kong, Macau or Taiwan, while the proportion of rural residents was only 0.65%. Just 4.66% of urban residents had been abroad, while only 0.85% of rural residents had had the chance of taking international journeys. These urban–rural differences in travel opportunities and experience might be the result of multiple possible factors. On the one hand, the income level of urban residents is usually relatively higher, along with more leisure time for long-distance travel on weekends. As a result, they are more likely to be able to afford trains and aeroplanes for cross-regional journeys. Meanwhile, it may be unaffordable for most rural residents to take aeroplanes or trains except to meet essential needs (such as for medical treatment or employment opportunities). On the other hand, as interregional exchanges across urban areas are usually more frequent and many commercial projects require cross-regional cooperation, urban residents are more likely to

6.2 The National Level: Evidence from CFPS Urban

Rural

90 80 70 60 50 40 30 20 10 0

Urban

120 100 80

%

%

Rural

183

60 40 20 0

No, I haven't

Yes, I have

No, I haven't

Yes, I have

Year

Year

Fig. 6.8 Answers to the questions “Have you taken a train” “Have you taken an aeroplane”

Rural

Urban

Rural

100

100

80

80

60

60

%

120

%

120

40

40

20

20

0

Urban

0 No, I haven't

Yes, I have

No, I haven't

Year

Yes, I have

Year

Fig. 6.9 Answers to the questions “Have you been to Hong Kong, Macau, Taiwan” and “Have you been abroad”

take long-distance journeys for business, exchanges or learning, which are mostly not accessible or of little necessity in rural areas (Figs. 6.8 and 6.9). 2) Results from the Multiple Regression Models a. Influencing factors of residents’ travel time on week days Considering the significant differences in travel behaviour between urban and rural residents, this section moves further to explore the influence of urban and rural attributes as well as other socioeconomic factors on people’s travel behaviour using a multiple linear regression model for estimation. Based on the travel information collected from the 2010 CFPS, we focus on the pattern of residents’ time allocation, and take the time residents’ spend on taking trips during workdays as the explained variable. Explanatory variables include the urban–rural status of residents and relevant individual socioeconomic attributes such as gender, age, education level, employment type, and personal income. Among them, urban or rural status, gender, employment status and education level are categorical variables, while age, personal income and time spent on trips are continuous variables. The main statistical characteristics of the sample are in Table 6.4. According to the results of the multiple OLS regression model, the F value of the model was 174.39, which passed the F-test at the 0.01 significance level, indicating

184

6 Travel Differences Between the Urban and Rural Population

Table 6.4 Main statistical characteristics of the sample in terms of categorical variables Variable

Category

Sample size

Percentage

Urban/rural

Rural

17,004

53.62

Urban

14,710

46.38

Female

16,355

51.57

Male

15,359

48.43

Employed

15,611

49.22

Unemployed

16,103

50.78

Literate/semiliterate

9,830

31.00

Primary school

6,658

20.99

Junior high

8,968

28.28

Senior high

3,986

12.57

College degree

1,386

4.37

838

2.64

Master’s degree

44

0.14

Doctor’s degree

4

0.01

Gender Employment Education level

Bachelor’s degree

that the model was very significant. VIF was applied to test whether there was any multicollinearity problem. The result showed that the VIF values of all variables were below 2.0, which indicated that there was no significant multicollinearity problem. The detailed estimation results of each explanatory variable are in Table 6.5. According to the estimation results for each explanatory variable, residents’ urban–rural attributes, gender, age, employment status, education level and personal income had significant effects on the amount of time residents spent taking trips on weekdays (Table 6.6). Consistent with the previous statistical results, urban residents spend relatively longer in daily travel than rural residents. The coefficient t parameter is 6.61, and the p value is less than 0.01. In general, most urban areas have a more comprehensive transport service system, and the diversity of travel modes and the density of transport facilities are higher than that in rural areas. However, since the Table 6.5 Results for multicollinearity test

Variable

VIF

1/VIF

Urban (base group: rural)

1.18

0.845528

Male (base group: female)

1.08

0.927889

Age

1.23

0.814041

Employed (base group: unemployed)

1.16

0.860085

Education level

Junior/senior high

1.3

0.769706

At least college degree

1.35

0.740551

Income

1.25

0.801098

Mean VIF

1.22

6.2 The National Level: Evidence from CFPS

185

Table 6.6 Regression results with travel time as the explained variable Variable

Unstandardised coefficient

Beta

Std. Error

P>t

Urban (base group: rural)

0.053

0.040

0.008

0.000

Male (base group: female)

0.095

0.072

0.008

0.000

−0.039

0.000

0.000

0.080

0.008

0.000

−0.007

0.009

0.241

0.158

0.061

0.017

0.000

Income

0.000

0.065

0.000

0.000

Constant

0.363

0.016

0.000

Age Employed (base group: unemployed) Education (base group: Junior/senior high below primary school) College or higher

−0.002 0.106 −0.010

travel demand in high-density cities is usually more complex and diverse, the demand for transport services often exceeds the supply, especially during the morning and evening peak hours on weekdays. When a large number of commuters have to travel at the same time, the efficiency of urban transport services may be reduced, resulting in longer trip times. In addition to the effect of urban–rural attributes, gender appears to play an important role. Its estimator is positive with the p value for the t parameter being less than 0.01. Males tend to spend more time on travel. One possible reason for this gender gap may be that the proportion of male residents participating in permanent jobs (58.17%) is higher than that of female residents (43.83%). According to the estimated results, employed residents would have to spend more time on taking trips than the unemployed. This is reasonable since residents who have permanent jobs often need to spend a long time commuting between their homes and their workplaces on weekdays, leading to the morning and evening peaks in city centres, while unemployed residents such as housewives who mainly take care of family affairs, or free workers, often have relatively more flexible and diverse travel chains. Instead of going to fixed workplaces, they can choose travel routes that require shorter distances and less time, such as buying daily necessities within the neighbourhood. The result of coefficient estimation for age shows that older residents spend relatively shorter time on trips on weekdays, with the estimated beta being −0.039 and the p value less than 0.001. Consistent with common knowledge, individuals’ physical functions become more restricted, and they tend to be less likely to tolerate long-distance trips when they get older. In many cased, retired people may only want to take part in entertainment, leisure, and shopping activities around their neighbourhoods. It is neither necessary nor pleasant for them to spend much time travelling. In addition, some older people may even have lost the physiological ability to travel a long distance, which brings about expectations and requirements for more humane customised logistics services and facilities around neighbourhoods with high proportions of older residents.

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6 Travel Differences Between the Urban and Rural Population

b. Factors influencing residents’ travel mode choice In the 2010 CFPS, information about residents’ preference for travel mode in the previous 3 months was collected. Here, MNL was applied to investigate how the urban/rural attribute and other socioeconomic factors affect residents’ mode choice. Based on the previous statistical analysis, the difference between urban residents’ and rural residents’ travel mode choice was mainly reflected in the different usage rates of public transport. Taking that into consideration, we divided the sample into three groups based on their different dependence on public transport: (a) the two most frequently used modes are both public transport (bus or subway); (b) one of the two most frequently used modes is public transport, and the other is private transport; and (c) The two most frequently used modes are both private vehicles. Considering that some residents may be unable to use any vehicles due to physical limitations (such as older people or those with disabilities) and can only travel by walking, participants who did not choose any vehicles are excluded here. The sample size and proportions of the above three types of valid samples are in Table 6.7. In the model, the type based on the two most frequently used travel modes was taken as the explained variable, and the urban/rural attribute along with other socioeconomic characteristics, including gender, age, education level, employment status, and personal income were taken as explanatory variables. Among them, urban/rural attribute, gender, employment and education level were categorical (dummy) variables, while age and personal income were continuous variables. The estimation results of the multiple regression model are in Table 6.8. The overall significance p value in the model appears less than 0.01, and each estimated coefficient turns out to be very significant. In the model, the first group (y = 0) in which residents’ most frequently used travel modes are both public transport is set as the base group. According to the modelling results, in the second group (y = 1) and the third group (y = 2), the estimated causal effects of urban are both positive, with the significance p value smaller than 0.001. This indicates that urban residents are more likely to choose public transport as their daily use mode. This result is consistent with the previous descriptive statistical analysis of urban and rural residents’ usage rate of different travel modes. In addition to urban and rural attributes, other personal socioeconomic attributes also appear to have significant impacts on residents’ decision-making on travel mode. The estimated odds of gender in the second and third groups are both less than 1, with the significance p value being smaller than 0.001. Since the female group was set as the base group, this means that men are less likely to take public transport as their most frequently used travel mode than women. Consistent with the common Table 6.7 Statistical characteristics of the sample based on frequently used travel modes Assignment Two most frequently used travel modes 0

Both public travel modes

1

One public travel mode and one private travel mode

2

Both private travel modes

Sample size Proportion (%) 16,610

62.40

9,717

36.51

291

1.09

6.2 The National Level: Evidence from CFPS

187

Table 6.8 Regression results with the most frequently used mode type as the explained variable Variable

Relative risk reduction

Std. error

z

P>z

Mode = 0 (Base outcome) Mode = 1 Urban (base group: rural)

1.353

0.038

10.690

0.000

Male (base group: female)

0.616

0.017

−17.800

0.000

Age

1.012

0.001

12.140

0.000

Employed (base group: unemployed)

0.710

0.020

−12.210

0.000

Education (base group: below primary school)

Junior/senior high

1.319

0.039

9.260

0.000

At least college degree

2.332

0.130

15.170

0.000

Income

1.000

0.000

−3.180

0.001

Constant

0.382

0.021

−17.380

0.000

Mode = 2 Urban (base group: rural)

14.365

4.305

8.890

0.000

Male (base group: female)

0.531

0.067

−5.040

0.000

Age

0.999

0.005

−0.190

0.852

Employed (base group: unemployed)

0.746

0.103

−2.120

0.034

7.873

2.173

7.480

0.000

25.682

7.520

11.090

0.000

Income

1.000

0.000

3.230

0.001

Constant

0.001

0.000

−16.970

0.000

Education (base group: below primary school)

Junior/senior high At least college degree

findings in the existing literature, women seem more reliant on public transport for daily travel activities. There may be multiple possible reasons for women’s preference for public transport. For example, women usually take on the main responsibility of doing housework in the family, which leads to their travel destinations becoming quite flexible and complicated. In addition to workplaces, destinations including vegetable markets, supermarkets and shopping centres also occupy an important position in women’s travel chains. Compared with bicycles, electric bicycles, motorcycles or cars, public transport provides women with more convenient and much cheaper travel services to meet their flexible and short-distance travel demand. At the same time, safety may also be a more vital concern for women than for men. Compared with booking a taxi, taking public transport such as buses and subways is usually believed to provide women with a safer travel environment. In addition, the estimated causal effect of age in the second group appears to be significantly positive, with the odds being 1.012 > 1. It seems that older residents prefer to use public transport combined with other private modes for their daily travel rather than relying just on public transport vehicles. Since many older people have some disabilities and cannot drive or ride bicycles alone, the integration of walking

188

6 Travel Differences Between the Urban and Rural Population

and bus can provide them with a convenient, safe and easy travel experience for leisure, shopping and visiting friends. Taking this into consideration, a more advanced older-friendly public transport system as well as more adequate barrier-free facilities in public transport are necessary.

6.3 A City Level: Comparison Among Urban Areas, Townships and Villages 6.3.1 Data The data applied in this section mainly come from the National Detailed Survey of Small Towns. In June 2016, a detailed survey of 121 small towns selected from 31 provinces (autonomous regions or municipalities directly under the central government) was conducted with the cooperation of several research institutes and universities. The survey was designed with five types of questionnaires, and a total of 211 survey questions and 1,305 indicators were collected. The data for this study mainly came from the questionnaires for households located in townships and for villagers. More details about the sampling and data collection are in the third part of Chap. 1. In this section, 11 cities are taken as the case cities based on a dispersed selection across different provinces and regions. Both megacities and big cities are taken into consideration, as well as both single-centre and multi-centre cities, to improve the representativeness and diversity of case cities. They are Beijing, Shanghai, Chongqing, Nanchang, Lanzhou, Taiyuan, Nanning, Taizhou, Baoji, Qiqihar and Zibo. The spatial location of the case cities in China is in Fig. 6.10, and the basic characteristics of each case city are in Table 6.9. We analysed data from the National Detailed Survey of Small Towns as well as previous studies for these 11 case cities. From residents of townships, there were 1,844 valid respondents. From residents living in villages, there were 444 valid respondents. The sample size of each case city are shown in Figs. 6.11 and 6.12. In addition to the survey data, a detailed collection of the findings and results on urban residents’ travel characteristics in the above case cities took place based on previous studies and published documents. Considering that the survey was conducted in 2016, we chose 2014–2016 as the time range for collecting existing evidence. These previous studies on different cities’ travel served as an empirical basis for exploring and discussing the difference in travel patterns between rural residents in townships and villages and those living in urban areas.

6.3.2 Methods Based on the data, we integrated the travel information of three groups of residents: those in urban areas, in townships and in villages of each case city to analyse their

6.3 A City Level: Comparison Among Urban Areas, Townships and Villages

189

Fig. 6.10 Spatial distribution of the case cities

Table 6.9 Basic characteristics of each case city

Name

Administrative level

City size

City structure

Beijing

Municipality

Megacity

Single-centre

Shanghai

Municipality

Megacity

Single-centre

Chongqing

Municipality

Megacity

Multi-centre

Nanchang

Prefecture-level*

Big city

Multi-centre

Lanzhou

Prefecture-level*

Big city

Multi-centre

Taiyuan

Prefecture-level*

Big city

Multi-centre

Nanning

Prefecture-level*

Big city

Multi-centre

Taizhou

Prefecture-level

Big city

Multi-centre

Baoji

Prefecture-level

Big city

Multi-centre

Qiqihar

Prefecture-level

Big city

Single-centre

Zibo

Prefecture-level

Big city

Single-centre

Note * Means that the prefecture-level city is a provincial capital

diverse characteristics in travel behaviour from a perspective of urban–rural comparison. There are two main aspects of innovation in the study. Firstly, on the basis of the general urban–rural division, it moves a further step through taking the particularity of township administrative units into consideration. Instead of only focusing on urban and rural in a general dualistic context, traditional rural areas have been further divided into townships and villages, to provide a more detailed and clearer understanding of the travel differences among different groups of residents living in

190

6 Travel Differences Between the Urban and Rural Population

Fig. 6.11 Proportions of each city’s respondents in townships

Fig. 6.12 Proportions of each city’s respondents in villages

different built environments. Secondly, since the regional differences in China have been quite significant in terms of their gaps in economic development status, social demographic situation and infrastructure construction, to ensure the comparability of cases and improve the applicability of policy implications, the analysis is based on a classification of cities. Comparisons between urban areas (city centres), townships and villages are made within certain cities to avoid the deviation and bias that may be caused by the inherent differences among cities.

6.3 A City Level: Comparison Among Urban Areas, Townships and Villages

191

Descriptive statistics analysis and variance analysis are taken as the main ways to screen the available data from towns and villages. The advantages and common usage of descriptive statistical analysis have been described in detail in the previous section of this chapter. These methods are mainly applied based on the data from the National Detailed Survey of Small Towns to analyse the travel behaviour characteristics of different demographic attributes, especially the urban–rural attribute. Comparison are made to provide better understanding of urban–rural gaps through discussing the different characteristics of rural residents, including those living in the townships and those living in villages, and those of urban residents living in the same case cities. On this basis, the relationship between regional urban–rural structure and travel behaviour as well as transport demand can be further analysed.

6.3.3 Analysis 1) Ownership of transport vehicles According to the survey results, residents living in townships and villages own different travel vehicles (Table 6.10). Based on variance analysis, that the main difference between townships and villages in terms of vehicle ownership concern in electric bicycles (p < 0.1) and motorcycles (p < 0.1). The car ownership and bicycle ownership of town residents and villagers seem to be quite similar. a. Popular use of electric bicycles and motorcycles in rural areas According to the survey results, electric bicycles and motorcycles are highly popular in rural areas. Compared with other types of travel modes, electric bicycles and motorcycles seem to have more advantages for the daily trips of villagers. On the one hand, Table 6.10 Transport vehicles ownership (%) in towns and villages Ownership

Private car

Electric bicycle

Motorcycle Towns

City

Towns

Villages

Towns

Villages

Beijing

43.00

36.20

25.50

67.20

2.70

Bicycle

Villages

Towns

Villages

10.30

28.10

33.80 43.00

Shanghai

33.20

33.70

41.60

75.60

6.40

10.20

30.90

Chongqing

24.30

16.90

7.70

7.20

23.40

48.20

8.00

3.60

Nanchang

20.70

12.00

44.10

72.00

24.10

36.00

24.20

20.00

Lanzhou

16.30

30.00

10.50

13.30

18.10

46.70

58.90

63.30

Taiyuan

23.80

11.10

9.10

25.90

5.60

18.50

16.90

11.10

Nanning

11.50

6.90

36.70

27.50

69.70

48.30

13.50

13.80

Taizhou

39.40

33.30

44.30

90.50

7.00

23.80

19.10

19.00

Baoji

40.20

15.00

18.70

10.00

36.30

60.00

31.60

20.00

Qiqihar

19.00

30.00

30.00

70.00

39.00

60.00

31.00

30.00

Zibo

54.40

62.50

46.60

83.30

9.50

45.80

43.20

62.50

192

6 Travel Differences Between the Urban and Rural Population

using electric bicycles or motorcycles makes up for the inconvenience of traditional bicycles when travelling long distances. Thus, electric bicycles or motorcycles can effectively meet their needs of travelling from village to village, from household to farmland. On the other hand, it is much less expensive to own an electric bicycle or motorcycle, than a car. As a result, electric bicycles and motorcycles are convenient in rural areas where the economic level is relatively low. In addition, most of the rural areas in China implement relatively looser control measures for traffic flow than urban areas, where the management of transport vehicles and driving behaviour is much stricter, leading to a more friendly environment for travel by electric bicycles and motorcycles. b. Narrowing gaps between townships and villages in car ownership According to the analysis results, gaps in car ownership between residents living in townships and villages seem to have narrowed significantly. In addition, car ownership in some villages even exceeds that in nearby towns for several case cities, such as Lanzhou, Qiqihar, and Zibo. The popularisation of private cars in rural areas could reflect the rapid development of society and the economy in China, especially in less-developed rural areas. With the continuous advancement of building the new countryside, motorised vehicles and electric cars are certain to become increasingly available and affordable in rural areas. As a result, there will be more demand and new requirements for high-quality transport facilities such as road networks and public transport in rural areas. 2) Main travel purposes This section details the varied reasons for traveling among different regional destinations, including travel from towns to county seats, from towns to city centres, from villages to town centres and from villages to county seats. a. Cross-regional shopping Among the diverse travel purposes to a superior administrative unit, shopping (including purchasing goods and delivering products as well as buying and selling agricultural materials) occupies an important position. Most interviewed residents took trips from villages to town centres motivated by their daily needs for shopping, and the proportion was more than 50% in all the case cities except Lanzhou. In Shanghai, 50.50% of town residents travelled to the county seat for shopping, while other travel purposes accounted for much smaller proportion. For Beijing, Taiyuan and Zibo, more than 40% of residents in towns went to county seats to buy something. Meanwhile, there were significant difference between cities. For example, in Lanzhou, the shopping demand appeared a less important reason to travel to the county seat, while the main motivation usually involved seeking medical treatment or work. For example, 45.50% of the residents of towns in Lanzhou travelled to the county seat for work. In Nanning, 46.34% of town residents travelled to the county seat to see a doctor. It seems that, in areas with relatively underdeveloped economies and inconvenient transport facilities, elastic demand such as consumption accounts

6.3 A City Level: Comparison Among Urban Areas, Townships and Villages

193

for a smaller proportion, while rigid demands such as medical treatment tends to be more important in daily travel. Nowadays, residents and goods in China seem to move and be exchanged more and more frequently across different areas. The frequent cross-regional goods exchanges indicate that residents’ material demands are becoming more diverse and complicated. There are still some gaps between people’s expectation for consumption and the local supply. As a result, they need to travel to county seats and city centres to seek more adequate and more abundant resources. To some extent, cross-regional exchanges also reflect the significant gap in development among China’s cities, towns and villages. In rural areas, commercial infrastructure is relatively backward, and supermarkets and shopping centres remain relatively scarce. Many villagers can only buy daily supplies from vegetables markets or commissaries. Since online shopping has not been well promoted in some rural areas with low economic development levels, many villagers are still unable to use online shopping software proficiently online. In many remote areas, consumers must pay for the postage themselves to buy something online, resulting in them being forced to travel to the town or county seat to buy daily necessities and agricultural supplies. b. Cross-regional medical treatment In addition to shopping, seeing a doctor is another important purpose of residents’ daily trips across regions. In some cities such as Nanning, most residents living in towns travel to the county seat for medical treatment. The proportion who go to see a doctor is even higher than those who go to buy something. Some 46.34% of trips from towns to county seats are to see a doctor. More than half (53.49%) of the journeys from villages to town centres in Lanzhou are to see a doctor. In Baoji, 39.39% of the trips from villages to county seats are for medical treatment, and this proportion is close to that of shopping. The high proportion of residents traveling across regions to see a doctor indicates the significant gap in the supply of medical and health services between city centres, county seats, towns and villages. In most cities in China, medical resources are more abundant in city centres and county seats, while villages and towns lack hospital and chemists. As a result, residents living in villages or towns often have to go to the county seat or city centre to get medical and health services. This gap between regions makes it necessary to promote better transport infrastructure in less developed areas such as remote villages and townships, to ensure that residents can get medical treatment more quickly and efficiently. c. Holiday visits across urban and rural areas Visiting relatives or friends is another important travel purpose. From the survey results, there is no significant difference in the proportion of those visiting relatives and friends between trips from township to the county seat and those to the city centre. However, this purpose seems to occupy a much more significant position in trips from village to the county seat than those to the town centre. For example, 43.48% of the villagers in Nanchang travel to the county seat to visit relatives and friends, and the proportion in Lanzhou is 40.00%. In Chongqing, 40.56% of the residents in towns travel to visit their relatives and friends, while only 25.41% travel

194

6 Travel Differences Between the Urban and Rural Population

for shopping. Visiting relatives and friends occupies an important position in crossregional travel activities in some cities. This result might be closely related to the traditional custom of going out to visit relatives at Chinese holidays such as Spring Festival, Mid-Autumn Festival, and Qingming Festival. With the rapid increase of the urbanisation rate, many people who originally lived in towns or villages have moved to county seats or city centres, becoming spatially separated from their relatives in the process. On weekends or holidays, many residents often travel to visit their relatives and friends to sustain their relationships. As a result, during traditional holidays, the cross-regional travel flows in different cities increase significantly. This largescale traveling flow requires regional transport facilities to have better elasticity to cope with cyclical changes in travel demand. Figures 6.13, 6.14, 6.15, 6.16 and 6.17 illustrate these reasons for travel. 3) Choice of travel mode Since there are significant differences among the vehicle ownership and the reasons for travel of residents in different areas, we make a cautious comparison of the usage frequency of cars (including petrol cars and electric cars), buses and bicycles among different groups of travellers (including those living in the urban areas, townships and villages) for different travel purposes. a. Cars are more popular in megacities than in towns/villages In the typical megacities of Beijing, Shanghai and Chongqing, the usage frequency of cars by residents of cities is much higher than that of residents of towns and villages. The proportions choosing cars are 31.50% in Beijing, 19.20% in Shanghai

Work

%

Pick up children

Go shopping

Visit friends

Personal affairs

See a doctor

Others

100 90 80 70 60 50 40 30 20 10 0

City

Fig. 6.13 Proportions of different types of travel purpose from towns to county seats

%

6.3 A City Level: Comparison Among Urban Areas, Townships and Villages

Work

Go shopping

Visit friends

Pick up children

Personal affairs

Others

See a doctor

100 90 80 70 60 50 40 30 20 10 0

City

%

Fig. 6.14 Proportions of different types of travel purpose from towns to city centres

Part-time job

Go shopping

Dining

Purchase agricultural materials

Selling agricultural products

Visit friends

See a doctor

Pick up children

100 90 80 70 60 50 40 30 20 10 0

City Fig. 6.15 Proportions of different types of travel purpose from villages to town centres

195

196

6 Travel Differences Between the Urban and Rural Population Part-time job

Go shopping

Dining

Purchase agricultural materials

Selling agricultural products

Visit friends

See a doctor

Pick up children

%

100 90 80 70 60 50 40 30 20 10 0

City Fig. 6.16 Proportions of different types of travel purpose from villages to county seats

Monday

Tuesday

Wednesday

Thursday

Friday

Saturday

120 100

%

80 60 40 20 0 First time

Second time

Survey time Fig. 6.17 Proportions of participants for each survey day in 2005

Sunday

6.3 A City Level: Comparison Among Urban Areas, Townships and Villages

197

Table 6.11 Usage rates of cars among residents of different areas (%) City

Urban

Townships

Villages

Commuting

Shopping

To town centre

To county seat

Beijing

31.50

22.99

18.13

26.81

17.81

Shanghai

19.20

11.63

8.84

13.43

26.55

Chongqing

33.5

22.75

5.70

13.36

14.22

Nanchang

–

Lanzhou

12.50

Taiyuan

–

2.17

1.99

4.35

5.26

18.75

5.10

17.27

17.17

8.24

8.40

11.53

11.99

Nanning

–

0.00

2.94

14.72

4.29

Taizhou

–

30.77

25.00

16.69

26.30

Baoji

11.70

25.93

30.12

21.05

21.05

Qiqihar

30.45

22.22

14.29

25.00

15.79

Zibo

18.51

31.31

23.16

20.80

50.00

and 33.50% in Chongqing. Although there is no significant difference in car usage between towns and villages, the gap between these areas and the cities is still significant. Taking Chongqing as an example, in 2016, the car usage rate for the whole city reached 33.50%, while those in towns for commuting and shopping were only 22.75% and 5.70%, respectively. The car usage rates of villagers traveling to town centres and county seats were only 13.36% and 14.22%, respectively (Table 6.11). Since traveling by cars is often more expensive, this difference might reflect the gaps in income level and consumption level between urban and rural areas. b. Public buses are more popular in villages than in cities and towns According to the survey results, the usage rate of public buses in village areas is much higher than that in city and town areas. In terms of the travels between villages and county seats, more than 70% of this type of cross-regional travel is made by bus in most cities. However, residents of towns rarely seem to choose bus as their travel mode (Table 6.12). The results show that public buses play an important role in connecting villages and county seats. Thus villagers can go to the county seat to purchase daily supplies, visit relatives and seek medical treatment conveniently. In future, improvement of the public transport system could become an entry point to improve the transport services effectively in rural areas. c. Township residents prefer walking in daily trips According to the survey results, in large cities, especially provincial capitals, the commuting and shopping trips of township residents mainly rely on walking. For example, more than 80% of town residents in Nanchang and Taiyuan choose to go shopping on foot (Table 6.13). It seems that essential facilities in the rural areas of large cities are well provided, the daily consumption demand can be met within walking distance, and vehicles for long-distance travel including buses and taxis are

198

6 Travel Differences Between the Urban and Rural Population

Table 6.12 Usage rates of public buses among residents of different areas (%) City

Urban

Townships

Villages

Commuting

Shopping

To town centre

To county seat

Beijing

–

21.31

12.09

3.52

80.33

Shanghai

–

4.69

8.96

15.84

67.30

Chongqing

59.30

14.14

9.28

28.04

72.74

Nanchang

–

2.17

0.00

0.00

84.21

Lanzhou

36.50

0.00

6.12

6.93

82.73

Taiyuan

29.00

10.59

7.63

49.95

84.02

Nanning

–

13.33

0.98

18.47

95.71

Taizhou

11.30

3.85

1.09

5.60

47.40

Baoji

34.60

39.51

46.99

10.53

73.68

Qiqihar

31.27

4.44

16.67

10.00

73.68

Zibo

5.99

6.06

42.11

0.00

37.50

Table 6.13 Usages rate of walking among residents of different areas (%) City

Urban

Townships Commuting

Villages Shopping

To town centre

To county seat

Beijing

–

26.17

37.36

0.00

0.00

Shanghai

24.80

32.24

39.83

3.67

0.00

Chongqing

–

39.34

74.79

25.61

2.59

Nanchang

–

74.88

83.87

8.70

0.00

Lanzhou

77.40

50.00

66.33

13.75

0.00

Taiyuan

–

70.59

83.21

15.37

0.00

Nanning

–

26.67

67.65

3.65

0.00

Taizhou

–

25.00

44.57

5.60

0.00

Baoji

30.80

13.58

12.05

5.26

0.00

Qiqihar

12.94

17.78

9.52

0.00

0.00

Zibo

28.71

13.13

22.11

0.00

0.00

less popular. Meanwhile, essential facilities in village areas are sometimes insufficient, so villagers may have to take buses for long-distance travel to purchase daily supplies. Therefore, it is necessary to improve the supply of public transport facilities in village areas.

6.4 City Level: Taking the Megacity Beijing as the Case

199

6.4 City Level: Taking the Megacity Beijing as the Case 6.4.1 Data In this section, we apply data from the third (2005), fourth (2010) and fifth (2015) survey results in Beijing’s Comprehensive Survey of City Traffic Issues. In 1986, Beijing launched its first city-wide comprehensive survey of traffic issues. It conducted the second, third, fourth and fifth travel surveys in 2000, 2005, 2010 and 2015, respectively. The survey provides a good database for the planning and management of the transport system in Beijing, and details about the main contents of this survey project were introduced in the third section of Chap. 1. Here, we provide an overview of the basic survey results for each year, and the statistical characteristics follow. 1) Survey results in 2005 a. Spatial range In 2005, the final sample included 81,760 households, of which 68,680 households came from the central urban area (consisting of eight districts1 ), accounting for 84.00%. Another 10,560 households came from the suburbs (consisting of six districts2 ), accounting for 12.92%. Finally 2,520 households came from the outer suburbs (consisting of four districts3 ), accounting for 3.08%. The sample involved all 18 (later 16) districts and counties in Beijing, and the sampling ratio of each district was basically consistent with the population base. b. Survey time The survey took place from October 25 to December 31, 2005. The distribution characteristics of the survey time during the week are in Fig. 6.17. The sample sizes for each survey day in the week were relatively balanced. Thus the travel information is quite random and comprehensive in terms of the collection time. These survey results can be regarded as a reflection of the daily travel characteristics of residents in Beijing. c. Population composition Of the surveyed household members, 102,530 were male and 105,760 were female. The proportions of males and females in the total sample were 49.22% and 50.78%, 1

In 2005, the central urban area in Beijing consisted of eight districts. Later the Xuanwu District was merged into the Xicheng District, and the Chongwen District was merged into the Dongcheng District. So the original eight districts are now six districts, namely Xicheng, Dongcheng, Chaoyang, Haidian, Fengtai and Shijingshan. 2 In this section, the suburbs of Beijing refers to six districts: Tongzhou, Shunyi, Daxing, Fangshan, Mentougou and Changping. 3 In this section, the outer suburbs of Beijing consist of four districts: Pinggu, Yanqing, Huairou and Miyun.

200 Table 6.14 Sample sizes and proportions of each income level in 2005

6 Travel Differences Between the Urban and Rural Population Income (CNY/month)

Sample size (Households)

Proportion (%)

1,500 or below

13,740

16.81

1,500–2,500

21,408

26.18

2,500–3,500

18,330

22.42

3,500–5,500

17,815

21.79

5,500–10,000

8,088

9.89

10,000–20,000

947

1.16

20,000–30,000

133

0.16

30,000 or above

88

0.11

Unknown

1,211

1.48

respectively. Thus, there were fewer males than females. However, the gender ratios in each district were balanced. Most surveyed residents in the sample were young or middle-aged, ranging from 26 to 55 years old. These residents accounted for 56.04% of the total. Residents over 65 years old accounted for only 12.96%. Some 74.23% of participants were registered in Beijing and lived in the district in which they were registered. Another 16.62% were registered in Beijing but lived in other districts. Permanent residents without Beijing registrations accounted for 9.03%, while 0.12% of the surveyed residents came from other cities and were living in Beijing temporarily. d. Household attributes The average household size was 2.55. The monthly incomes of households were mainly in the range of 1,500–5,500 CNY/month, which accounted for more than 70% of the participants. The sample sizes and proportions of each income range are in Table 6.14. e. Vehicle ownership Just 24,657 of the surveyed households owned at least one private car, while 57,103 households did not own a car. The car ownership rate was 29.54% (the ownership rate is the total household number divided by the number of households with cars). Among those with private cars, 98.23% said they owned only one car, and fewer than 2% of households said they owned two or even more cars. The average number of private cars per household was 1.02. The ownership of common vehicles among the surveyed households and the average number are in Table 6.15.

6.4 City Level: Taking the Megacity Beijing as the Case Table 6.15 Ownership of common vehicles and average number per household in 2005

201

Vehicles

Ownership rate (%) Average number per household

Private cars

30.16

1.02

Pedal bicycles

77.78

1.71

4.06

1.58

Electronic bicycles

2) Survey Results in 2010 a. Spatial range In 2010, the final sample included 46,900 households. Of them, 33,121 households came from the central urban area (consisting of six districts4 ), accounting for 70.62% of the participants. Another 11,240 households came from the suburbs (consisting of six districts5 ), accounting for 23.96%. Finally, 2,540 households came from the outer suburbs (consisting of four districts6 ), accounting for 5.42%. The sample involved all 16 (originally 18) districts in Beijing, and the sampling ratio of each district was basically consistent with the population base. b. Survey time The survey took place from September 7 to October 31, 2010. The distribution characteristics of the survey time during the week are in Fig. 6.18. The sample sizes of each survey day in the week were relatively balanced. c. Population composition Of the surveyed household members, 55,577 were male and 60,565 were female. The proportions of males and females in the total sample were 47.85% and 52.15%, respectively. Thus, there were fewer males than females. However, the gender ratios in each district were quite balanced. Most surveyed residents in the sample were young or middle-aged, ranging from 26 to 55 years old. These residents accounted for 52.70% of the total. Residents over 65 years old accounted for 15.65%. This proportion is higher than that in 2005, and it seems that the aging problem is becoming more serious in Beijing. Some 72.31% of residents were registered in Beijing and lived in the district in which they were registered. Another 10.06% were registered in Beijing but lived in other districts. Permanent residents without Beijing registrations accounted for 17.37%, while 0.26% of the surveyed residents came from other cities and were living in Beijing for a short time. 4

By 2010, the Xuanwu District had been merged into the Xicheng District, and the Chongwen District had been merged into the Dongcheng District. So the original eight districts in 2005 were then six districts, namely Xicheng, Dongcheng, Chaoyang, Haidian, Fengtai and Shijingshan. 5 In this section, the suburbs of Beijing refers to six districts: Tongzhou, Shunyi, Daxing, Fangshan, Mentougou and Changping. 6 In this section, the outer suburbs of Beijing consists of four districts: Pinggu, Yanqing, Huairou and Miyun.

202

6 Travel Differences Between the Urban and Rural Population

Monday

Tuesday

Wednesday

Thursday

Friday

Saturday

Sunday

120 100

%

80 60 40 20 0 First time

Second time

Survey time Fig. 6.18 Proportions of participants for each survey day in 2010

d. Household attributes The average household size was 2.47. The average number of employed members was 1.18 per household. Some 65.00% of households had annual incomes of less than 50,000 CNY, and 27.58% of households had annual incomes of more than 50,000 CNY but less than 100,000 CNY. Among the surveyed households in 2010, the annual household incomes were mainly below 100,000 CNY (accounting for 92.58%) (Table 6.16). Table 6.16 Sample sizes and proportions of each income level in 2010

Income (CNY/year)

Sample size (Household)

Proportion (%)

50,000 or below

30,484

65.00

50,000–100,000

1,2935

27.58

100,000–150,000

2,331

4.97

150,000–200,000

674

1.44

200,000–250,000

212

0.45

250,000–300,000

103

0.22

300,000 or above

147

0.31

14

0.03

Unknown

6.4 City Level: Taking the Megacity Beijing as the Case Table 6.17 Ownership of common vehicles and average number per household in 2010

203

Vehicles

Ownership rate (%) Average number per household

Private cars

28.86

1.08

Pedal bicycles

63.04

1.53

Electronic bicycles 13.99

1.10

e. Vehicle ownership Just 13,537 households owned at least one private car. In addition, 983 said they had two cars, and 51 had at least three cars. The car ownership rate was 28.86%. The average number of private cars per household was 1.08, which was a little bit higher than that in 2005. The ownership of common vehicles among the surveyed households and the average numbers are in Table 6.17. 3) Survey results in 2015 a. Spatial range In 2015, the final sample included 40,003 households. Of them, 23,276 households came from the central urban area (consisting of six districts7 ), accounting for 58.19% of the participants. Another 14,027 households came from the suburbs (consisting of six districts8 ), accounting for 35.06%. Finally, 2,700 households came from the outer suburbs (consisting of four districts9 ), accounting for 6.75%. The sample involved all 16 districts in Beijing, and it involved 667 transport zones. b. Survey time The survey took place from September 4 to November 27, 2014, with National Day holidays avoided. The distribution characteristics of the survey time during the week are in Fig. 6.19. Except for a few days (such as Monday and Tuesday in the collection round), the sample sizes of each survey day in the week are relatively balanced. c. Population composition Of the surveyed household members 49,253 were male and 52,562 were female. The proportions of males and females in the total sample were 48.37% and 51.63%, respectively. Most surveyed residents in the sample were young or middle-aged, ranging from 26 to 55 years old. These residents accounted for 69.29% of the total. Residents over 65 years old accounted for 18.67%. This proportion has grown higher than that in 2010 by 1.22 percentage points. It seems that the aging problem is 7

In this section, the central urban area in Beijing refers to Xicheng, Dongcheng, Chaoyang, Haidian, Fengtai and Shijingshan. 8 In this section, the suburbs of Beijing refers to six districts: Tongzhou, Shunyi, Daxing, Fangshan, Mentougou and Changping. 9 In this section, the outer suburbs of Beijing consists of four districts: Pinggu, Yanqing, Huairou and Miyun.

204

6 Travel Differences Between the Urban and Rural Population

Monday

Tuesday

Wednesday

Thursday

Friday

Saturday

Sunday

120 100

%

80 60 40 20 0 First time

Second time

Survey time Fig. 6.19 Proportions of participants for each survey day in 2015

becoming more serious in Beijing. Some 78.41% of participants were registered in Beijing and lived in the district in which they were registered, higher than that in 2010 by 6.10 percentage points. Another 6.83% were registered in Beijing but lived in other districts. The surveyed residents without Beijing registrations accounted for 14.59%. d. Household attributes The average household size was 2.55. In 2015, 34.22% of households had annual incomes of less than 50,000 CNY, and this proportion had decreased to almost half of that in 2010. 42.7% of households had annual incomes of more than 50,000 CNY but less than 100,000 CNY, which was a much larger proportion than that in 2010. According to the survey results, household income had clearly grown significantly from 2010 to 2015 (Table 6.18). e. Vehicle ownership Some 16,998 households owned at least one private car. In addition, 2,494 said they had two cars (accounting for 14.67%), and 104 had at least three cars (accounting for 0.61%). The car ownership rate was 42.49%. The average number of private cars per household was 1.17, higher than that in 2010. The ownership of common vehicles among the surveyed households and the average number are in Table 6.19.

6.4 City Level: Taking the Megacity Beijing as the Case Table 6.18 Sample sizes and proportions of each income level in 2015

Table 6.19 Ownership of common vehicles and average number per household in 2015

205

Income (CNY/year)

Sample size (Household)

Proportion (%)

50,000 or below

13,690

34.22

50,000–100,000

17,080

42.70

100,000–150,000

5,974

14.93

150,000–200,000

1,976

4.94

200,000–250,000

656

1.64

250,000–300,000

323

0.81

300,000 or above

202

0.50

Unknown

102

0.25

Vehicles

Ownership rate (%) Average number per household

Private cars

42.49

1.16

Pedal bicycles

61.51

1.43

Electronic bicycles 26.22

1.16

6.4.2 Methods In this section, we take the megacity of Beijing as an example to explore the urban– rural differences in the travel characteristics of residents, based on a series of citylevel travel surveys conducted in 2005, 2010 and 2015. The surveyed residents are divided into three categories based on our comprehensive consideration of where they live, the travel distance to the city centre, and the characteristics of Beijing’s circular transport network. These three categories are residents of the central urban area (Dongcheng, Xicheng, Chaoyang, Haidian, Fengtai and Shijingshan districts), residents of the suburbs (Tongzhou, Shunyi, Daxing, Fangshan, Changping and Mentougou districts), and residents of the outer suburbs (Pinggu, Yanqing, Miyun and Huairou districts). Figure 6.20 shows the spatial distribution of these areas. Comparisons among the three groups of residents in terms of their travel behaviour and travel preferences follow. For the analysis of residents’ travel characteristics, we mainly applied descriptive statistics, which refers to a suite of statistics that summarise the characteristics and distribution of a set of data values (Lee, 2020). The advantages and the common usage of descriptive statistical analysis are described in detail in the previous section of this chapter. Here, it is mainly applied to the data from the BCTS to analyse the diverse travel behaviour characteristics of different groups in different areas of a megacity, on the basis of which the relationship between regional urban–rural structure and travel behaviour as well as transport demand is further analysed. Based on information about 24 h travel chains from the survey, we divided the daily travel chains of residents during one whole day into several trips, which we defined

206

6 Travel Differences Between the Urban and Rural Population

Fig. 6.20 Central area, suburbs and outer suburbs of Beijing

as one-way movements in which the traveller takes a certain mode of transport to move from one place to another. One trip usually consists of stays and movements, which may include multiple transfers or changes in travel modes. In this section, a complete trip is one in which there are no further transfers, and the traveller has arrived at the destination. Analyses are mainly centred on the surveyed residents’ vehicle ownership, trip frequency, mode choice, travel time and travel purpose, as well as socioeconomic attributes that are possibly relevant to travel behaviour.

6.4.3 Analysis 1) Differences in travellers’ socioeconomic attributes a. More significant aging in the central area According to the survey results from 2005 to 2015, the proportions of young and middle-aged people aged 25–65 remained fairly stable over this period, and there

6.4 City Level: Taking the Megacity Beijing as the Case Central area

Suburbs

Outer suburbs

Central area

30 25

%

%

20 15 10 5 0

2005

2010

Year

2015

207

20 18 16 14 12 10 8 6 4 2 0

2005

Suburbs

2010

Outer suburbs

2015

Year

Fig. 6.21 Proportions of young people under 25 and older people over 65 in different areas

was not much difference between the central area, suburbs and outer suburbs. The proportion of young people under the age of 25 remained higher in the outer suburbs but lower in the central area, while the proportion of people over 65 years old remained higher in the central area but lower in the outer suburbs. In terms of changes over time, the proportion of teenagers declined overall, while the proportion of older people rose slightly (Fig. 6.21). Although there are significant differences in the child dependency ratio and the older dependency ratio of residents in the suburbs of Beijing, the aging trend seems to be common and widespread. In 2005, there were some differences in age structure among residents in the central area, the suburbs and the outer suburbs. The differences were mainly reflected in the higher proportion of teenagers in the outer suburbs (25.52%) and the higher proportion of older people in the central area (13.88%). Specifically, the proportion of teenagers in the central area was quite close to that in the suburbs, while the proportion of older people in the suburbs and the outer suburbs were quite close. However, there was no significant difference in the sex ratios, which were 97.03 in the central area, 97.74 in the suburbs and 91.89 in the outer suburbs. In 2010, the age structure and differences among those three areas remained largely unchanged. The proportions of young and middle-aged people were 67.83%, 69.92% and 68.01%, respectively. The proportion of teenagers in the outer suburbs (20.64%) was still higher than in other areas, and that of older people in the central area (15.65%) was also still higher than other areas. Meanwhile, it seemed that the proportion of teenagers reduced and that of older people increased from 2005 to 2010. The proportion of older people among the surveyed residents in central area had increased to 15.65% by 2010. As the same time, gender structure had also shown some changes. The gender ratios in the central area and the suburbs were 91.74 and 93.41, respectively in 2010, while that in the outer suburbs had dropped significantly to 84.94. By 2015, the proportions of young and middle-aged residents in the three areas had increased slightly, while those of teenagers had all dropped slightly and those of older people had all increased slightly. As a result, the difference in the age structure among the central area, the suburbs and the outer suburbs has become more significant. These differences in residents’ characteristics might lead them to different travel behaviour.

208

6 Travel Differences Between the Urban and Rural Population

b. Lower levels of education in the suburbs In 2010, 23.99% of residents in the central area had a bachelor’s degree or higher, while in the outer suburbs, it was only 10.33%. In 2015, this difference in education level remained. Residents with a bachelor’s degree or higher in the central area accounted for 25.63% of the surveyed residents, while in the outer suburbs, it was only 13.56%. The distribution of residents’ highest education levels is in Figs. 6.22 and 6.23. It shows that there are significant and continuous gaps among different areas in Beijing in terms of people’s education levels. There are various reasons for this. On the one hand, there are relatively more adequate and higher quality education resources in the central area. Universities and research institutes in the city are usually located near the city centre, leading to the formation of university towns. As a result, more people with high education levels (such as researchers, teachers, university students etc.) may live and work in the central area. On the other hand, the central area has more abundant opportunities for employment and entrepreneurship, attracting a large number of highly educated talents to settle down. The uneven distribution of city functions and social resources makes the education levels of residents quite different in the central area and the suburbs. c. Labour participation between central area and suburbs The employment rates among residents in the central area, the suburbs and the outer suburbs were very similar (45.99%, 45.58% and 46.10%, respectively). Meanwhile, the proportion of retirees in the central area appeared to be much higher than that

Primary school or below

Junior high

College or above

Uneducated

Senior high

50 45 40 35

%

30 25 20 15 10 5 0 Central area

Suburbs

Area Fig. 6.22 Education levels in different areas in 2010

Outer suburbs

6.4 City Level: Taking the Megacity Beijing as the Case

Primary school or below

Junior high

College or above

Uneducated

209

Senior high

60 50

%

40 30 20 10 0 Central area

Suburbs

Outer suburbs

Area Fig. 6.23 Education levels in different areas in 2015

in the outer suburbs. This gap is tending to become larger over time. In 2005, the proportion of retirees in the central area was 27.87%, while that in the suburbs was only 10.60%. In 2010, the proportion of retirees in the central area had risen to 32.20%, while the proportion of retirees in suburbs was still only 14.07%. In 2015, this employment rate gap remained significant. The share of retirees in the central area kept increasing, while that in the other two areas dropped to 20.57% and 12.69%, respectively (Table 6.20). In addition, there seemed to be a higher proportion of housewives in the outer suburbs, who took on the responsibility of doing housework and taking care of other family members. In 2015, only 2.30% of residents in the central area said all they had to do was housework, while this proportion was as high as 8.62% in the outer suburbs. This shows that there were significant differences among different areas in terms of the social roles people perform. This difference is also likely to lead to differences in travel demand. Table 6.20 Proportions of retirees among the total sample

Year

Central area (%)

Suburbs (%)

Outer suburbs (%)

2005

27.87

20.34

10.60

2010

32.20

22.90

14.07

2015

35.28

20.57

12.69

210

6 Travel Differences Between the Urban and Rural Population

2) Differences in vehicle ownership and usage rate a. Car ownership rate According to the survey results, in recent years, the proportion of households with cars has risen, after a period of falling. Taking the central area as an example, in 2005 only one third (29.54%) of households owned a private car. In 2010, this proportion dropped slightly to 27.97%. By 2015, 40.90% of households owned a car. In addition, the car ownership rate in the outer suburbs appeared to be the highest, at 49.11%. Among households with cars, the average number of cars was similar in different areas of Beijing. In the central area, for example, in 2005 the average number of cars owned by each household was 1.02. Among those having private cars, most households owned only one. In 2015, the average number of cars owned by each household rose to 1.14, and many households owned two, three, or even four cars for the various travel demands of their family members. The previous literature suggests that the ownership of private cars might be influenced by multiple factors. The location of residence affects car ownership. Residents who hold rural Hukous have fewer cars than urban Hukou residents, even if they live in urban areas (Li & Zhao, 2017). More specifically, the longer the distance between residents’ homes and the central business district (McCormack et al., 2001), job or business centre (Ewing & Robert, 2010; Potoglou & Kanaroglou, 2008) and public transport (Potoglou & Kanaroglou, 2008) the more likely residents were to own private cars. This may help to explain why the car ownership rate in the outer suburbs of Beijing is higher than that in the central area. Since the population and facilities are both at a lower density in the suburbs, residents living away from the city centre have to travel further to access workplaces, shopping centres and other services, resulting in a greater need for driving. In addition, many studies have shown the significant influence of car ownership on people’s travel mode choice (Tiikkaja & Liimatainen, 2021; Yu & Zhao, 2021). Generally, owning more cars encourages people to drive more often instead of taking a bus (Figs. 6.24 and 6.25). The higher car ownership rate in the outer suburbs might lead to higher usage of private cars for daily trips, which is further explored in the following analysis. b. Car mileage and monthly fuel costs There have been differences in the distance travelled per car in the past 12 months among the central area, suburbs, and outer suburbs of Beijing. This distance is slightly higher in the suburbs than in the outer suburbs. The average distance travelled per car in the suburbs was 17,522.49 km in 2010, while in the central area and outer suburbs, it was much lower, at less than 15,000 km. In terms of the median value, the distance travelled per car in the suburbs in 2010 was 12,000 km, while in both the central area and the outer suburbs, it was 10,000 km. By 2015, this gap had significantly narrowed. The average distance travelled per car in the central area, suburbs and outer suburbs were 5,5223.48 km, 54,905.01 km and 55,589.54 km, respectively. It seemed that there was almost no difference between the three areas (Tables 6.21 and 6.22).

6.4 City Level: Taking the Megacity Beijing as the Case

Central area

Suburbs

211

Outer suburbs

60 50

%

40 30 20 10 0 2005

2010

2015

Year Fig. 6.24 Proportions of households with at least one car

Central area

Suburbs

Outer suburbs

1.25 1.2

Number of car

1.15 1.1 1.05 1 0.95 0.9 2005

2010

Year Fig. 6.25 Average number of cars owned by each household

2015

212 Table 6.21 Average distance travelled by one car in the past 12 months (Unit: km)

Table 6.22 Median distance travelled by one car in the past 12 months (Unit: km)

6 Travel Differences Between the Urban and Rural Population Year

Central area

Suburbs

Outer suburbs

2010

14,639.97

17,522.49

14,455.64

2015

55,223.48

54,905.01

55,589.54

Year

Central area

Suburbs

Outer suburbs

2010

10,000

12,000

10,000

2015

39,000

37,000

35,000

The average fuel cost per car per month is another indicator of the usage of cars. In 2010, the average fuel cost per car in the outer suburbs was significantly less than that in the central area and suburbs. The fuel cost for one car was 1,336.53 CNY/month in the central area, and that in the suburbs was 1,296.79 CNY/month. In the outer suburbs, however, one car only cost 680.23 CNY/month for the fuel. By 2015, the fuel cost per car in the outer suburbs had increased slightly to 738.22 CNY/month, while that in the central area and suburbs had dropped to 807.12 CNY/month and 862.53 CNY/month, respectively. In general, the difference in fuel cost per car among those areas has narrowed (Figs. 6.26, 6.27 and 6.28). The usage habits of cars may also have changed over the years. c. Ownership of public bus cards From 2005 to 2015, residents of the central area appeared more likely to own discount cards for buses. In 2010, only 9.85% of residents in the central area did not have discount cards for buses, while in the outer suburbs, it was 31.58%. The difference in the popularity of bus discount cards indicated that Beijing’s public transport system was more commonly used in the central area in 2005. Since fewer public transport services were available in the outer suburbs, residents living there may not have adequate access to public transport. By 2015, the operation and management of Beijing’s public transport system had been largely improved and optimised, and it was more convenient to take a public

Fig. 6.26 Average monthly fuel cost per car in 2010 and 2015 for the central area

6.4 City Level: Taking the Megacity Beijing as the Case

213

Fig. 6.27 Average monthly fuel cost per car in 2010 and 2015 for the suburbs

Fig. 6.28 Average monthly fuel cost per car in 2010 and 2015 for the outer suburbs

bus in the outer suburbs. The proportion of residents without a bus discount card had dropped to 24.60%. At the same time, as more diverse travel modes became popular in the central area (such as shared bicycles and online-booked taxis), fewer people still owned bus discount cards. There were differences in the types of bus discount cards among the three areas. The proportion of residents holding senior citizens’ discount cards in the central area was significantly higher than that in the outer suburbs. This gap increased from 5.75% in 2010 to 7.24% in 2015 (Figs. 6.29 and 6.30). This might be related to the higher proportion of older people in the central area. Evidence has shown that promoting bus cards can help to encourage the use of public transport in cities. Fujii and Kitamura (2003) carried out an experiment to explore the relationship between discount availability and the frequency of using public buses. Their results showed that when drivers received a 1 month free bus ticket, their attitudes towards buses became more positive and the frequency of bus use increased markedly. Similarly, when this experiment was carried out on a wider sample including 1,000 drivers, it was also effective. Receiving a free 1-month bus card significantly changed people’s habits on mode choice and increased their use of public transport (Thøgersen & Møller, 2008). Discussions on the effectiveness of policy options in motivating travellers to choose buses instead of cars have further

214

6 Travel Differences Between the Urban and Rural Population

All-in-one card

Old-people discount card

Worker discount card

No bus card

100 90 80 70

%

60 50 40 30 20 10 0 Central area

Suburbs

Outer suburbs

Area Fig. 6.29 Proportions of ownership of different types of bus card in 2010

All-in-one card

Old-people discount card

Worker discount card

No bus card

100 90 80 70

%

60 50 40 30 20 10 0 Central area

Suburbs

Outer suburbs

Area Fig. 6.30 Proportions of ownership of different types of bus card in 2015

6.4 City Level: Taking the Megacity Beijing as the Case

215

proved this effect. Aizezi et al. (2017) focused on the travel patterns of students and pointed out that encouraging students to apply for bus cards can improve the proportion choosing to travel by bus. It is obvious that using a bus fare concession strategy could guide people to choose bus travel (Guan & Li, 2019). In addition to discount cards aimed at certain groups, other measures for optimising the monetary cost of public transport are needed to reduce car dependency. For example, some studies have shown that distance-based fares can benefit low-income, older and disadvantaged populations (Farber et al., 2014). The usage of bus discount cards has been an ongoing factor in China’s public transport development. By allowing some certain groups of people to pay less for taking a bus, bus discount cards can be effective for promoting public transport. According to the survey results in Beijing, the ownership rate of bus cards is still low in the suburbs and outer suburbs. Nearly one fifth of residents hold no bus discount card. Meanwhile, subways are less available in the outer suburbs area, and people usually have to choose between public buses and private car when taking longdistance trips to the city centre. In this context, promoting bus cards and relative discount measures for people living far away from the city centre is necessary. d. Parking problems The number of private car parking spaces in the suburbs of Beijing has depended mainly on the size of car parks in residential quarters. The second most commonly used parking space was the roadside, which may not comply with the regulations. Illegal and disorderly parking along the roadside is still common in Beijing, and there is a lack of parking space in the central city, the suburbs, and the outer suburbs. The problem has become more serious since 2005. More households tend to park their cars along the roadside, which can lead to the obstructed and untidy roads. In addition, the data show that the proportion of parking inside industrial complexes has gradually declined from 2005 to 2015. This change was particularly obvious in the central area. In 2005, 20.47% of cars were parked inside industrial complexes, while that had dropped to 1.81% in 2015, a reduction of 91.16%. In China, an industrial complex is a special type of living mode. In the early days of the People’s Republic of China, most employed workers in cities lived inside or around the buildings of national agencies and institutions. Thus these complexes were both places for leading the development of the country and places where many workers lived. As a special type of social organisation and living mode, industrial complexes acted as agents for city’s administration and social management. Industrial complexes may have a significant influence on the spatial order of the city (Zhang & Chai, 2009). The industrial complex has been the dominant type of residential unit in Beijing since the founding of the People’s Republic of China. There are three basic categories of industrial complexes: those housing government agencies, enterprise units and public institutions. In addition, the enterprise units include industrial, commercial, and service enterprises. Public institutions include institutions for administration, education, medicine, etc. During the market economy period, with the reform of national enterprises and the process of the marketisation of the Chinese economy, society became freer and more flexible. There tended to be

216

6 Travel Differences Between the Urban and Rural Population

fewer and fewer industrial complexes in China’s cities (Xiao & Chai, 2012). Modern residential zones have gradually replaced the traditional living mode of industrial complexes. Accordingly the living activities of residents rely more on residential zones instead. As a result, the parking demand of households is mainly in modern residential zones. In 2015, almost 70% of the cars in Beijing were parked in modern residential units (Figs. 6.31, 6.32 and 6.33). According to these survey results and this analysis, there are differences in the usage of cars and public transport among difference areas in Beijing. More specifically, residents living in or near the central area tend to be more likely to own a private car, and to pay more for fuel. At the same time, residents in the central area tend to be more likely to own a bus discount card; thus, they can get more discounts while traveling by bus. However, these differences in travel behaviour among areas tend to shrink gradually. It is likely that the accessibility and mobility will become more balanced and equal among the central area, suburbs and outer suburbs. 3) Differences in trip frequency

%

According to the surveys, trip frequency (the average number of trips for the surveyed residents taking at least one trip) of residents in Beijing has shown a gradually decreasing trend (Fig. 6.34). By 2015, the number of trips had dropped to only 2.84 per day for residents living in the central area, compared with 3.57 in 2005. For residents of the outer suburbs, trip frequency among residents with travel activities dropped from 4.21 per day in 2005 to 3.41 per day in 2015. At the same time, the non-travel rate (the proportion of residents who did not make any trips during the survey day) in Beijing increased over the same period. More residents may just choose to stay at home if there is no rigid travel demand (such as work or school).

Roadside (with line)

Roadside (without line)

Under the overpass

Besides public infrastructure

Off-street

Residential quarters

Institution Yard

Others

100 90 80 70 60 50 40 30 20 10 0 Central area

Suburbs

Area

Fig. 6.31 Proportions of different types of parking spaces in 2005

Outer suburbs

%

6.4 City Level: Taking the Megacity Beijing as the Case

217

Roadside (with line)

Roadside (without line)

Under the overpass

Parking lot of public infrastructure

Off-street

Residential quarters

Institution Yard

Others

100 90 80 70 60 50 40 30 20 10 0 Central area

Suburbs

Outer suburbs

Area

%

Fig. 6.32 Proportions of different types of parking spaces in 2010

Roadside (with line)

Roadside (without line)

Under the overpass

Parking lot of public infrastructure

Off-street

Residential quarters

Industrial complex

Others

100 90 80 70 60 50 40 30 20 10 0 Central area

Suburbs

Outer suburbs

Area

Fig. 6.33 Proportions of different types of parking spaces in 2015

In 2010, the non-travel rates of the central area, the suburbs and the outer suburbs were 15.89%, 16.35% and 14.52%, respectively. By 2015, the rates had all increased significantly for these three areas, to 23.46%, 25.65% and 28.52%, respectively. It seems that people prefer to make fewer trips now. Also, the non-travel rate in the outer suburbs exceeded that in the central area. Other researchers have obtained similar results. Based on a phone survey conducted across the province of Alberta, Canada,

218

6 Travel Differences Between the Urban and Rural Population

Central area

Suburbs

Outer suburbs

4.5 4 3.5

Times

3 2.5 2 1.5 1 0.5 0 2005

2010

2015

Year Fig. 6.34 Average trip frequency among those who have made at least one trip

Shirgaokar et al. (2020) found that rural residents were more likely not to take trips than those in cities. One possible reason for that is lack of transport to various trip destinations. Meanwhile, there is still a significant gap in the trip frequencies of residents of different areas of Beijing. The survey shows that the farther away from the city centre, the more times residents travel in a day. The closer to the city centre, the fewer trips residents have. In the city centre, the higher travel costs and the greater travel obstacles (such as traffic congestion) might encourage people to simplify their travel chains. At the same time, advances in network technology and the popularisation of online shopping have enabled many people to access livelihood opportunities without leaving home. For example, the complete logistics and delivery system in the central area has spawned a series of straight-to-home services in recent years, such as delivering vegetables, meat or medicines straight to home. As a result, people’s travel demand for shopping and entertainment may be weaker, while for people living far from the city centre, such as in the suburbs and the outer suburbs, the demand for shopping and basic facilities still largely relies on actual trips. This difference in trip frequency between the central area and the suburbs seems consistent with previous studies. Based on a mid-1989 mail-back survey of individuals residing in selected Chicago suburbs, Prevedouros and Schofer (1991) found that residence in the outer suburbs implies more local trips due to the lower population density and more employment opportunities. Although the city structure and social environment differ between the United States and China, one can reliably attribute the different trip frequencies between city centre and suburbs to built environment factors.

6.4 City Level: Taking the Megacity Beijing as the Case

219

4) Differences in mode choice According to the survey, walking, bicycling, driving cars and taking buses are the most common modes for daily trips, and the share of walking in daily trips has remained steady at more than one third. In recent years, the share of bus and bicycle use for traveling has declined slightly, while that of cars has risen slightly. At the same time, there are still some differences in mode preference among different areas. Residents in the central area were more likely to choose public transport such as buses and subways than residents in the outer suburbs, while residents in the outer suburbs seemed to be more likely to use bicycles, electric bicycles and just walking. In 2015, for example, the proportion of bicycle as travel mode in the outer suburbs had reached 19.06%, while only 11.09% of the journeys in the central area were made by bicycle. Buses (21.79%) and private cars (14.79%) had more important positions in their travel activities (Figs. 6.35, 6.36 and 6.37). A comparison of the statistical results in 2005, 2010 and 2015 reveals that that the differences in people’s preference for various travel modes have been changing over time. In 2010, the difference between the proportions of bicycles use for traveling between the central area and the outer suburbs was up 12.34 percentage points. By 2015, that difference fell to 7.97 percentage points, indicating that bicycles have become a common travel mode in all areas in Beijing. Meanwhile, the difference in the proportions of those taking public transport for traveling has remained still significant. For public buses, 21.81% of residents in the central area chose bus as their travel mode in 2010, while the proportion in the suburbs was as low as 17.33%. In the

2005

2010

2015

70 60 50

%

40 30 20 10 0 Walking

Bicycle

E-bicycle

Subway

Travel mode Fig. 6.35 Composition of mode choices in the central area

Bus

Car

Others

220

6 Travel Differences Between the Urban and Rural Population

2005

2010

2015

70 60 50

%

40 30 20 10 0 Walking

Bicycle

E-bicycle

Subway

Bus

Car

Others

Bus

Car

Others

Travel mode Fig. 6.36 Composition of mode choices in the suburbs

2005

2010

2015

70 60 50

%

40 30 20 10 0 Walking

Bicycle

E-bicycle

Subway

Travel mode Fig. 6.37 Composition of mode choices in the outer suburbs

6.4 City Level: Taking the Megacity Beijing as the Case

221

outer suburbs, that proportion was only 6.35%. By 2015, 21.79% of residents in the central area chose bus as their travel mode, while this proportion in the suburbs and outer suburbs remained at a relatively low level, namely 15.48% and 6.86%, respectively. For the usage of subways, there was a 5.02 percentage point difference between residents in the central area and the outer suburbs. By 2015, more residents in the central area were taking the subway for traveling, while that proportion in the outer suburbs remained quite low, at only 0.69%. As a result, the urban–suburban gap in the usage of subways increased to 9.95 percentage points. The results show that the difference in mode choice among different areas in Beijing is still significant, especially in terms of the usage of public transport. It seems that difference in travel modes between central areas, suburbs and outer suburbs is mainly in the usage of public transport. For megacities such as Beijing, residents living in the core area seem to rely more on buses for daily trips. However, some previous studies have drawn different conclusions. Using a face-to-face survey data in Fuzhou, Fujian province in China, Du et al. (2020) explored the travel characteristics of older people in the core area and the suburbs. The results show that older people in the suburbs are more dependent on buses than those in the core area. There are several possible reasons for the different findings. On the one hand, as a typical megacity in China, Beijing has a more complex city structure and more obvious dualistic phenomenon between the city centre and the suburbs than other medium and small cities. The supply of bus services is concentrated mainly in the central area, leading to less access to public transport for those living in the suburbs. On the other hand, age is a significant factor in travellers’ mode choice (Liyanage & Kumarage, 2008; Pucher & Renne, 2005). For older people, most long-distance trips may involve taking a bus since driving alone is usually unrealistic. In our analysis, the proportion of older people in the central area was higher than that in the suburbs and outer suburbs. It is reasonable that older travellers will have higher travel demands by public transport. What is more, the higher car ownership rate in the outer suburbs might also be responsible for the lower usage of public buses. To promote public transport, strategies for the central area should focus on improving the quality of services, especially for older people, while strategies for the suburbs should first consider the accessibility and convenience of public transport to reduce residents’ dependence on private cars. 5) Differences in travel time In general, the completion of a trip is often accompanied by walking to a transport location and spending some time waiting. This necessary but elastic processes will result in the time required for a trip extending and sometimes getting out of control. According to the survey results in 2010 and 2015, the time required for walking to the station and waiting for a bus/subway/taxi was usually no more than 10 min. In 2010, the average times for walking to a station in the central area, suburbs and outer suburbs were 8.75 min, 9.25 min and 5.03 min, respectively. The average waiting times were 7.46 min, 8.72 min and 5.05 min, respectively. The walking and waiting time in the outer suburbs was significantly less than that in the central area and the suburbs. By 2015, this difference had been significantly narrowed. The time to walk

222 Table 6.23 Average time to walk to a transport station (Unit: minutes)

Table 6.24 Average waiting time at a transport station (Unit: minutes)

6 Travel Differences Between the Urban and Rural Population Year

Central area

Suburbs

Outer suburbs

2010

8.75

9.25

5.03

2015

6.60

6.44

5.42

Year

Central area

Suburbs

Outer suburbs

2010

7.46

8.72

5.05

2015

4.62

5.41

4.50

to a station in the central area, suburbs and outer suburbs were all in the range of 5–6 min. This shows that the time for connecting the starting points/end points with transport stations has become much shorter in all the three areas of Beijing. In central Beijing, the average waiting time dropped from 7.46 min in 2010 to 4.62 min in 2015, a decrease of 38.07%. In terms of the time distribution, in 2010 more than 75% of the time, walking time and waiting time in the central area and suburbs were less than 10 min, while the proportions in the outer suburbs of less than 10 min were as high as 93.53% (for walking) and 93.85% (for waiting). Even more than 75% of the walking (to a station) and waiting can be finished within 5 min. In 2015, the gap between the centre and suburbs had been significantly narrowed, and the vast majority (over 90%) of residents in Beijing could complete walking to a station and/or waiting for a bus within 10 min. The results show that the time for connecting the starting spot or end spot with transport stations was much less in the outer suburbs. Meanwhile, since population density in the central area is much higher and the problem of traffic congestion is more serious, residents have to spend more time (about 5–10 min per trip) on walking to a station and waiting for a bus to come (Tables 6.23 and 6.24). In terms of the total travel time, Beijing has almost achieved the goal of building a 30 min living circle. Among the surveyed residents in Beijing, more than 60% (65.95% in the central area, 73.08% in the suburbs and 89.47% in the outer suburbs) of the trips were completed within 30 min in 2010. Almost 90% (respectively, 86.59%, 86.84% and 96.62%) of the trips were completed within 60 min. Compared with the situation in 2005, the accessibility of residents’ living and commuting in Beijing has been significantly improved in recent years. It seems that total time required for completing a trip in the outer suburbs was shorter than that in the central area according to the surveys conducted in both 2010 and 2015. On the one hand, the time to walk to a station and the waiting time at the station were usually less in the outer suburbs, as the previous analysis has shown. On the other hand, the living circle of the residents in the outer suburbs is relatively compact, and their daily activities mostly took place within a smaller spatial range. As a result, there might be not much demand for long-distance travel, while the spatial range of commuting and other activities in the central area might be much larger, leading to more time cost for residents’ daily travel.

6.4 City Level: Taking the Megacity Beijing as the Case

223

6) Differences in travel purpose In addition, there are differences in the composition of travel purposes among the central area, the suburbs and the outer suburbs, and there have been some changes in recent years. On the proportion of traveling for shopping, the urban–suburban difference greatly narrowed from 2005 to 2010, but it appeared to widen from 2010 to 2015. The travel demand for shopping in the outer suburbs has gradually increased from a low level to exceed that in the central area and the outer suburbs. On the proportions of commuting trips (between home and workplace), the urban–suburban difference has narrowed in recent years. By 2015, the proportions of commuting among all the various travel purposes were 20.95%, 21.99%, and 20.38%, respectively in the three areas in Beijing. There is not much difference between them. On the proportions of entertainment, the urban–suburban difference has narrowed as well. With the rapid development in the outer suburbs, residents have become more likely to go out for entertainment activities during weekends. As a result, the urban–suburban difference in travel purpose and the continuous changes in lifestyles might lead to residents’ demand for transport services becoming more diverse and flexible in the future (Figs. 6.38, 6.39 and 6.40). In general, the pattern of residents’ daily activities during weekends may be a good reflection of their living habits and lifestyles. According to the survey results, residents living in the central area were more likely to travel for play and exercise than those in the outer suburbs. The proportions of playing and exercising in the various travel purposes were 13.85% (in the central area), 8.23% (in the suburbs) and 5.32% (in the outer suburbs), while residents in the outer suburbs were more

Central area

Suburbs

Outer suburbs

40 35 30

%

25 20 15 10 5 0 Work

Back home

Go shopping

Travel purpose Fig. 6.38 Composition of travel purpose in 2005

Visit friends

Entertainment

224

6 Travel Differences Between the Urban and Rural Population

Central area

Suburbs

Outer suburbs

50 45 40 35

%

30 25 20 15 10 5 0 Work

Back home

Go shopping

Visit friends

Entertainment

Travel purpose Fig. 6.39 Composition of travel purpose in 2010

Central area

Suburbs

Outer suburbs

60 50

%

40 30 20 10 0 Work

Back home

Go shopping

Travel purpose Fig. 6.40 Composition of travel purpose in 2015

Visit friends

Entertainment

6.4 City Level: Taking the Megacity Beijing as the Case

225

likely to travel to take classes/learn (15.84%) and for personal affairs (13.06%). This difference between the central area and the suburbs is similar to the conclusions on urban–rural difference in previous studies. Generally, residents in rural areas appear to take fewer trips for leisure activities (Potoglou & Kanaroglou, 2008). Su et al. (2006) compared the major leisure activities of rural and urban older residents in China. They found that rural residents prefer activities indoor in their leisure time, such as playing with children and chatting with relatives, while urban residents prefer activities outdoors, leading to more trips for entertainment. The previous analysis has shown that the ownership rates of bus discount cards and the average money cost per trip both differed between the centre and the suburbs. Residents living in the outer suburbs may pay more attention to meeting their rigid demand for traveling, but they are not that eager to undertake outside activities for entertainment. In the central area however, the better public transport systems and more adequate facilities have made it easier to travel outside for diverse activities on weekends. Different compositions of travel purpose seem to have close correlations with residents’ choices of travel modes as well. According to the survey results in 2010, residents in the central area would prefer to take public buses, followed by bicycles as their travel modes (accounting for 50.49% and 38.86%, respectively) in terms of travel for playing and exercising and for visiting relatives/friends. For travel to pick up others, they would prefer to drive a car. Public transport was more likely to be chosen for entertainment demand. However, since public transport often is not that active and flexible, most personalised travel still relies on private travel modes such as cars (Fig. 6.41).

Car

Bus

Bicycle

Others

100 90 80 70

%

60 50 40 30 20 10 0 Entertainment

Visit friends

Pick up others

Travel purpose Fig. 6.41 Choices of travel modes for different travel purposes in the central area in 2010

226

6 Travel Differences Between the Urban and Rural Population

6.5 Conclusion In this chapter, the relationship between urban–rural attributes and residents’ travel behaviour has been investigated from three perspectives based on the temporal and spatial characteristics of China’s urban–rural structure as summarised in previous sections. To provide comprehensive empirical evidence to learn about and understand the travel behaviour and transport demand in the urban–rural context of China, we firstly focused on the differences in daily travel habits between urban residents and rural residents based on data from CFPS, then on the differences in travel behaviour among residents living in city centres, townships and villages of several typical case cities based on data from the National Detailed Survey of Small Towns and evidence from previous studies. Thirdly, we focused on the differences in travel behaviour among residents living in central area, suburban area and outer suburban area of the typical megacity Beijing based on data from Beijing’s Comprehensive Survey of City Traffic Issues. The commonality of urban and rural residents’ travel behaviour represents the overall characteristics of people’s travel demand in China, and it can serve as a starting point for narrowing the gap between urban and rural areas and promoting regional equity. The difference between urban and rural residents has represented the imbalance in the development of China’s urban and rural transport as well as people’s travel behaviour caused by the urban–rural gap in the social environmental. Based on this difference, the layout of transport services in the urban areas/township/villages, and the central areas/suburbs within a megacity need to be provided differently based on local socioeconomic conditions and local population structure, to promote urban and rural equity, narrowing the regional gap, as well as comprehensively improving the travel satisfaction and life happiness of people. Based on the three types of survey data or statistical data, this chapter has verified that there are indeed many differences between the travel characteristics of urban and rural residents, which mainly include the following aspects: (1) Travellers’ socioeconomic attributes: The differences in travel behaviour of urban and rural residents may largely result from the differences in the individual characteristics of travellers. Research based on survey data from Beijing shows that residents of the central area tend to have a higher dependency ratio for older people and a lower youth dependency ratio, as well as higher rates of car ownership, higher holding rates of public transport cards and higher family income levels. The age structure, gender structure, vehicle ownership and family attributes all have impacts on residents’ travel behaviour to different degrees and from different perspectives. The differences in travellers themselves might be the most essential determinant and cause of differences in travel behaviour. (2) Vehicle ownership and usage rate: The differences in travel opportunities between urban and rural areas can be summarised as differences in vehicle and driving licence ownership, differences in public transport accessibility, and differences in public transport cards holding rates. With the coordinated development of China’s urban and rural areas, the gap between urban and rural, rich and poor has narrowed, and there is no significant difference in the ownership

6.5 Conclusion

227

rate of cars and driving licences. In some areas, the ownership rate of cars in rural areas exceeds that in urban areas, but gaps in the level of the built environment as well as public transport accessibility seem still obvious. The results have shown that rural and suburban residents tend to take public transport mainly for cross-regional and long-distance trips, while some public transport systems in China’s rural areas do not provide enough buses and single lines. As a result, the accessibility and utilisation rate of public transport for the daily short-distance trips of villagers is relatively low, and the gap in travel service quality in urban and rural areas has caused a difference in travel opportunities. (3) Choice of travel mode: Generally, whether travel is divided by urban–rural, city–town–village, or centre–suburban, residents far from the city centre are more likely to walk or use e-bikes, motorcycles and other autonomous modes than those living near the city centre, while residents in urban centres use public transport such as buses and subways/railways at a higher rate. To a certain extent, this difference might be related to the large gap in the development of public transport systems in China’s urban and rural areas. Fast and convenient bus services are mostly provided in cities and central areas with more developed economics and higher densities of population, while access to public transport is often more limited in rural areas. (4) Composition of travel purpose: The main travel purposes of urban and rural residents in China include working, shopping, visiting relatives and friends, medical treatment, and activities for leisure and entertainment. The urban–rural difference in travel purpose is mainly reflected in non-work trips. Advances in the construction of transport networks in urban areas have made it much easier for urban residents to meet their shopping demand through online shopping and door-to-door delivery services, which may greatly reduce their desire for shopping out. In some rural areas in China, the logistics network has not yet been built perfectly and online shopping is still lacking in popularity. Villagers and suburban residents often rely on physical stores or vegetable markets in villages and towns for acquiring daily necessities, increasing their demand for shopping and traveling. In addition, the urban–rural gap in quality level of facilities has made cross-regional trips a ubiquitous travel type in China, mainly cross-regional trips for shopping and medical treatment. (5) Travel time: Travel cost is mainly represented by time cost and money cost. In terms of time cost, the total time spent by urban and rural residents is quite similar, but the time allocation pattern in some areas (such as Beijing) has shown regional gaps. Suburban residents often spend less time on transferring than residents of the central areas. The high level of traffic congestion and the greater travel density in central areas make transfer and connection between different modes of transport an important part of time costs. In terms of money cost, the difference between urban and rural residents might be related to the difference in the holding rate of public transport cards and the preference of mode choice. Higher holding rates of public transport cards make the cost of public transport for most urban residents significantly lower. For suburban or rural residents, the higher utilisation rate of autonomous transport methods such

228

6 Travel Differences Between the Urban and Rural Population

as walking and electric bicycles has reduced the costs of daily short-distance travel. At the same time, the travel behaviour of urban and rural residents in China also shows certain common characteristics, such as car ownership, travel time and distance, popular travel modes, and main travel purposes, indicating that the economic gap between urban and rural residents in China has become much smaller. Over time, the abovementioned travel behaviour differences also have a trend of remaining static or gradually shrinking. Based on the analysis and discussions in this chapter, there are more effective ways of narrowing the urban–rural gap and promoting more balanced development in China. Promoting the construction of a complete rural public transport service system, improving the accessibility of logistics networks in rural areas, increasing the popularity of online shopping for rural residents, and paying attention to the difference in travel demand caused by the structural differences between urban and rural residents are all of great importance for building a harmonious relationship between urban and rural areas from a perspective of transport.

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Yu, Z., & Zhao, P. (2021). The factors in residents’ mobility in rural towns of China: Car ownership, road infrastructure and public transport services. Journal of Transport Geography, 91, 102950. https://doi.org/10.1016/j.jtrangeo.2021.102950. Zhang, Y., Zhao, P., & Lin, J.-J. (2021). Exploring shopping travel behavior of millennials in Beijing: Impacts of built environment, life stages, and subjective preferences. Transportation Research Part a: Policy and Practice, 147, 49–60. https://doi.org/10.1016/j.tra.2021.03.012. Zhang, C., & Chai, Y. (2009). Zhong guo cheng shi dan wei she qu de kong jian yan hua: Kong jian xing tai yu tu di li yong. [The Spatial Dynamic of Danwei Community in Transitional Urban China: Spatial Response and Land Use Renewal]. Urban Planning International, 24(05), 28–32. Zhao, P., & Bai, Y. (2019). The gap between and determinants of growth in car ownership in urban and rural areas of China: A longitudinal data case study. Journal of Transport Geography, 79, 102487. https://doi.org/10.1016/j.jtrangeo.2019.102487.

Chapter 7

Effects of Family Structure on Travel Behaviour

7.1 Literature Review According to previous studies on travel behaviour, many built environment factors (such as the earlier 3Ds and the later 5Ds) (Daniel et al., 2013; Faghih-Imani & Eluru, 2015; Heinen et al., 2010) and socioeconomic factors (such as employment status, education level and family income) (Chakrabarti & Joh, 2019; Deka & Fei, 2019; Elias & Katoshevski-Cavari, 2014) have had a significant influence on travellers’ decision-making. As the starting point and ending point of residents’ daily activities, and the most basic unit of various social affairs, the influence of family seems to be of great value for understanding the decision-making mechanism of individuals’ travel behaviour. In the past few decades, a several studies have pointed out the temporal and spatial constraints family structure imposes on individuals’ travel activities. Characteristics of family structure are mainly manifested in the size of the family, the presence and number of young children and older people living together, and other aspects such as the economic status of family. These attributes linked with family structure can restrict adult participation in travel activities by increasing housework responsibilities (Bhat & Singh, 2000), which has significant effects on residents’ travel behaviour. In the existing literature, discussions on how family characteristics affect people’ demands for travel activities can be divided into two aspects. On the one hand, some studies have explored the direct influence of family structure (such as family size and number of young children), family type (such as single-employee family and dual-employee family, single-parent family and two-parent family) on individuals’ travel activities (Dieleman et al., 2002; Srinivasan & Ferreira, 2002). On the other hand, other studies have focused on the indirect effects of other family members on travel behaviour (Kang & Scott, 2010; Yarlagadda & Srinivasan, 2008). However, the indirect effects of different family members could also be contained and reflected by the family size, family type or family structure. Since in this study we have focused on the family structure in China and how attributes about family structure © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Zhao and D. Yuan, Population Growth and Sustainable Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-7470-0_7

233

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7 Effects of Family Structure on Travel Behaviour

influence individuals’ travel behaviour and transport demand, we summarize the previous studies about family effects on travel behaviour into three topics: family structure, family type and family income, for a later analysis.

7.1.1 Family Size and Travel Behaviour According to existing studies, larger families are more likely to use cars. The use of cars is positively related to household size (Abuhamoud et al., 2011). The number of adults in a family and the composition (e.g., the number of people working) can influence the household travel behaviour (Etminani-Ghasrodashti & Ardeshiri, 2015). Fischer et al. (2019) showed that an increasing number of adult household members increases the probability of having more cars in a household. Similarly, in a study based on six versions of the South East Queensland Household Travel Survey data in Brisbane, Kamruzzaman et al. (2020) pointed that larger households travelled significantly further using private vehicles in most of the surveyed years. As they use private cars more often, larger families tend to walk less. This may be partly attributable to the inconvenience of walking with children and partly to the increased likelihood of being transported by other family members as a faster alternative to walking (Zhao et al., 2018). In addition to travel mode choice, household size can influence travel distance and trip frequency. A study in Hong Kong found that larger household size was associated with more and longer mandatory trips during the early morning (before the a.m. peak) and fewer and shorter discretionary trips, particularly in the second half of the day (He et al., 2018). One important reason why family structure has a significant impact on residents’ travel behaviour is that the time allocation among family members is closely related. The residents in the family do not live in isolation, but they live with other members. Time is both a personal resource and a resource shared by the family. The pattern of time spent by each member of the family affects the pattern of time spent by other members of the family (Craig & van Tienoven, 2019). The characteristics of time allocation have a significant impact on residents’ travel arrangements. For example, if one member of a couples has to commute for a long distance between the home and the workplace, he or she might be less likely to have enough time to do housework. Thus the responsibilities for doing housework will fall more onto other family members. Previous studies have shown that the time allocation of female members is more significantly affected by family factors and other members, while male members are relatively less sensitive (Sayer, 2016). This interaction between members in time allocation explains the effect of family attributes on residents’ travel behaviour to a certain extent.

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7.1.2 Family Type and Travel Behaviour In addition to family size, the type of family is another factor in family members’ travel behaviour (Bartosiewicz & Pielesiak, 2019; Witte et al., 2013). In different types of families, the members tend to have different preferences for time allocation (Allard & Janes, 2008; Bhat et al., 2015), which leads to different preferences for daily travel. For example, having a child appears to be a significant factor. According to Ding et al. (2017), the number of children in the family is positively correlated with the vehicle miles that individuals travel. For older people’s mobility, having children increases the demand and time for maintenance trips during the earlier hours of the day (before and during the a.m. peak). Hamidi and Zhao (2020) pointed out that a family with one or no child may travel to a nearby destination with a bike while a family with three children may be more likely to drive a car. Additionally, age seems to play a role, as households with a youngest adult household member between 30 and 60 years tend to have more cars available for both single person and 2-person households than households with the youngest adult household member under 30 or above 60 years. Households with a minimum of one child under 18 years (family households) are distinguished from 2-person (as well as from single person and single parent) households significantly, having a clear tendency to have more cars per household (Fischer et al., 2019). The effects of having a young child on other members’ travel behaviour may come from various factors. Some studies have shown that having a small child is likely to reduce the mobility of the parents (Schwanen et al., 2008), increase the time limit for parents’ travel behaviour (Lee et al., 2009) and possibly lead to the difference between responsibilities of family members becoming much more significant (Ettema & van der Lippe, 2009). Previous studies have mainly discussed the impacts of having a child based on survey results conducted in different areas. Families with young children have a corresponding reduction in shopping and personal travel, while community activities and children’s trips are more frequent. They are also more likely to use cars. The impact of the presence of children is different for fathers and mothers (Chakrabarti & Joh, 2019). Women tend to take on more housework, and their travel is more related to housework, such picking up children from school (Sánchez et al., 2014; Taylor et al., 2015). In large families where retired parents live together, the burden of raising young children may be shared by the older members, thereby alleviating the time and space constraints imposed on adults. On the other hand, retired parents also need to be taken care of, which may lead to other adults having more problems in time allocation and cause new restrictions on their travel activities (Ta et al., 2016). Meanwhile, single-family households without the pressure of raising children or caring for older members are more likely to participate in shopping and leisure activities (Srinivasan & Bhat, 2005). According to some previous studies, dual-employee families seem to adjust their travel decisions to shorten the commuting distance, and the number of daily trips is fewer for them than for single-employee families (Surprenant-Legault et al., 2013).

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It seems that the number of people working in a family could also affect the travel behaviour of family members (Srinivasan & Ferreira, 2002). Dual-employee families and families with at least one child are more likely to use a car for travelling (Dieleman et al., 2002). Some scholars have also paid attention to the influence of family attributes on children’s travel behaviour. On the one hand, the economic status of the family (represented by family income levels) affects the choice of travel modes for children (Pont et al., 2009). Many believe that children in low-income families are less likely to take a car or taxi while travelling than those in high-income families. Their participation in leisure activities may also be lower (Bjerkan & Nordtomme, 2014). On the other hand, family structure will also affect young residents’ personal travel behaviour, including their choice of travel mode for school and entertainment (Johansson, 2006). Evidence has shown that, compared with living in 2-parent families, teenagers living in single-parent families are more likely to walk or ride bicycles to school, and less likely to take a car (Bruijn et al., 2005; Larsen et al., 2009; Merom et al., 2006; Yarlagadda & Srinivasan, 2008). Teenagers in single-parent families have fewer leisure activities and smaller spatial ranges of daily life. The purpose, the length and travel mode choices of traveling for entertainment seem to be more restricted (Bjerkan & Nordtomme, 2014). In addition, just as children influence their parents’ travel behaviour, parents’ commuting behaviour also has a significant effect on their children’s travel patterns (McDonald, 2008). There is a two-way influence affecting family members’ travel activities (Hsu & Saphores, 2014; Yoon et al., 2011).

7.1.3 Family Income and Travel Behaviour In addition to family structure and family type, previous studies also regarded family income as a significant factor in people’s travel behaviour. A study in Germany found that households may consume more energy due to the increase in real family income (Matiaske et al., 2012). Similarly, a study in the United States found that a 1% increase in household income would result in a 0.82% increase in the annual miles travelled to all destinations by driving Kotval-K and Vojnovic (2015). Ding et al. (2017) also pointed out the effect of family income on travel behaviour, namely that high income is positively correlated with vehicle miles travelled. Family income is significantly associated with the use of a car. For example, a family with one or more members who can afford to own or rent a car has a higher potential to use a car than a family with insufficient economic resources to access a car (Hamidi & Zhao, 2020). In addition to the travel behaviour of adults in the family, children’s travel to school is influenced by family income. Shokoohi et al. (2012) examined whether socioeconomic factors moderate the relationship between parental perception of traffic safety and children’s travel to school. The results from an MNL model showed that the number of cars in a household and household income are the two main moderators. Similarly, children in households with cars will be driven by their parents to school more often than

7.2 Data and Methods

237

walking (Richter et al., 2009). Meanwhile, some studies argue that family income may not have that significant an effect. For example, Stapleton et al. (2017) found in a study in the United Kingdom that family income does not have a significant impact on travel distances.

7.1.4 Research Gaps Based on the summarised literature above, there are still some research gaps on the effects of family structure attributes on individuals’ travel behaviour. Although there have been many studies in Europe, cases in developing countries such as China are much fewer, and conclusions still remain unclear. Among the previous studies in China, discussions are mainly focused on the influence of traditional family attributes, such as family size and family income, which seems inadequate for a comprehensive understanding of the effects of family structure since the relationships between family members are not considered. More family attributes should be added to discuss their influence on members’ travel behaviour and decision-making. To make contributions to the existing literature, this study moves forward by organising the analysis of three aspects based on a travel survey conducted in Beijing. In addition to paying attention to traditional family attributes (such as family size, family type and income level), we have also taken the potential impact of family members into consideration (including having a young child, living with older members). Ownership of private cars is also included. Against the macro background of the steady development of the social economy and the optimisation of fertility policies, the analysis provides an empirical basis for guiding the formulation of transport policies and the improvement of transport systems better. The key questions for this chapter are as follows: (1) Are there differences in travel behaviour and preferences among different groups of families? (2) What effect do factors relevant to family structure have on residents’ decisionmaking for daily trips? (3) What can transport services do to meet the travel demand of various families for building a more humane transport system more effectively?

7.2 Data and Methods 7.2.1 Data To investigate the relationship between the travel behaviour of residents and their family attributes comprehensively, we analysed data from Beijing’s Comprehensive Survey of City Traffic Issues in 2015. Details about the main contents of this survey

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project were introduced in the third section of Chap. 1. According to this survey (conducted from September 4 to November 27, 2014, with the National Day holidays avoided), the sample included 40,003 households in the 16 districts of Beijing. Specifically, 23,276 households came from the central urban area (consisting of six districts1 ), accounting for 58.19% of the participants. Another 14,027 households came from the suburbs (consisting of six districts2 ), accounting for 35.06%. Finally 2,700 households came from the outer suburbs (consisting of four districts3 ), accounting for 6.75%. Based on the analysis in the previous chapters, the travel behaviour characteristics of urban residents are significantly different in multiple ways from those of rural residents. The urban–rural gap in travel behaviour may come from the huge urban–rural gaps in the accessibility of transport facilities, the built environment and other relevant factors. To be more representative and to avoid the interference of urban–rural attributes, we selected respondents living in the central area of Beijing (Dongcheng, Xicheng, Chaoyang, Fengtai, Shijingshan and Haidian) as the sample for further analysis. The sample included 58,306 residents from 23,276 households, with an average household size of 2.51 persons per household. Male residents accounted for 47.92%, and female residents accounted for 52.08%. In terms of the age structure, residents aged over 65 accounted for 18.98%, while children under 12 accounted for 7.18%. Most of the respondents were employed, which may have led to a large travel demand for commuting. The educational composition of the sample and the family size are shown in Figs. 7.1 and 7.2. The surveyed families in central Beijing were mainly 2-person and 3-person families, accounting for 40.89% and 31.62% of participants, respectively, while single-person families and families with more than four members accounted for 14.10% and 13.39%, respectively, which seems quite consistent with the overall characteristics of China’s family structure.

7.2.2 Methods In this chapter, we applied descriptive statistical analysis, multivariate OLS regression analysis, and MNL regression analysis as the main methods. The advantages and common usage of those methods have been described in detail in Chap. 6. Here, the three main methods are used together to discuss the differences in the travel behaviour of different kinds of families. We applied the multiple OLS model to estimate the causal effect of demographic and socioeconomic attributes quantitatively on residents’ trip frequency, while we used the MNL modelling analysis to investigate the relationship between those factors and residents’ choices of travel modes 1

In this section, the central urban area in Beijing consists of Xicheng, Dongcheng, Chaoyang, Haidian, Fengtai and Shijingshan. 2 In this section, the suburbs of Beijing consist of Tongzhou, Shunyi, Daxing, Fangshan, Mentougou and Changping. 3 In this section, the outer suburbs of Beijing refers consist of Pinggu, Yanqing, Huairou and Miyun.

7.2 Data and Methods

239

4.73% 08.6% 4.18%

8.49%

22.04% 18.47%

Preschooler Primary school Junior high Senior high Technical secondary school College Undergraduate Postgraduate Uneducated

16.10% 18.14% 6.98%

Fig. 7.1 Proportions of different educational levels in the central area in 2015

Fig. 7.2 Proportions of different family sizes in the central area in 2015

8.57%

4.41% 0.40% 0.01%

14.10% One Two Three Four Five

31.62%

Six 40.89%

Seven

to provide readers with a much clearer understanding of the travel preferences of different population groups. The specification of variables for the multivariate OLS model and MNL model were based on Beijing residents’ family attributes, taking into account the presence or absence of certain family members. Through applying the modelling method, the influence of each factor, especially family attributes, can be tested more accurately and the special role of the family as a primary unit in individuals’ travel decision-making can be analysed quantitatively. Based on the results of this analysis, this chapter provides evidence and suggestions for promoting an efficient and harmonious urban transport service system. Learning from the existing literature, we focused on four main categories of family attributes to explore the potential effects on individuals’ travel behaviour.

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(1) Family structure: according to the definition in the travel survey, all family members with blood relationships and other members (including nanny, classmates, relatives and friends, etc.) temporarily living at home during the investigation period were regarded as family members. In this study, the number of people living in the family is used as an indicator of the family size. (2) Family type: according to the previous studies, there are multiple different ways of defining family types. For example, families can be divided into 1generation households, 2-generation households etc. They could also be divided into nuclear families, disabled families, composite families, direct families etc. Alternatively, families could be divided into single-child families, multi-child families etc. Learning from previous studies and considering the changing trends of China’s family structure, we regarded the composition of family members as important and we finally divided the surveyed families into four types: families with older members,4 families without older members, families with at least one young child,5 and families without any young children. Through this redivision, the type of families act more concisely as an important indicator of families’ composition and member relationships in the later analysis. (3) Family economic status: the family’s annual total income (including wages, weekday bonuses, year-end bonuses, and other income such as gifts) is taken into consideration when discussing the influence of family economic status on individuals’ travel behaviour. The annual income of families has been divided into eight groups from the lowest to the highest. (4) Family vehicle ownership: based on the information in the travel survey, the number of bicycles owned by each family (excluding electric bicycles and motorcycles) and the number of commonly used motor vehicles (excluding motorcycles) have both been taken into account as a reflection of family vehicles’ ownership. In addition to the potential impact of family attributes, this study also considers the impact of individual socioeconomic attributes and four types of indicators selected for further analysis. (a) Gender. Several existing studies have shown that individuals’ travel behaviour often represents significant gender differences. Since men and women generally differ in the social division of labour and family division of labour, and they have different living habits in China, the impact of gender on travel activities might be systematic and universal. (b) Age. People in different age groups tend to have different preferences for daily trips, which may be related to their different lifestyles and employment status. In addition, age may act as an indicator of one’s ability to participate in independent trips. (c) Occupation. The differentiation of the social division of labour seems to affect the individual’s travel demand for commuting and the travel chains directly. The travel survey divided individuals’ occupations into nine subcategories, and we further categorised them into three major groups to analyse their difference: (i) individuals with permanent jobs, (ii) students and (iii) individuals with no fixed social division of labour. (d) Educational level. In general, individuals’ 4 5

In this chapter, older people are over 65 years old. In this chapter, a child is below 12 years old.

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241

Table 7.1 Classification of individuals’ occupations in the survey questionnaire Code

Occupation

Type

Code

Occupation

Type

1

Full-time jobs

a

6

Retirees

c

2

Part-time jobs

a

7

Housewives

c

3

Full-time study

b

8

No occupation

c

4

Part-time study

b

9

Others

c

5

Preschool

c

Table 7.2 Classification of individuals’ educational levels in the survey questionnaire Code

Highest educational level

Type

Code

Highest educational level

Type

1

Preschool

a

6

Senior college

c

2

Elementary school

a

7

Undergraduate

c

3

Junior high

b

8

Masters

d

4

Senior high

b

9

Uneducated

d

5

Junior college

c

education experiences may have a direct or indirect impact on their living patterns, consumption patterns and environmental awareness. Based on respondents’ highest educational achievement, they were divided into four groups: (i) elementary school or below, (ii) junior and senior high school, (iii) college and undergraduate, (iv) master’s or Ph.D. (Tables 7.1 and 7.2). For travel behaviour characteristics, we focused on the trip frequency within a day (a complete trip refers to a movement from the original place to the destination), mode choice and travel purpose based on the survey using the 24 h travel chains of residents.

7.3 Analysis 7.3.1 Statistical Description 1) Family size and travel behaviour According to the survey results, residents in the central area of Beijing generally travelled two or four times a day. The proportion of residents with more than five trips per day was relatively low. The characteristics of trip frequency indicated that residents’ travel chains in the central area mainly appeared to be the type of H–W–H and H–O–W–O–H (where W refers to work place, H refers to home, and O refers to other places). There are significant differences in the distribution of residents’ trip frequencies among families of different sizes. For example, the proportion of

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people staying at home the whole day among families with several members was significantly higher than that among families with only a single member. With the family size got smaller, the proportions of residents having two trips, four trips and six trips appeared to be higher. This shows that residents who live alone travel more frequently. It seems that the numbers of cars owned by a family is positively related to the family size. Some 83.46% of single-person households have no private cars, while the car ownership rate of 2-person, 3-person, and multi-person households is significantly higher. The car ownership rate in 3-person households is 55.83%, that is, more than half of 3-person households have at least one private car (Fig. 7.3). These results make sense, in that as the number of members living together in a family increases, the travel demands tend to be diversified. Large families may also be more likely to take group trips together. For families without cars, especially single-person households, the public transport system needs to adapt appropriately to their personalised travel demands and to provide customised as well as personalised transport services. Some 65.33% of residents in single-person households did not own a private bicycle, while more than two thirds of residents in 3-person households and multiperson households owned at least one private bicycle (Fig. 7.4). Thus, small families, especially single-person households, might rely more on public transport services for their daily travel activities, while their demand for private transport vehicles is quite low. In the context of the changing trend of miniaturisation of family size in China, public transport systems need to be of higher quality and higher efficiency to meet the travel demands of different families, as well as promote energy conservation and emission reductions. In the daily travel activities of different families, the main travel modes are mainly walking, buses, cars, subways and bicycles, but there are significant differences in preferences for travel modes among families of different sizes. Smaller households 0

1

2

3

100 90 80 70

%

60 50 40 30 20 10 0 One

Two

Three

Family size

Fig. 7.3 Number of private cars owned by families of different sizes

Four or more

7.3 Analysis

243 0

1

2

≥3

100 90 80 70

%

60 50 40 30 20 10 0 One

Two

Three

Four or more

Family size Fig. 7.4 Number of private bicycles owned by families of different sizes

use subway, railway, bus and walking more frequently than larger households. In terms of walking, 41.78% of the daily trips of single-person households are by walking, while this proportion is only 29.57% in 3-person families. In larger families with three or more members, the proportion of residents using cars in travel is significantly higher than that of single-person or 2-person households. According to the survey results, the car usage rate of 3-person and multi-person households in the survey sample was up to 20%, while the proportion of single-person households traveling by car was only 8.41%, indicating that the size of the family is related to the travel preferences of residents. The larger the family, the more diverse the travel demands of family members. Thus, they may have higher requirements for the convenience and flexibility of travel services, such as taking children to and from school, and going out with families together. For families with several members, cars seem to have better applicability, while for small families such as single-person households, the travel demands can often be met by public transport. Since the use of cars brings higher travel costs, the car utilisation rate in small families tends to be relatively low (Fig. 7.5). The survey results have shown that the main purposes of daily trips for residents in Beijing’s central area are work, leisure and entertainment, and shopping. Although there is no significant difference in the proportion of work trips, the proportion of residents who choose leisure, entertainment and shopping trips in small households is significantly higher than that in 3-person and multi-person households. Based on the difference in the trip frequency among families with different sizes, it seems that the travel chains of single-person households where residents live alone are more diverse and their demands for leisure travel tend to be larger (Fig. 7.6).

244

7 Effects of Family Structure on Travel Behaviour Walk

Car

Subway

Bus

Bicycle

Others

100 90 80 70

%

60 50 40 30 20 10 0 One

Two

Three

Four or more

Family size Fig. 7.5 Travel mode choices of families of different sizes Dining

Go to work

Go to school

Personal affairs

Housework

Entertainment

Go shopping

Visit friends

Deliver goods

Others

100 90 80 70

%

60 50 40 30 20 10 0 One

Two

Three

Four or more

Family size Fig. 7.6 Travel purposes of families of different sizes

2) Family type and travel behaviour According to the survey results, families with at least one older member living together had significantly stronger preferences for walking and taking buses than families without older members. When there are no older relatives living together,

7.3 Analysis

245 Walk

Car

Subway

Bus

Bicycle

Others

100 90 80 70

%

60 50 40 30 20 10 0 At least one older member

No older member

Family type Fig. 7.7 The travel mode choices of different types of families

people tend to prefer other types of travel modes, such as cars, subways and bicycles. Those modes seem to be relatively more autonomous although they might cause more risks to health. To adapt to the common aging trend in China’s big cities, it is necessary to promote safe pedestrians, slow-moving systems and a barrier-free public transport environment further (Fig. 7.7). For the surveyed families, 53.13% of the trips made by families without older members were for work, while trips relating to leisure activities only accounted for 10.30%. In contrast, more than 60% of families with older people travelled for activities related to leisure (including entertainment, shopping, visiting relatives and friends). It seems that the main travel purposes of families might depend on the composition of family members. There is a significant difference in the distribution of travel purposes between families with older members and those without older members. There may be several possible reasons. On the one hand, in families with older members, the older members themselves tend to have more travel demand for leisure, while there is almost no need for commuting. Older people who are still in good physical condition after retirement may often go to the vegetable market to buy food and for shopping, visiting relatives, walking with friends, going to the park to exercise, and going to the square for square dancing.6 As a result, travel for leisure occupies a large part in the daily lives of older people. On the other hand, in families with older members, the travel activities of other family members might often be affected by the older members. For example, when older people go to markets, parks, squares or other places, children or other family members may go with them. 6

In China, middle-aged and older people are increasingly gathering in open public spaces such as squares to learn dancing with music. This form of entertainment is usually called a square dance.

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7 Effects of Family Structure on Travel Behaviour

%

Some studies have already provided some empirical evidence of the composition of older residents’ travel purpose in China. Zhou and Chai (2013) summarised the characteristics of senior citizens’ travel behaviour from a spatial perspective. They analysed the relationship between the daily activities of older people and the social built environment. They pointed out that the daily activities of older people in China mainly relate to health seeking, shopping and leisure activities. Based on the theory of family life cycle, Mao and Li (2020) analysed the characteristics of older people’s daily life circles taking Xi’an as an example. They found that besides home, leisure places are the most common destinations for older people in their daily activities. On average the time they spent in leisure activities was 2–6 h per day. The morning is the peak of shopping trips for older people. The unique travel behaviour of older people might be the fundamental reason for the significant differences in the travel characteristics of families with and without older members (Fig. 7.8). Families with at least one child and those without children appeared to have different travel habits. Families raising at least one child were more likely to choose cars as a travel mode. In the survey sample, the car usage rate of families with young children was 20.57%, while families without young children (such as single-person households, couples who have not raised children, older people living alone, etc.) using cars as a travel mode accounted for only 13.01%, which is 7.56 percentage points lower than that of families with young children. At the same time, families without children are more likely to take buses as their travel mode than families with at least one child, and the usage rate of buses was about 23.22%. The difference in bus usage rate between the two types of families was 6.07 percentage points, indicating that when there were no children to support, residents might rely more on public transport (Fig. 7.9). Dining

Go to work

Go to school

Personal affairs

Housework

Entertainment

Go shopping

Visit friends

Deliver goods

Others

100 90 80 70 60 50 40 30 20 10 0 At least one older member

No older member

Family type Fig. 7.8 Travel purposes of different types of families

7.3 Analysis

247 Walk

Car

Subway

Bus

Bicycle

Others

100 90 80 70

%

60 50 40 30 20 10 0 At least one child

No child

Family type Fig. 7.9 Travel mode choices of different types of families

There are several possible reasons for the influence of raising children on the travel habits of the family. First of all, families with young children tend to have more pressure on time allocation. Parents often need to take care of the activities of their young children while working, as well as participating in other activities. A study based on qualitative interviews with parents in Sydney, Melbourne and Canberra showed that more and more parents choose to drive their children to and from school. The most important consideration is to save as much time as possible (Nakanishi et al., 2017). Even if the schools are within walking or cycling distance, parents still tend to prefer driving to save time. Secondly, families with young children tend to have more diverse travel needs than families without young children to support, such as for children’s medical treatment or playing with their children, making the family’s daily travel chain more complicated. Compared with public travel modes, cars can provide families with more timely, personalised and private travel services, and they can better cover the various daily demands as well as possible emergencies. A study using Beijing as an example to analyse the determinants of residents owning and using cars (Zhang et al., 2021) found that the birth of children encourages residents to buy cars and use cars more frequently. 3) Family income and travel behaviour There are significant differences in the usage rates of public transport (including subway and bus), motorised modes (including taxis, private cars or company cars) and non-motorised modes (including bicycles) among families with different economic levels. On the one hand, for families with relatively lower income levels, residents appeared to prefer public transport. This is particularly significant in terms of buses.

248

7 Effects of Family Structure on Travel Behaviour

Among the surveyed families, the bus usage rate has shown a trend of gradual decrease with the increase of family income. For people with annual family incomes of less than 50,000 CNY, the bus usage rate can reach 36.75%, while among those with annual household incomes of more than 300,000 CNY, the bus usage rate is less than 20%. On the other hand, families with relatively higher economic levels have stronger preferences for the motorised travel mode. The usage rate of motorised travel modes has shown a trend of gradual increase with the increase of family income. For example, only 15.10% of residents in families with annual incomes of less than 50,000 CNY used a car as their travel mode, while this proportion in families with annual incomes of 50,000–150,000 CNY has increased markedly, nearly accounting for one third. More than 40% of families with annual incomes of more than 150,000 CNY used cars for their travel. For the highest income group with annual incomes of more than 500,000 CNY, the usage rate of cars for travel was 53.46%. The usage rates of taxis among different income level groups has a similar distribution pattern to that of private cars. Fewer than 2% of residents with annual family incomes of less than 300,000 CNY take taxis as a daily travel mode, while for families with annual incomes of more than 300,000, taxis are more often used. The usage rate of taxis for residents with annual family incomes of 300,000–500,000 CNY was 5.17%, and that for residents with annual family incomes of more than 500,000 CNY was 6.54%. Generally, it seems that families with higher income levels are more likely to take taxis as their travel mode (Fig. 7.10). In terms of non-motorised travel modes, the difference among families with different income levels appears to be most significant in terms of bicycles. Among families with annual incomes of less than 50,000 CNY, 37.61% of trips were made Walk

Car

Subway

Bus

Bicycle

Others

100 90 80 70

%

60 50 40 30 20 10 0 50

7.3 Analysis

249

by bicycles. As the families’ annual income level got higher, this proportion showed a significant downward trend. Less than 20% of residents in families with annual incomes of 150,000–200,000 CNY used bicycles for traveling, some 49.4 percentage points lower than that in families with annual incomes of less than 50,000 CNY. The bicycle usage rate of families with annual incomes of more than 300,000 CNY was as low as 15%, and that in families with annual incomes of more than 500,000 CNY was only 8.08%. According to the survey results, the economic status of the family may also have a significant influence on residents’ preferences for public transport, motorised transport and non-motorised transport. In most parts of China, the cost of public travel modes is relatively low, and the cost of one trip by bus is significantly lower than that by motorised vehicles such as cars and taxis. According to the latest charging standards for Beijing’s public transport issued by the Beijing Municipal Development and Reform Commission on November 27, 2014 and implemented on December 28, the bus ticket fee in Beijing is 2 CNY per person within 10 kms, and 1 CNY per person for every additional 5 kms. With a public bus discount card, people can get additional discounts. Specifically, you can get a 50% discount with an all-in-one card, or a 25% discount with a student card, or free travel with a senior card (65 years old and above). Children whose height is less than 1.3 m (1.2 m in the previous standards) can also take free buses. Beijing’s rail transport (subway) charges 3 CNY per person within 6 kms and 4 CNY per person for 6–12 kms. There is an increase of 1 CNY per 10 kms for 12–32 kms, and 1 CNY for every additional 20 kms. Generally, the cost of the subway within the central area of Beijing (including six districts) is 3–5 CNY. In contrast, the cost of traveling by taxi is much higher. According to the Taxi Price Adjustment Plan issued by the Beijing’s Municipal Commission of Development and Reform and the Beijing Municipal Commission of Transport in 2013, the price of taxis has been adjusted to 13 CNY within 3 kms, and the basic unit price has been 2.3 CNY per kilometre thereafter since June 10, 2013. The fuel surcharge is 1 CNY per trip. In addition, a fee is charged for the scheduled car-booking service and for service during night time. The cost of traveling by taxi in central area in Beijing can reach 15–25 CNY or even higher. Therefore, public travel modes with much lower costs have become the main travel modes for families with low incomes and insufficient affordability. Residents with incomes of less than 50,000 CNY rarely choose to travel by car. For families with a higher income level, money costs are no longer the key factor restricting their choice of travel mode. In contrast, residents are more concerned about comfort and convenience during their journeys. As a result, despite the high costs of cars and taxis, their advantages in comfort, flexibility, and customised services encourage high-income families to prefer to travel by cars. The survey results have also shown that families with lower income levels might have a higher proportion of leisure and entertainment and shopping trips, while more than 50% of the trips of high-income families are commuting (such as traveling for work and official business). Among surveyed families with annual incomes of less than 50,000 CNY, trips for working accounted for only 31.67%, while trips for leisure, entertainment and shopping accounted for 45.78%. With an increase in

250

7 Effects of Family Structure on Travel Behaviour Dining

Go to work

Go to school

Personal affairs

Housework

Entertainment

Go shopping

Visit friends

Deliver goods

Others

100 90 80 70

%

60 50 40 30 20 10 0 50

10,000 CNY Fig. 7.11 Travel purposes of different income groups

family income, the proportion of leisure travel has shown a decreasing trend, while the proportion of work travel has risen accordingly. Among surveyed families with annual incomes of more than 100,000 CNY, work trips accounted for more than half. For families with annual incomes of more than 500,000 CNY, 56.78% of trips were for work, while the proportions of leisure, entertainment and shopping trips were much lower. For families with lower income levels, the proportion of trips for leisure/entertainment was 4.24% and that for shopping was 9.75%. The difference in the distribution of travel purposes among families with different income levels has shown the relationship between economic conditions and daily travel patterns. Work often occupies a higher position in the life of residents in high-income families, making their travel chains mainly from the residence to the workplace (Fig. 7.11). 4) Ownership of transport vehicles and travel behaviour Based on the ownership of private cars and private bicycles, the surveyed families were divided into different groups to explore the correlation between the usage of different travel modes and the families’ vehicle ownership. The results have shown that the effects of ownership of certain vehicles on usage rate or dependence seem quite significant. Families who do not own any private car are more likely to take trips by buses, bicycles or just walking, while those who own at least one private car have a higher dependence on driving for daily trips, and they are less likely to choose public buses or walking. This relationship between vehicle ownership and choice of travel mode appears to be consistent with the evidence in previous studies and people’s common sense. On the one hand, those choosing to buy a private car or private bicycle are usually the kind of travellers who prefer driving or cycling. So, it is reasonable for car owners to have a more significant dependence on private

7.3 Analysis

251

driving for daily trips. On the other hand, owning a private car or bicycle provides residents with more easily available and convenient access to driving and cycling, making it much easier for them to take a private car when they need high efficiency and short travel time (Figs. 7.12 and 7.13). Walk

Car

Subway

Bus

Bicycle

Others

100 90 80 70

%

60 50 40 30 20 10 0 At least one car

No car

Vehicle ownership Fig. 7.12 Travel mode choices of families with or without private cars

Walk

Car

Subway

Bus

Bicycle

Others

100 90 80 70

%

60 50 40 30 20 10 0 At least one bicycle

No bicycle

Vehicle ownership Fig. 7.13 Travel purposes of families with or without private bicycles

252

7 Effects of Family Structure on Travel Behaviour

7.3.2 Regression Modelling 1) Results of linear OLS model for trip frequency According to the observed variables selected based on previous literature and the survey questionnaire, we constructed an OLS regression model to explore the potential effects of family attributes on residents’ trip frequency on one single day, which can be represented by the following formula: Y = β1 X 1 + β2 X 2 + · · · + βk X k + ε In the model, the explained variable is the trip frequency of surveyed residents within 24 h. The explanatory variables include the family attributes and the individual characteristics. In terms of family attributes, factors such as family size, total annual income of the household, and ownership of vehicles (including the number of cars and the number of bicycles), whether there are older people living together, and whether there is at least one child are all taken into consideration. In terms of individual characteristics, factors include the gender, age, occupational type, and education level of the respondents. Based on these variables, we used Stata 15.0 to estimate modelling parameters. The estimation results are in Table 7.3. Based on the significance level of 5%, the variables of having at least one member over 65 years living together and earning a middle-low income (with annual family income of 50,000–100,000 CNY) did not show any significant effect, while the other variables are significantly correlated with the explained variable trip frequency. The relationships between these significant explanatory variables and the explained variable seem quite consistent with the conclusions in the previous analysis in terms of the travel differences among families with different attributes. a. Family size Among the family attributes included in the model, family size and the number of trips made on one single day appeared to have a significant negative correlation (α > 95%). According to the estimation results based on the residents’ travel survey data, the surveyed families were mainly 2-person and 3-person families, while families with more than four members accounted for less than 14%. The estimated parameter shows that among the residents of Beijing’s central area, the trip frequency of 3-person families was lower than that of 2-person and single-person families. Singleperson families where residents living alone, tend to have more trips on one day. This is matches the evidence in previous studies, which pointed out the significant association between family size and people’s travel behaviour (Kitamura & Kostyniuk, 1986). According to He et al. (2018), in Hong Kong, larger household size is associated with fewer and shorter discretionary trips particularly in the second half of the day. One explanation for the effect of family size on trip frequency may be that families with fewer members might have fewer group activities, especially single-person

7.3 Analysis

253

Table 7.3 Estimation results of the regression model Y

Coef.

St. Err.

Gender

−0.060

0.011

t −5.46

p-value

Sig.

0.000

*** ***

Age

0.008

0.001

13.52

0.000

Having older member

0.008

0.021

0.40

0.690

Having young kid

−0.226

0.039

−5.78

0.000

Family size

***

−0.046

0.006

−7.82

0.000

***

Car ownership

0.052

0.009

5.49

0.000

***

Bicycle ownership

0.076

0.006

12.47

0.000

***

Elementary school or below

0.000

Junior and senior high school

0.560

0.023

24.00

0.000

***

College and undergraduate

0.466

0.024

19.11

0.000

***

Master’s or Ph.D.

0.397

0.035

11.30

0.000

***

Having permanent job

0.063

0.025

2.51

0.012

**

Being in schools

0.000 0.026

−26.75

0.000

***

0.015

−0.30

0.764

No fixed job

−0.702

Low income

0.000

Middle-low income

−0.004

Middle-high income

−0.133

0.018

−7.49

0.000

***

High income

−0.130

0.021

−6.10

0.000

***

1.531

0.041

37.58

0.000

***

Constant

Note *** Refers to p < 0.01, ** Refers to p < 0.05, * Refers to p < 0.1

families. Therefore, compared with families with three or more members, residents living alone often have more demands for outside activities such as traveling for shopping and visiting relatives and friends. In combination with the current trend of family size miniaturisation in many cities of China, urban residents in the future may have higher demands for outside activities and group activities. For areas where family size is shrinking significantly, it is necessary to improve residents’ travel comfort and convenience and to provide more efficient transport services. In this way, transport services may be able to improve the quality of life for small families, especially residents living alone, by shortening the psychological distance and reducing the social isolation in interpersonal communication. b. Vehicle ownership According to the survey results, 40.69% of the residents owned at least one private car, and 59.01% of them owned at least one private bicycle. Modelling results have shown that families that own more private transport vehicles also tend to have higher trip frequencies. One explanation for this relationship is that ownership of a private car has a significant influence on people’s frequency of driving (Aizezi et al., 2017; Kitamura & Kostyniuk, 1986). Most evidence suggests that owning more private

254

7 Effects of Family Structure on Travel Behaviour

transport vehicles gives residents more opportunities for making trips with these vehicles. Although the public transport system is extensive in the central area of Beijing, the autonomy and convenience of private vehicles are still attractive for travellers. Public transport, with its large passenger flows and fixed times, is limited to meeting the temporary and personalised travel demand of residents, such as going out for medical treatment. In the future, along with the promotion and expansion of public transport services to meet the daily demand of most people, it is also necessary to provide customised and personalised transport services to reduce the ownership and usage of private vehicles. c. Family type The surveyed families were categorised into four groups depending on whether they had older members or young children living in the family. Having at least one young child (aged below 12 years old) appeared to have a significant correlation with the trip frequency. This is consistent with conclusions in previous studies. There is evidence that having a little child is likely to reduce the mobility of the parents (Schwanen et al., 2008). Based on the 2012–2013 California Household Travel Survey, Chakrabarti and Joh (2019) found that the presence of young children was associated with lower levels of physically active travel (i.e., walking and bicycling) and public transport use on average. The likelihood of engaging in 20 min or more of active travel per day falls as couples transition to parenthood. The family type affects not only parents’ travel patterns, but also the behaviour of the children. Aizezi et al. (2017) used an MNL model and found that family type has a significant influence on children’s travel mode choice. To conclude, the travel behaviour of different family members affected each other. Generally, children under the age of 12 still lack the ability to take trips independently. For physical or psychological reasons, it is unsafe to allow them to travel alone. At the same time, the existence of children puts pressure on their parents to look after and take care of them. As the literature shows, the presence of young children usually increases the time limit for parents’ travel behaviour. Families with young children have a corresponding reduction in participation in shopping and personal travel (Lee et al., 2009). As a result, the frequency of travel activities for families with child-raising obligations is relatively reduced. d. Family income Based on the survey questionnaire, the original eight categories of income level were merged and sorted into four categories of income. The levels of family income were added to the model as dummy variables. The four categories of income were low-income families (below 50,000 CNY), middle-low-income families (50,000– 100,000 CNY), middle-high-income families (100,000–200,000 CNY) and highincome families (over 200,000 CNY). The estimated parameters for last two groups are both significantly negative. The middle-high-income and high-income groups of families are more likely to have fewer trips than the low-income group of families. According to previous studies, family income is a significant influencing factor on

7.3 Analysis

255

people’s travel behaviour. Evidence from Western countries has shown that higher family incomes are usually correlated with more transport energy consumption (Matiaske et al., 2012) and more miles travelled by driving (Kotval-K & Vojnovic, 2015). A study using the National Household Travel Survey Baltimore Add-on data has pointed out that income level is positively correlated with longer traveling distances (Ding et al., 2017). Some researchers attribute this relationship to people’s usage of private cars. For families with sufficient economic sources to own a car, the likelihood of driving for long distances definitely increases (Hamidi & Zhao, 2020). However, the influence of family income on residents’ trip frequency remains unclear. One possible explanation may be that families with higher income levels usually care more about the quality of travel; thus, they may prefer to simplify their daily travel chains. Some flexible travel demand such as buying vegetables and foods can also be replaced by using door-to-door delivery services. The differences in trip frequency among families of different income levels provides evidence and a reference for the future development of customised and differentiated transport services. e. Other factors In addition to the family structure attributes, other factors about the individuals’ socioeconomic attributes have also shown relationships with residents’ trip frequency. Estimated coefficients for variables including gender, age, and education level and occupational type have passed the significance test. For example, there are significant gender differences in the daily travel behaviour of individuals when taking residents living in Beijing’s central area. Compared with women, men’s trip frequency is often lower, and the travel chain is relatively simpler. According to the previous statistical analysis, it seems that women have more travel demand for non-work trips such as shopping, leisure and entertainment activities than men (Basari´c et al., 2016; Sweet & Kanaroglou, 2016). Female residents are usually more responsible for trips with their children (Boarnet & Hsu, 2015). Influenced by traditional social culture, some women who are housewives spend most of their time taking care of household chores, raising children, and buying daily necessities. Picking up children might be the main content of daily life for those housewives, and this may lead to complexity and diversification in their travel chain, as well as enhancing their travel demand. The estimation results in the model have shown that there is a significant positive correlation between residents’ age and trip frequency on a single day. The result is counterintuitive, in that older people are less likely to take trips due to physical difficulties. One explanation may be that the lifestyles of older people may vary substantially among different countries and cities. In China, substantial portions of the older population still co-reside with their adult children (Feng et al., 2015). A survey in Shanghai found that more than 50% of children under the age of 3 are being raised by their grandparents. These older people thus share some of the household responsibilities, such as daily shopping and picking up children after school (Goh, 2009). It is obvious that living with young family members has profound effects on older people’s mobility patterns and likelihood of taking trips (Feng, 2017). In the central area of Beijing, continuous improvement of public transport systems

256

7 Effects of Family Structure on Travel Behaviour

has effectively adapted to the aging trend, so that older people can have access to many places for leisure and entertainment without much difficulty. At the same time, this positive relationship also means that the public transport system in big cities, especially those faced with severe aging trends, needs to improve access to be friendly enough for older people. For education level, relative to the low education group (primary school and below), groups with higher education levels (including the middle-low group with junior/high school diplomas, the middle-high group with college/undergraduate degrees and the high group with postgraduate degrees) have shown higher trip frequency per day. This indicates that residents’ travel behaviour is directly or indirectly affected by their education level, with their lifestyle and daily habits as the possible intermediate factors (Etminani-Ghasrodashti & Ardeshiri, 2015). This relationship between education and people’s travel behaviour is further analysed in the remainder of this book. In addition, the types of individuals’ occupations also seem to be associated with their daily trip frequency. Based on the classification of occupations in the survey questionnaire, we further reclassified the respondents in to three types of occupations: individuals with permanent jobs, individuals in schools, and individuals with no fixed social division of labour. The variable of occupation has also shown a significant correlation (α > 95%) with residents’ travel behaviour. Compared with the in-school group (including full-time students and part-time students who were receiving education during the survey), individuals with permanent jobs tend to take more trips each day, while those without a fixed social division of labour (including preschool children, retirees, the unemployed etc.) have the lowest trip frequency. This difference among individuals is in line with the large amount of commuting demand in China’s big cities. For those who have permanent jobs, work-based trips are necessary on every workday, resulting in huge flow of traffic and a high level of trip frequency. Students are also a significant portion of travellers. Going to and from school, as well as other after-school activities, may partly explain the higher trip frequency of students than unemployed people. It is obvious that the occupational types or employment status have significant effect on residents’ daily travel patterns, and that commuters usually have more frequent demands for traveling. In the future, urban transport services need to be improved to meet people’s commuting demand, optimising commuting travel efficiency and the quality of commuting. 2) Results of MNL model for mode choice According to the statistical description analysis based on the survey results and evidence from previous literature, there are significant gaps between different groups of families in commonly used travel modes. This section describes an MNL regression model to examine the potential effects of family attributes on residents’ choice of travel mode for daily trips. The model can be represented by the following formulae: Uin (X in |βn ) = X inT βn + εin

7.3 Analysis Table 7.4 The sample size and proportion of each travel mode category according to survey results

257 Category

Travel mode

Sample size

Proportion (%)

1

Public transport

41,067

49.93

2

Motorised vehicle

21,203

25.78

3

Non-motorised vehicle

19,981

24.29

exp(Uin ) pin =  An j=1 exp(Ujn ) In the model, the explained variable is the travel mode of surveyed residents, set as a dummy variable. Since previous analysis has shown that the differentiation of mode choice among families mainly appears in the use of cars and public transport, residents’ mode choices are divided into three types: public transport (including public bus, subways and intercity railway), motorised vehicles (including cars, taxis and shuttle buses operated by employers) and non-motorised vehicles (including bicycles, electric bicycles and shared bikes). To make the classified travel modes comparable, respondents who chose walking or who did not answer were excluded from the final model. The size and proportion of each category are in Table 7.4. The explanatory variables include the family attributes and the individual characteristics. In terms of family attributes, relevant factors included family size, total annual household income, and ownership of vehicles (including the number of cars and the number of bicycles), whether there were older members living together, and whether there was at least one child. In terms of individual characteristics, relevant factors include the gender, age, occupational type, and education level. Among the explanatory variables in the model, family size, car ownership, bicycle ownership and age are continuous variables, while the other explanatory variables are category variables. Among them, whether there are older members (over 65 years old), whether there is at least one child (under 12 years old) and gender are dichotomous variables, while annual household income, occupational type and education level are multiple variables. Based on the statistical pattern of household income among valid respondents, families were divided into four groups: low-income families (less than 50,000 CNY), middle income families (from 50,000 to 100,000 CNY), middle-high-income families (from 100,000 to 200,000 CNY) and high-income families (more than 200,000 CNY). In terms of individuals’ occupational type, there are three main categories: employed workers (full-time or part-time), in-school students (full-time or part-time), and those without a fixed social division of labour (such as retirees, housewives or full-time mothers and freelancers). For education level, the division was according to the highest educational level of the respondents: low education level (no more than elementary school), middle education level (junior or senior high education), middle-high education level (junior college or university undergraduate) and high education level (master’s or Ph.D.).

258

7 Effects of Family Structure on Travel Behaviour

We used Stata 15.0 to estimate the modelling parameters using these variables. The group choosing public transport as the most commonly used travel mode was set as the base group. Table 7.5 gives the estimation results of the MNL model. The overall significance p value of the model (Prob > χ2 ) appeared less than 0.01, indicating that the MNL model can generally estimate the impacts of explanatory variables on the explained variables. According to the coefficient estimation results, most of the contained explanatory variables showed some significant causal effect on the explained variable (travel mode choice) and passed t tests at the 1% significance level. On the whole, the family attributes and individual attributes jointly affect residents’ preferences and choice of travel modes in daily life, and the estimated coefficients effectively confirmed the statistical analysis results above. a. Family size For the family attribute factors in this chapter, the estimated coefficients of family size in the two comparison groups were both negative, which means the odds value (indicating the likelihood) was less than 1 (α > 99%). According to the estimation results, the coefficient of family size variable in the group using motorised vehicle was −0.296 (α > 99%), and the coefficient of family size variable in the group using Table 7.5 Estimation results of the regression model Y

Coef.

Gender

−0.060

0.011

−5.46

0.000

***

0.008

0.001

13.52

0.000

***

Age

St. Err.

t

p-value

Sig.

0.008

0.021

0.40

0.690

Children

−0.226

0.039

−5.78

0.000

***

Family size

−0.046

0.006

−7.82

0.000

***

Older people

Car

0.052

0.009

5.49

0.000

***

Bicycle

0.076

0.006

12.47

0.000

***

Education level 1

0.000

Education level 2

0.560

0.023

24.00

0.000

***

Education level 3

0.466

0.024

19.11

0.000

***

Education level 4

0.397

0.035

11.30

0.000

***

0.025

2.51

0.012

**

0.026

−26.75

0.000

***

Occupation 1

0.063

Occupation 2

0.000

Occupation 3

−0.702

Low income

0.000

Middle-low income

−0.004

0.015

−0.30

0.764

Middle-high income

−0.133

0.018

−7.49

0.000

High income

−0.130

0.021

−6.10

0.000

***

1.531

0.041

37.58

0.000

***

Constant

Note *** Refers to p < 0.01, ** Refers to p < 0.05, * Refers to p < 0.1

***

7.3 Analysis

259

non-motorised vehicle was −0.14 (α > 99%). This indicates that family size plays a significant role in determining residents’ choices of travel modes. Meanwhile, there are differences between the results of MNL model and those of previous statistical analysis. According to the estimated parameters, the log odds for large families to choose motorised and non-motorised vehicles over public transport is lower than that for small families, which seems to indicate that families with more members are more likely to use public transport, while the statistical analysis found that families with more members may prefer to drive private cars for daily trips. There are several possible reasons for the differing results. Since the previous statistical analysis took family size as the only factor for dividing different family groups and making comparisons, indirect effects from other factors were not considered. However, the MNL regression model makes it possible to observe the causal effect of family size with key family-level and individual-level socioeconomic factors kept under control. As a result, when dividing residents’ choice of travel mode into three main categories, it is more accurate to use a regression model to estimate the causal effect of family attributes. Thus, when keeping family composition, income level and other family-level factors under control, individuals in larger families may prefer to use public transport, while the result that larger families are more likely to drive a car in the statistical analysis may be due to the indirect effect of having more older members or young children on people’s mode preference. b. Vehicle ownership For vehicle ownership, the coefficient estimation results show that household vehicle ownership (the number of bicycles and cars owned by families) has a significant impact on residents’ preferences for travel mode. There was a positive correlation between the number of bicycles and preference for non-motorised travel mode, and between the number of cars and preference for motorised travel mode. For the number of family-owned bicycles, estimated coefficient in the third group is 0.70, with the significance p value being less than 0.01. This indicates that the families with more bicycles have significantly higher log odds ratios of choosing non-motorised vehicles (Group 3) than public transport (Group 1). Further estimation has also been conducted with the second group (motorised vehicles), which is the base group for comparison, and the estimated coefficient was 0.74 in the third group, with the significance p value being less than 0.01. This shows that the more bicycles a family owns, the higher the likelihood (larger log odds ratio) of it choosing nonmotorised vehicles rather than motorised vehicles. This significant relationship between vehicle ownership and mode choice is consistent with most of the findings in to existing literature. There are possible explanations for the effects. On the one hand, families with higher ownership of private bicycles have more choices of travel modes and more opportunities to take a ride for short daily trips. Some travel demand (such as shopping and leisure activities within a bicycle-friendly spatial range) can be met by cycling. On the other hand, owning more private bicycles may be an indicator of a positive attitude to non-motorised vehicles such as bicycles, e-bicycles and shared bikes. In general, those owning one or more private bicycles usually have higher environmental protection consciousness

260

7 Effects of Family Structure on Travel Behaviour

or regular riding habits. Thus, compared with those without bicycles, the habit of riding a bicycle or physical fitness required for cycling, those owning bicycles are more likely to consider a non-motorised vehicle for daily trips. On the number of family-owned private cars, the estimated coefficient in the second group is 20.9, with the significance p value being less than 0.01. This indicates that families with more cars have a significantly higher likelihood of choosing motorised vehicle for daily trips than using public transport. This finding matches the statistical results in previous analysis, and is also consistent with previous studies. Owning more private cars provides family members with more choices of vehicles for trips, especially long-distance trips. In most cases, the advantages of private cars over public buses or subways are attractive for families to satisfy their travel demands for comfort, convenience and private collective trips. c. Family type Compared with families without any older members, residents whose family contains at least one older member have lower log odds ratios (likelihood) of choosing nonmotorised vehicle such as bicycles than public transport. In other word, families with older members have a lower probability of choosing bicycles over buses. According to the statistical results, older people usually have a significantly higher preference for taking buses. On the one hand, older people’s physical fitness may cause limitations and prevent them from choosing active travel modes such as riding a bicycle and driving a car, which often bring more safety risk, while public transport provides a safer and more convenient alternative for them. On the other hand, the current public transport service system in all parts of China has provided the older group with special discount cards, which makes it convenient and cheap for them to take public buses. The older group’s preference for public transport may also have a direct or indirect impact on the other family members living together. For example, when other family members accompany older people during a journey, they may be more likely to choose public transport, while the probability of using non-motorised vehicles such as bicycles is definitely lower. In addition, the results showed that there are a total of 7,340 households with older members in the sample, among which up to 3,728 households have more than one older member. Many families include two or more older members, which may make a high dependence on public transport in daily trips more reasonable. Compared with families without any young children, the log odds ratio of family members with at least one young child choosing motorised vehicles over public transport is significantly higher. Consistent with the statistical analysis of the differences in travel mode choice among different family groups, the estimation results of the MNL model proved again that the presence of young children may increase the willingness and preference of families to travel by car. This may be explained from multiple perspectives. On the one hand, families with young children often have more flexible and diverse travel patterns, such as trips for children’s medical treatment, school, and leisure and entertainment activities. With these purposes, private cars can provide families with safer and quicker travel services than public transport, and can ensure that necessary and urgent travel demands are met in time. On the

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261

other hand, unlike the more open travel environment of public transport, private cars can provide families with more customised and personal services for their collective activities (such as family tours on holiday), as well as providing a relatively private environment. As a result, private cars tend to be more attractive for families with babies or young children. d. Family income In addition to family size, family type and vehicle ownership, the level of family income also influences people’s choices of travel mode. According to the estimation results of the model, the dummy variable on family economic level was significant in the third group (non-motorised group), and the estimated coefficients were all negative, with significance p less than 0.01. Compared with families with relatively low income levels, those with higher income levels seemed less likely to choose nonmotorised travel modes over public transport. Furthermore, we ran the model again with the second group (motorised vehicle group) as the base group for comparison. New estimation coefficients of the model showed that, unlike families with relatively low income levels, those with higher income levels seem less likely to choose nonmotorised travel modes over motorised modes. Thus, high-income families have a significantly lower preference for non-motorised vehicles, such as bicycles, ebicycles or shared bicycles than those with lower income levels. This is consistent with the view that bicycles are usually more favoured by low-income groups because of their convenience, flexibility and cheapness. e. Other factors In addition, the modelling results verified that individual attributes have a significant impact on residents’ choice of travel mode. In terms of gender, female residents hold lower preferences for motorised travel modes such as private cars and non-motorised modes such as bicycles. Women tend to make more use of public transport for daily trips. This significant gender difference in the usage of public transport is consistent with the previous studies, which are further explored and discussed in the following chapter of this book. For education level, people with higher levels of education seem to have stronger preferences for choosing public transport. One possible reason may be that the level of education may inform residents’ attitudes towards low-carbon travel as well as their consciousness of traveling in an environmentally friendly manner. In general, those with higher education tend to have stronger environmental consciousness. As a result, they may prefer to choose public transport for their daily travel activities if possible. In terms of occupational type, unlike employed workers (full-time or part-time), in-school students (full-time or part-time) and those without any fixed social division of labour (such as retirees, full-time mothers and freelancers) are more likely to take public transport. One possible explanation may be that motorised vehicles usually cost more money, such as buying a private car or paying for a taxi ride, than public transport. Employed workers also usually have more ability and more willingness to pay for the travel cost. In addition, motorised vehicles may provide them with more convenient travel services to satisfy their demand for commuting.

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7.4 Conclusion In this chapter, we have applied multiple quantitative analysis methods such as descriptive statistics, multiple OLS regression, and MNL regression models to explore the relationship between family attributes and residents’ travel behaviour based on data from Beijing’s Comprehensive Survey of City Traffic Issues conducted in 2015. In general, family is seen as the basic unit of residents’ daily life and social behaviour. There are complex interactions among family members, and in many cases they may participate in social activities as a whole. With the optimisation of China’s fertility policy and changes in people’s fertility concepts and willingness, family structure is becoming an increasingly important content under the topic of population growth and its structure. In recent years, the size of Chinese families has shown a trend of miniaturisation, and family types have become more diverse. It is necessary to pay attention to residents’ travel behaviour from the perspective of family attributes to learn more about people’s transport demand. Based on attributes including family size, family composition type, family economic level, and family vehicle ownership, this chapter has first compared and discussed the differences in the travel characteristics of different families. These travel characteristics include trip frequency, travel mode choice, travel purpose, etc. It has shown that family attributes play an important role in decision-making on travel activities for residents. The results indicate that there are differences in preferences for travel modes between small families and multi-person households. Small families use public transport more often, while their car ownership and bicycle ownership rates are relatively low. Multi-person households have higher car ownership and utilisation rates, which means their family members are more likely to use private transport instead of public transport. In terms of travel purpose, families of different sizes have also performed differently. Small families have a higher proportion of travel related to leisure activities, which may have something to do with the greater social communication needs of single-person and 2-person households. On the composition of family members, families with older people tend to use buses or walk more frequently. On the one hand, this may be caused by the special travel preferences of older residents. On the other hand, it may be related to the influence of the older members on their family’s travel habits. For example, other members might have travel needs such as taking care of and accompanying older people on walks. Families with older people also have a higher demand for leisure and entertainment trips. Families with young children have a stronger preference for cars, which may be related to daily needs such as transporting young children to/from school and going to the doctor, indicating that the presence of children may affect residents’ travel choices. From the perspective of family economic level, the car utilisation rate is positively correlated with the family economic level. Families with higher incomes are more likely to drive a car. This may be related to the requirements of high-income residents for comfort and convenience. There are also differences in the travel purposes of families with different income levels. Low-income families have a higher proportion of leisure

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trips, and their chains of activities are more flexible. In addition, the ownership of family vehicles also affects residents’ choices of travel mode. Families with a car are more likely to choose self-driving instead of public transport to get to another place. Furthermore, in this chapter, we have applied two multiple regression models to test the possible factors that may affect individual trip frequency and travel mode choice quantitatively. The results show that family attributes, including family size, family vehicle ownership, family type and family economic level all play significant roles in residents’ travel behaviour, for both trip frequency and travel mode choice. In addition, individual attributes of residents, including gender, age, education level and personnel type (employment status), have significant effects on their decision-making for daily trips. The quantitative analysis and qualitative discussion in this chapter demonstrate that family attributes are important factors in residents’ travel behaviour and transport demand. In the context of the new adjustment of China’s fertility policy and the further transformation of the population structure, family structure should receive more attention. To meet the actual needs of the population and their expectations for better life better, construction and improvement of a people-oriented, high-quality transport service system should be based not only on individual attributes, but also on the special role of family, which is a basic social unit. Considering the empirical evidence in this chapter, the sustainable development of China’s transport needs to give more care to the diverse travel demands of different family groups.

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Chapter 8

Gendered Mobility

8.1 Literature Review In previous studies on residents’ travel characteristics, there has been extensive evidence of the significant differences between males and females in travel characteristics. The focus on female travel behaviour research can be traced back to the 1960s. Tivers (1977) first proposed that previous studies of human geography ignored half the people, referring to females. Since the 1970s, the development of feminism has led some scholars to realise that transport research and transport planning methods had been significantly gender blind for a long time. Some scholars began to criticise the ignorance of gender difference, and then the research field of gender and transport was born. Studies in gender and transport initially focused on the analysis of the differences in travel behaviour characteristics between males and females and the possible reasons for them. For example, Gordon et al. (1989) reported a slight gender difference in public transport choice in the United States. Bose and Jones (2004) analysed the differences between American women and women in other countries in terms of family attributes, occupations, and vehicle ownerships. Krizek et al. (2005) pointed out the difference between women and men in the choice of travel mode and in the perceptions of transport facilities. Nobis and Lenz (2004) agreed that employment status and family structure have an important influence on women’s choice of travel mode. As studies of gender and transport proceeded, some scholars began to criticise and make suggestions about current transport policies and planning methods based on their conclusions. For example, some earlier studies analysed the urban land use structure, the relationship between public space and private space, and the spatial relationship between labour and reproduction from the perspective of women (England & Gad, 2002; Little, 1994). They pointed out that there are problems to be solved in these relationships. These studies have made great contributions to the field of women’s travel behaviour. Bhat and Koppelman (1999) used data from the Dallas metropolitan © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Zhao and D. Yuan, Population Growth and Sustainable Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-7470-0_8

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area to develop a shopping departure time allocation model. They found that women’s shopping behaviour during peak to off-peak periods was quite different from that of men. To conclude, previous studies discussing gender difference in travel behaviour characteristics mainly covered three aspects: gender difference in travel modes, gender difference in trip frequency and distance, and gender difference in travel purpose. Some studies further discussed the possible reasons for the gender differences in travel behaviour, and analysed the issue of gender equity. In this literature review section, we summarise the main conclusions of previous research on these aspects.

8.1.1 Gender Gap in Mode Choice and Travel Purpose Gender difference in mode choices for travel is a highly significant issue, and it has attracted lots of discussion. Gender difference is significant in using cars, cycling, public transport and walking (Abasahl et al., 2018; Duartea et al., 2015; Mahadevia & Advani, 2016; Sivasubramaniyam et al., 2020). In most studies, females tend to use cars less and public transport more than males, especially for long tips (Mahadevia & Darshini, 2012; Sánchez & González, 2016; Srinivasan, 2008). For short trips, females appear more likely to travel on foot (Mahadevia & Darshini, 2012; Ubogu et al., 2010). Adlakha and Parra (2020) took the case of Chennai, India and found that females represent the largest share of public transport users. Even for the same modes, such as cars, there are differences in the roles of males and females. A study based on three Arab communities pointed out that women tend to travel by car more as passengers, while men tend to be drivers (Elias et al., 2015). Some studies explored the different mode choices between males and females for a certain type of travel. For commuting, Sánchez and González (2016) found that men’s commuting almost exclusively takes place by private vehicle, especially in young men. Meena et al. (2019) investigated the mode choices for shopping and found that gender was significant in mode choice. Males were more likely to use twowheelers for shopping trips than females, while females prefer walking. In terms of travel for schooling, gender difference are still significant. Most evidence shows that boys are more likely to take bicycles to school than girls, which may be due to the lower independence mobility of girls (McDonald, 2012). Several studies have suggested several reasons that might help to explain the gender gap in mode choices better. For example, Abasahl et al. (2018) focused on the students on major college campuses in the Baltimore metropolitan area, and found some negative factors that discourage females from using bicycles. These factors include distant trips, longer travel times, not having access to a bicycle and an unsafe environment. Some earlier studies tended to take the household responsibility hypothesis, and they pointed out that domestic and parenting responsibilities greatly influence women’s travel mode for commuting (Clark et al., 2002). Scheiner (2014) focused on key events in the life course that may affect travel mode, and found

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that the birth of a child has the most notable effects on mode use with respect to family biography. Similarly, a study using data from the United States found that the gender commuting gap was largest in (married) couples with children (Fan, 2017). However, this hypothesis could not explain all the gender gap in travel. Some studies argued that even after controlling for family type, presence of children, age and associated adaptations, the differences in commuting between male and female are still significant (Duarte et al., 2015; Hjorthol & Liva, 2014). Another explanation is that women have different commutes from men because they are constrained by part-time employment and low pay so that some modes do not pay off (Carter & Butler, 2008; Sandow, 2008). However, conclusions vary in different cases and the reasons why female choose travel modes differently remain complex and confused. In terms of travel purpose, the gender gap also appears significant. Although in many studies, commuting is the main and primary travel purpose for both genders, there is a significant difference in the proportion. Generally, a greater percentage of men travel to work than women, while women make a greater number of non-work trips than men, such as shopping trips (Basari´c et al., 2016; Sweet & Kanaroglou, 2016). A study using data from the 2001 Southern California Household Travel Survey focused on the gender gap in non-work travel. The results showed that women tend to take more non-work trips than men, and this gap is mainly reflected in service trips, including chauffeuring trips, shopping trips and errands (Boarnet & Hsu, 2015). The gender difference in travel purpose can be partly explained by the different roles in the household as well as in society that men and women usually play. Generally, women tend to juggle their roles as workers and as care-givers more than men do (Craig & Brown, 2017). This difference may lead to women having to undertake more multipurpose trips (Cao et al., 2008; Scheiner & Holz-Rau, 2017). For example, when a child falls sick at school or is faced with other problems, it is often his mother who takes him to hospital or back home. Activities related to the child have more effects on mothers’ travel than on fathers’. Similarly, the birth of a child seems to affect the travel behaviour of males and females differently. Previous research has shown that women’s time allocation is much more sensitive to household and spousal characteristics than men’s (Sayer, 2016). In this context, women have a greater share of travel with and for children than men. Craig and van Tienoven (2019) used time-diary data from Australia, the UK, Spain and Finland to point out that fathers perform 27.2%, and mothers perform 61.7%, of total household travel with and for children. For other factors that might influence individuals’ travel behaviour, males and females perform differently as well. For example, previous studies have pointed out that women have greater sensitivity to the different variables that influence their commuting choices (Sánchez & González, 2016).

8.1.2 Gender Gap in Trip Frequency and Travel Distance For trip frequency and travel distance, gender differences are significant in many cases. Generally, males tend to have more standard and longer travel patterns, such

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as those travels to or from workplaces, with few interruptions, while females tend to have shorter and more complex travel patterns. For commuting travel, women tend to have shorter commutes than men (Reuschke & Houston, 2020). One reason for this gender difference in travel distance may be that women tend to work more within their communities (Elias et al., 2015). Using data from their research on travel characteristics in the United States, McGuckin and Nakamoto (2005) pointed out that women tend to stop more frequently during the execution of their daily trip chain to allow the passengers or the driver to embark/disembark, or to perform everyday purchases and use services they need to meet household needs. Li et al. (2005) pointed out that men tend to travel for longer periods and cover greater distances in the morning peak hour, while working women are more likely to stop during their regular trips to work and on the journey home, especially if they have to care for children and the household. There are similar gender difference in developing countries. In Rajkot, a mid-sized Indian city, females prefer to make short trips even for work purposes so that they can multitask and fulfil the requirements of the multiple roles they are expected to play (Mahadevia & Advani, 2016). Many studies suggest that differences in employment may be a reason for the gender gap in trip frequency and distance. Research conducted in California revealed that unemployed mothers are generally more mobile than their male counterparts in the same situation (Gossen & Purvis, 2005).

8.1.3 Gender Equality in Transport Many researchers have discussed gender difference in travel behaviour in the context of social equality. According to previous studies, females tend to have more complex travel patterns than males because of differing household and work roles buttressed by societal norms (Carter & Butler, 2008; Mahadevia & Advani, 2016; Prati, 2018). The gender gap in travel behaviour could reflect the lack of gender equality in society. For example, the presence of children and household responsibilities play more significant roles in influencing the travel behaviour of females than that of males (Emond et al., 2009). Safety seems to be a more significant concern for female cyclists than male cyclists, which could lead to females being less likely to travel by bicycle (Akar et al., 2013). Mejía-Dorantes and Soto Villagrán (2020) concluded that fear of aggressions and sexual violence have the greatest influence on women’s urban mobility. Factors relating to culture could also matter, since in some western countries female students are usually not allowed to cycle to or from school without an adult (McDonald, 2012). Iqbal et al. (2020) took Karachi in Pakistan as a case and argued that women are at a disadvantage in terms of gender due to additional household chores, harassment, and fewer leisure travelling opportunities. Gender equality is a complex and multidimensional concept comprising a range of factors encompassing social, cultural, historical and economic processes. Throughout many countries, an unequal division of time and distribution of tasks between women

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and men persists, with women spending more time in housework and care activities. To measure gender gaps within a range of areas across European Member States, the European Institute for Gender Equality put forward the Gender Equality Index. Prati (2018) used the composite indicator of the Gender Equality Index and its six core domains (work, money, knowledge, time, power and health) plus violence to explore the relationship between gender equality and women’s participation in cycling for transport. The results show that there is a gender difference in participation in transport cycling. Some have made suggestions to improve the travel environment for females to contribute to social equality. In a study in India, large proportions of females preferred walking. Accordingly, Mahadevia and Advani (2016) pointed out that walking infrastructure such as footpaths needs to change to make walking safer for women. According to Bastian and Börjesson (2018), who conducted a study in Sweden that focused on the role of city as a driver of new mobility patterns, women may be less likely than men to have access to company cars, free fuel, free congestion charge, and free parking benefits from their employers. They suggested that a dense urban core may lead to more gender-equal and greener daily travel behaviour.

8.1.4 Existing Evidence in China Studies of the gender difference in travel behaviour in China started later than in Western countries, but they have gradually increased in recent years. Zhang et al. (2008) compared and analysed the characteristics of female travel behaviour in terms of travel rate, travel time, travel purpose and travel mode based on data from the 2005 Beijing Resident Travel Survey. They pointed out that the travel rate of older females is far lower than that of older males. The proportion of female travel for non-work purposes such as shopping and leisure activities is significantly higher than that for males, and the main mode of transport for females is walking, which they do significantly more than males. Wang (2011) used Nanchang Railway–9 District as an example to discuss the choices of travel modes for women of different age groups, using a combination of a questionnaire survey and a field survey. Wang found that the use of bicycles or electric vehicles peaked in the 30–44 age group, second only to public transport. The older the age, the higher the probability of choosing walking. For travel purposes other than work and study, most women choose public transport, and the probability of walking is still positively correlated with their age. The data also show that besides work and for school, women mostly take trips for shopping. In another study, Chai and Zhang (2014) conducted a survey on daily activities and traffic trips of Beijing residents in 2012 through a week’s activity log, and they used it to analyse the time use characteristics of suburban women. They concluded that suburban women’s travel is regular in terms of time. In terms of space, female residents in the suburbs prefer to work near the suburbs, and shopping and leisure activities in daily life are mainly completed in the suburban space. In addition, Gao et al. (2014) conducted a descriptive analysis of the characteristics of those disabled

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due to traffic accidents using the data from a national survey on disabled individuals in 2006, and they found a gender inequality in traffic injuries. He et al. (2017) took Guangzhou as an example and selected some communities for discussion. Based on the differences in travel characteristics between male and female residents, they focused on travel purpose, and they explored the factors causing that gender difference. The proportion of men shopping and picking up children on workdays is only 7.88%, while that of women is 19.41%. Ren and Huang (2018) took the central area of Nanjing as an example to explore the travel patterns and driving habits of female drivers. They found that women’s driving experience was not only determined by the efficiency of traffic, but also related to their sense of comfort, safety, convenience and even the landscape along the way. Thus better people-oriented traffic planning needs to allow for female travellers’ unique expectations for road facilities and transport services to improve their trip experience. Tong and Wang (2018) conducted a study based on a questionnaire survey of residents’ travel in Wuhan and Urumqi. They found that the traditional gender division of labour affects the travel mode of both males and females. Women travel slightly less often for work than men do. However, they may travel more often to take care of their families, such as picking up children and buying goods for daily use. Women’s transport costs are much lower than men’s, and they seem to be more sensitive to travel prices, paying more attention to the affordability of travel. The proportion of women who walk or take public transport is higher than that of men. In general, men place more emphasis on the efficiency for time saving, while women pay more attention to safety. When there is a conflict, larger proportion of women will insist on choosing safety. Gu et al. (2018) found similar results based on a questionnaire survey on trip logs of several streets in Guangzhou. They found that the trip frequency of female residents is higher, while the travel distances are usually shorter, and the travel time and average speed are lower than those of male residents. To conclude, many studies have agreed that the travel activities of most female residents are less restricted by time and space.

8.1.5 Research Gaps Most previous studies have reached a consensus on the gender differences in travel behaviour, such as travel mode, trip frequency and distance, and travel purpose. Multiple factors may be responsible for this phenomenon, including gender inequality in social roles, facility accessibility, travel opportunities and sense of traffic safety. However, there are still some research gaps on exploring and discussing gender differences in travel behaviour. Those research gaps can be summarised in three ways. Firstly, most studies focus on the differences in the choice of travel modes between men and women, while they pay less attention to other travel characteristics, such as trip frequency and travel purpose, travel space span etc., leading to the lack of a comprehensive understanding of the differences between male and female travel.

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273

Secondly, although the reasons behind the gender difference have been discussed, most previous studies are qualitative analyses and lack empirical data. As evidence, the differences in travel opportunities and attitudes behind the differences in travel are still unclear, there are controversies about the interpretation of gender difference in travel behaviour and more empirical cases are necessary. Thirdly, the current gender differences in travel behaviour in China still lack evidence, and previous studies mostly focus only on a certain aspect of travel behaviour or only discuss a certain group of people. There are few discussions on the possible causes of gender differences in travel behaviour. Therefore, cases in China on gender difference in travel behaviour and how to promote gender equity have not yet reached a consensus, and more Chinese cases are needed to provide reference for public policies under the development strategy of building China’s strength in transportation. This chapter addresses the research gaps. It focuses on the gender attribute and the significant position of gender as a basic characteristic of people’s socioeconomic attributes. By comparing males and females in the discussion of residents’ travel characteristics, it analyses the gender differences in residents’ travel behaviour. This chapter compares the characteristics of male and female residents in terms of travel purpose distribution, travel mode choices, trip frequency, travel space span etc. It also reveals the direct or indirect influence of gender factors on residents’ travel decisions. The discussions and conclusions may contribute to a humanised and refined development of transport services, based on the different travel demands of males and females. The key questions for this chapter are: (1) What are the characteristics of travel behaviour for female travellers and male travellers? (2) Is there is a significant gender difference in terms of travel preference? (3) How can transport services adapt to the travel demands of different genders to promote a more humane and fairer transport system?

8.2 Data and Methods 8.2.1 Data To examine the relationship between the current travel behaviour of residents and their gender attributes in China comprehensively, taking Beijing as a typical case city, we used data from Beijing’s comprehensive survey of city traffic issues in 2015. Details about the main contents of this survey project were introduced in the third section of Chap. 1. Among them, the sub-survey of residents’ travel behaviour is the most important content, and it could provide us with sufficient information to understand residents’ travel choices better. In 2015, the survey included 101,815 residents from 40,003 households, of whom 49,253 residents were male and 52,562 were female. The age structures of the male

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and female samples are in Fig. 8.1. Both male and female respondents mainly ranged from 30 to 60 years old, with little gender difference in terms of age structure. The socioeconomic attributes of the male and female samples are in Table 8.1. There was little gender difference in the proportions of each type of household registration. Both the male and female samples had more than 80% with Beijing household registrations. The educational levels of males seemed slightly higher than those of females, while the illiteracy rate of males was slightly lower than that of females. Only 0.45% of males had not received any education, while the proportion of the females was 1.81%. This indicates that women are more likely to face problems during journeys due to their lack of education. According to their employment status at the time of the survey, the valid samples were classified into several categories. Male respondents appeared more likely to participate in social labour than females. More than 50% of male respondents undertook full-time or part-time jobs, while the proportions of retirees, those taking care of the family and the unemployed

Fig. 8.1 Age structures of male and female residents in the 2015 survey

Table 8.1 Distributions of socioeconomic attributes of males and females Socioeconomic attribute Type of household registration Educational level

Category

Female (%)

Local in Beijing

86.11

84.42

Non-local in Beijing

13.89

15.58

Elementary school and below

13.24

15.30

Junior-high and senior-high

48.11

47.63

College and above

38.21

35.26

Uneducated Employment status

Male (%)

0.45

1.81

Full-time/part-time worker

54.44

39.85

Full-time/part-time student

8.12

7.18

Preschool

4.86

4.27

24.94

33.22

Full-time mother

0.67

7.13

Unemployed

6.95

8.35

Retiree

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were significantly lower than those of females. The proportion of housewives with no permanent jobs due to taking care of other family members was 7.13%, which was significantly higher than that of males (0.67%). This indicates that there were significant gender differences in the social division of labour in Beijing, which is inconsistent with the traditional belief that men dominate the outside and women dominate the inside. Women tend to take more responsibility for caring for the family and raising children, making their participation in the social division of labour relatively low. In addition, the survey results indicated that the main occupational types of male and female samples were corporate employees, commercial service employees, and public institution employees. These three types of respondents accounted for 56.0% of the male sample and 61.6% of the female sample. However, there were also gender differences. In the male samples, 10.03% were construction workers, while that proportion in female samples was less than 6%. Just 5.22% of the male sample were full-time drivers, while only 0.38% of the female sample were full-time drivers. At the same time, females were more commonly working in commercial and service industries, dealing with community issues or being teachers or doctors. It seems that in China’s big cities such as Beijing, male residents and female residents not only differ significantly in their participation in the labour market, but also in their types of occupations.

8.2.2 Methods In this chapter, we use descriptive statistical analysis to investigate the characteristics of travel behaviour and the travel preferences of males and females, along with Pearson chi-square tests to verify the gender differences. The advantages and common usage of descriptive statistical analysis have been described in detail in previous sections. We explored the travel behaviour of male and female residents and the potential gender differences by comparing the composition of travel purpose, choice of travel mode, trip frequency and spatial pattern of daily trips. In addition, we also combined the gender differences in travel behaviour with multiple personal socioeconomic attributes to provide explore the possible reasons.

8.3 Analysis 8.3.1 Gender Differences in Travel Purpose Travel purpose is a vital reflection of the primary demand of residents’ travel behaviour. On the one hand, the purpose determines the basic characteristics of

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the trip itself, such as short- or long-distance travel, low or high time cost, and neighbourhood or cross-region spatial range. On the other hand, different trip purposes might lead to travellers holding different expectations and requirements for efficiency, comfort and cost during the trip. For example, a relaxed shopping trip in spare time is likely to require less efficiency but more convenience. According to the 2015 survey results, daily travel activities were mainly based on residents’ homes and workplaces. For male respondents, 30.17% of trips were based on workplaces or schools, while that proportion was lower among female respondents at 23.94%. Meanwhile, trips based on other places such as shopping centres and places of entertainment accounted for a higher proportion among female respondents, at 28.83%. According to the Pearson chi-square test, the progressive significance p value was less than 0.05, indicating a significant difference in the types of destinations between males and females. Based on trips with workplaces/schools/other places than home as destinations, we further divided the travel purposes into 14 types. The progressive significance of Pearson chi-square test p value was less than 0.05, indicating significant gender differences in the detailed structure of travel purpose. Among the surveyed male residents, 51.72% of trips were for work and official trips, while official trips only account for 40.52% of trips for female residents. As the same time, shopping trips occupied an important position for women, with the share of shopping in all travel purposes significantly higher than that of men. Among the trips made by female respondents, 21.27% were related to shopping such as going to supermarkets, vegetable markets, shopping centres and other commercial service places. In addition, trips for entertainment also accounted for a quite high proportion at 14.84%. The composition of travel purposes for male and female respondents indicated that men’s travel activities mainly involve commuting, while female residents have a greater demand for consumption activities. Generally, female residents have a lower participation rate in the labour market than male residents, which leaves them less restricted by full-time or part-time work. In central Beijing, the proportion of male residents engaged in full-time or part-time jobs (54.44%) was significantly higher than that of female residents. More than half (60.15%) of female residents did not have full-time or part-time jobs. Considering those facts, it is reasonable for the share of work-related activities to be smaller in females’ daily lives, allowing many women to manage time more flexibly for shopping, leisure, and social communication such as visiting relatives and friends. Daily travel activities often involve commuting and travel chains are mostly based on H–W–H or H–O–W–O–H, which may cause travellers to be more concerned about the efficiency and convenience of the trips. This kind of gender difference is consistent with the findings in the previous literature (Sayer, 2016). In a study based on the German Mobility Panel 1994 Survey, Scheiner and Holz-Rau (2017) pointed out that women have more complex travel patterns than men, and they may be affected by circumstances in different ways from men. One widely mentioned explanation is that female residents usually take on more responsibility for household chores and raising children, and many of them even leave fixed jobs to take care of family members better. Some studies have found that travel patterns of non-work trips do not vary much by gender unless children are

8.3 Analysis

277 Dining

Go to work

Go to school

Personal affairs

Housework

Entertainment

Go shopping

Visit friends

Deliver goods

Others

100 90 80 70

%

60 50 40 30 20 10 0 Male

Female

Gender

Fig. 8.2 Composition of travel purposes for different gender groups

in the family (Boarnet & Hsu, 2015). Another study based on the Norwegian Travel Survey of 2009 found that having children in the family does not lead to any reduction in men’s working hours, but has a significant effect on women’s labour market participation (Hjorthol & Liva, 2014). According to the 2001 Southern California Household Travel Survey, women in households with children make over 300% more chauffeuring trips than do men living alone (Boarnet & Hsu, 2015). Evidence from the Canadian General Social Survey of 2010 also shows that women spend more time on childcare, domestic responsibilities and shopping (Sweet & Kanaroglou, 2016). Furthermore, women seem to pay more and more time on childcare now (Sayer, 2016). Full-time mothers who spend most of their time and effort on family affairs are still common in some areas, affected by the traditional concept of male dominates outside, female dominates inside on the assignment model in family responsibilities. Although the type of prevalent patterns in the gendered division of labour and social parenting norms may vary internationally, the relative gaps in child-serving travel seem to be universal (Craig & van Tienoven, 2019). As a result, the travel chains of female residents appear much more diversified and complicated than those of male residents (Fig. 8.2).

8.3.2 Gender Differences in Travel Mode According to the survey results, most of the trips in residents’ daily travel chains were made by walking. For male respondents, walking accounted for 32.96% of trips. Besides walking, cars (including private cars, company cars and rental cars) were also commonly used, accounting for 21.59% of trips. Public travel modes such as buses and subways occupied a less importation position. Meanwhile, female respondents

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Car

Subway

Bus

Bicycle

Others

100 90 80 70

%

60 50 40 30 20 10 0 Male

Female

Gender

Fig. 8.3 Structure of mode choices for different gender groups

used walking and buses as their main modes with the usage rates being 42.9% and 21.34%, respectively, while the proportion of trips by car (11.25%) was much smaller than that of male respondents. The results of the chi-square test showed that there were significant gender differences in residents’ choice of daily travel modes, with the progressive significance p value being less than 0.05. The structure of respondents’ choices of daily travel modes for different gender groups in Beijing is in Fig. 8.3. The usage rate of private vehicles (such as cars and bicycles, including electric bicycles) was higher among male residents. To be specific, 21.59% of male residents’ trips relied on driving private cars, while only 11.25% of females’ trips were completed by car. For public transport, 21.34% of the female respondents’ trips were completed by bus, while that proportion for male respondents was only 17.08%. In contrast to male residents’ preferences for private travel modes, female residents seem to rely more on public vehicles such as buses and subways. This gender difference in mode preference might have something to do with the gender difference in ownership of driving licences. More than half (51.11%) of the surveyed male residents in central Beijing had held driving licences, while the ownership rate was less than a third (28.95%) among female residents. Owning a driving licence may provide men with more opportunities to travel independently by driving. In addition, this difference may be a reflection of the different travel preferences and living habits of men and women. In general, private cars are more efficient and more comfortable, but correspondingly more expensive than public travel modes, while public transport has lower cost and higher security. The gender difference in the usage of cars and public transport indicates that male and female residents might have different expectations for the efficiency, experience, and cost of a trip. These results are consistent with the view that men tend to pay more attention to improving efficiency than women but care less about how much a trip costs, while

8.3 Analysis

279

in China’s current social context, women are often the main housekeepers, and they are more concerned about the monetary costs. These findings are consistent with those of previous studies. Using questionnaire data on people’s gender and intra-urban travel behaviour, Ubogu et al. (2010) found evidence for a significant relationship between gender and intra-urban mode choice in Zaria, Nigeria. Based on a unique dataset on residents’ activity and travel diaries in the Galilee region of Israel, Elias et al. (2015) demonstrated that female residents tend to work more within their communities and conduct more daily activities by walking. For the determinants of gender gap in mode choice, some studies offered explanations using the social roles hypothesis. Scheiner and Holz-Rau (2012) claimed that mode choice may be impacted by the gendered roles a person takes in a household. Basari´c et al. (2016) analysed travel patterns of transport users in Novi Sad and found gender differences in the utilisation of various travel modes. They suggested that the reasons for women using passenger cars less frequently than men were traditional societal values and poor economic conditions. Many studies have demonstrated the significant influence of the built environment on people’s travel mode choices. However, these factors may work differently for women and men. Female residents can be more sensitive to environmental and infrastructural conditions (Abasahl et al., 2018). Although the determinants of decision-making for mode choice remains under discussion, the existence of gender differences in using public transport and walking is widely acknowledged. Thus, the configuration of public transport and walking environment facilities needs to consider the special needs of female travellers to ensure the safety and convenience of their trips. To emerge as a viable and modern society, equity and fairness must be assured in the provision of public transport services, and gendered variation in transport demand needs to be taken into consideration.

8.3.3 Gender Differences in Trip Frequency Among the surveyed respondents, 22.04% of males did not take any trips during the day before the survey, while the proportion for female residents was 26.97%. Since men are more likely to be engaged in full-time or part-time jobs, their daily lives might be more limited by commuting from their homes to workplaces, especially on weekdays. Elias et al. (2015) reached a similar conclusion on gender difference in travel pattern in the Arab world: women tend to travel less than men in terms of the number of tours (defined as chain of trip segments that start and end at home). For unemployment time use, female residents also had less time for pure leisure. Using detailed Australian time-use data, Craig and Brown (2017) found that mothers averaged more contaminated leisure such as multitasking housework and childcare than fathers on weekends. Generally in China, the proportion of employees with full-time or part-time jobs is much lower for women, as many are housewives and spend most of their spare time on housework, leading to much less travel demand and more flexible travel patterns.

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For residents who have made at least one trip on the survey day, the average trip frequency for male residents was about 1.19, and the average trip frequency for female residents was about 1.18. Thus, the average trip frequency of male residents is slightly higher than that of female residents. However, some studies have observed quite significant gender differences. Earlier research on gender difference in trip chains has demonstrated that women make more trips than men because of householdsustaining activities, such as shopping, childcare and family errands (McGuckin & Murakami, 1999). Travel diaries collected from communities in the Galilee region of Israel showed that women tend to travel less than men in terms of both number of trips and total time spent traveling (Elias et al., 2015). However, this gender difference does not always appear in studies based on Chinese cities. Ma et al. (2014) used detailed land use data and an activity dairy survey for workers in Beijing and drew similar conclusions to this study. There was no significant difference in trip frequency for commuting trips between male workers and female workers. To conclude, even if there are significant differences between male and female travellers in travel purpose and travel mode choices according to the travel survey held in Beijing, the difference in overall trip frequency does not seem significant after excluding those who have not made any trips during the day.

8.3.4 Gender Differences in Spatial Range Based on the gender attributes of travellers and their residence locations, trips’ spatial range (the OD pattern) of male/female residents in the central area, and male/female residents in the suburbs are visualised in Figs. 8.4 and 8.5. There are some common characteristics in the spatial range between male and female residents’ travel activities. The trips of male and female residents in the central area were both concentrated within the area, while the connection between the centre and the suburbs seemed to be quite weak. The OD pattern of residents’ daily trips all presented a radial pattern within each district. At the same time, a large number of residents living in suburban areas had taken the central area as their destinations and travelled across districts for

Fig. 8.4 OD patterns of male and female respondents living in the central area

8.3 Analysis

281

Fig. 8.5 OD patterns of male and female respondents living in the suburban area

a long distance. Districts such as Haidian and Fengtai, which had a large number of working opportunities, seemed to have closer correlations with suburban areas. However, there are still gender differences in the spatial pattern of residents’ daily trips. Male residents in the central and suburban areas both had larger spatial ranges of trips than female residents. For example, in addition to the extensive travel activities inside the central area, male residents living in the centre also travelled to or from the surrounding suburban areas, indicating that trips made by male residents seemed more divergent than those of female residents, while the trips made by female residents of the central area of city were mainly concentrated in a small spatial range, with much less travel connection with the surrounding suburbs. Some previous studies have demonstrated that the travel distance and travel time of female residents has multiple determinants. For example, a study focusing on women’s commuting behaviour pointed out that income level and marriage seem to be the greatest determinants of travel distance. It found that many low-income women without husbands or with husbands who provide limited financial resources are usually faced with spatial constraints (Carter & Butler, 2008). Even for comparable groups with similar working hours, family types, education, place of living, income, access to a car and occupation, there is evidence that women usually do not commute as far as men (Hjorthol & Liva, 2014). There are multiple possible explanations for these factors. Considering the gender differences in travel purposes discussed above, female residents were more likely to take trips for consumption, leisure and shopping, which are usually not fixed and more flexible. As a result, many female residents can just go to the nearest and most accessible supermarket to meet their demands, with the overall spatial range of trips for female residents being smaller and more concentrated than those of males, while most male residents have to commute to fixed workplaces regularly. Some workers living in the suburbs have to travel to the central area for work every day, since the housing cost in the centre of Beijing is relatively unaffordable, resulting in their trips’ spatial range being much more divergent and longer than those of female residents.

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8.4 Conclusion In this chapter, we have applied descriptive statistics analysis quantitatively to discuss residents’ travel behaviour from the perspective of gender difference, based on the data from Beijing’s comprehensive survey of city traffic issues in 2015. Gender structure is an important part of the population structure, and it is directly or indirectly affected by factors such as fertility concepts, fertility policies, traditional culture and the social environment. China’s sex ratio has been at a high level for a long time. In recent years, as people’s living standards and ideological concepts have changed, the attitudes towards giving birth have also changed. Paying attention to the population’s gender structure is of great significance to understanding the current characteristics and the latest changing trend of the population structure fully, as well as to building a people-oriented transport service system of high quality. Female and male residents appear to have significant differences in life habits, travel behaviour and travel preferences. In this chapter, we have mainly focused on four aspects of travel behaviour to compare and analyse the travel differences between male and female residents, namely travel purpose, travel mode, trip frequency and spatial range of their trips. In terms of travel purpose, male residents mainly travelled for work and official activities in general, while female residents had a higher demand for leisure and consumption activities, which is consistent with the findings of previous studies on gender difference. One possible explanation may be the significant gender gap in residents’ occupational types. In our study, more than half the female respondents had neither full-time nor part-time jobs, indicating a lower participation rate in the labour market on average. Evidence in the literature also showed that females take on more responsibility for caring for household chores and raising children, forcing them to leave permanent jobs. The gender difference in travel purpose is certain to result in different demands and preferences for transport services. In terms of travel mode, male residents have a significantly higher preference for private vehicles than female residents, which is consistent with several previous studies. According to our analysis, men are more likely to drive a car for commuting, while women are more likely to take a bus or the subway. This indicates that female and male travellers might have different expectations and tolerance in terms of the efficiency, experience, and cost of daily trips. As a result, the optimisation of public transport is particularly important for meeting women’s travel demands and improving their travel experience. On trip frequency, the survey data of Beijing residents has shown that the proportion of residents having at least one trip a day and the frequency of trips were both higher for men than for women, and that some female residents did not need to take any trips on the survey day. According to the existing literature, this gender gap may be because female residents usually have more limitations due to householdsustaining activities, such as shopping, childcare and family errands. However, for residents who made at least one trip on the survey day, the average trip frequency showed little gender difference in a megacity like Beijing. As a result, the travel demands of female residents and their special preferences and personal needs deserve

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no less consideration than those of male residents. In terms of spatial range, the range of male residents’ daily trips appeared to be more divergent, and the proportion of cross-regional travel was higher, while female residents tend to take daily trips mainly concentrated within a smaller spatial range, resulting in much less spatial connection between the central areas and suburbs of Beijing. These differences may also be related to the division of labour between men and women to some degree. Evidence has shown that income level and marriage status have great impacts on female residents’ travel distance and travel range, while men are more involved in commuting and are more likely to have mid-distance or long-distance cross-regional commutes. The analysis and discussion in this chapter have explored and discussed the gender differences in residents’ travel behaviour from four perspectives. Against the macro background of social transformation and changes in China’s population structure, paying attention to the differences in travel demand and travel preferences of both male groups and female groups is necessary and can provide important guidance for the development of a people-oriented and sustainable comprehensive transport service system. In response to the differentiated travel demands of men and women, especially the special travel preferences of female travellers, it is necessary to improve public transport services, provide customised and refined services, and strengthen the comfort and safety of people’s daily trips to improve people’s travel satisfaction and life happiness.

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Chapter 9

Relationship Between Education and Travel Behaviour

9.1 Literature Review 9.1.1 Education and Travel Behaviour According to previous studies on the relationship between education and travel behaviour, individuals’ educational level is a significant factor influencing their decision-making for travel behaviour. Yin et al. (2021) took Changchun as an example and used an MNL model to analyse the impact of the built environment on the choice of commuting modes on the basis of spatial heterogeneity. They pointed out that education level and other individuals’ variables (such as gender, age, household registration, income level etc.), after controlling for spatial heterogeneity, all have significant impacts on residents’ mode choices. Liu et al. (2019) took Beijing as an example to study the factors influencing residents’ daily travel satisfaction, and they pointed out that education level is a significant factor. For certain types of travel, education level also has a significant impact. A study on the travel behaviour of residents in small and medium-sized cities showed that the education level of urban residents is positively related to their daily travel space for medical treatment. Whether they have received college education or above might be a key factor in residents’ awareness for medical consumption (Gao et al., 2018). Conclusions about the relationship between education level and travel behaviour have been drawn based on multiple factors. Individuals with higher educational levels are more likely to be users of ride-sourcing services. For example, a case study in San Francisco found that ride-sourcing customers were generally younger and more highly educated than the overall population (Rayle et al., 2016). By examining both frequency and propensity, Deka and Fei (2019) found evidence that people with higher incomes and better education tended to use ride-sourcing more extensively. Veterník and Gogola (2017) identified the main factors and their potential impacts on travel behaviour, and they pointed out that with an increase in the level of education, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Zhao and D. Yuan, Population Growth and Sustainable Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-7470-0_9

287

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the transport energy consumption and the proportion of public transport use would increase as well, while individuals with lower levels of education are more likely to walk (Zhao et al., 2018). Some studies explained the effect of education from a view of lifestyle. For example, individuals with education-oriented lifestyles are more likely to use public transport for their daily trips (Etminani-Ghasrodashti & Ardeshiri, 2015). Some studies focused on groups with certain educational levels to explore the specialised characteristics of their travel behaviour. For example, a study in Iran found that adults in university show a greater preference for private cars than public transport. Etminani-Ghasrodashti and Ardeshiri (2015) pointed out that more cultural and educational context will encourage a preference for driving among such adults in Shiraz. However, the relationship between education level and travel behaviour seems to differ among different countries. Evidence from the Paris metropolitan area in a study on the car usage rate under zero-redundant commuting has shown that groups with high education levels (such as managers, experts and employees) are more likely to use moderate travel modes such as walking and bicycles. Groups with lower levels of education are more likely to use cars (Koesu et al., 2017). Evidence from other countries has drawn some opposite conclusions. Lin and Zuo (2017) took Dalian as an example to discuss the impact of new subways on the usage of buses, and they established a binary logit model with variables related to travellers’ characteristics and travel patterns. The results showed that education level is negatively correlated with the utility of traveling by public transport, which means that people with higher levels of education are less likely to prefer traveling by buses. In developing countries such as India and China, car ownership has stronger links with education levels and occupational types (He et al., 2017; Verma et al., 2016). One possible explanation may be the lower average incomes and standards of living in these countries than in developed countries such as Australia. In the context of China, some studies have taken individuals’ educational levels along with other socioeconomic attributes (such as age, gender, occupation, income level and vehicle ownership) as primary variables related to people’s social status. Based on a residents’ survey across China’s small towns by the Ministry of Housing and Urban–Rural Development of the People’s Republic of China in 2016. Zhao et al. (2020) found that educational level is significantly related to trip frequency. With an increase of education level, the difference in the trip frequency to cities and county seats gradually expanded, and the odds of highly educated people traveling with a high frequency were more than twice those of low-educated people. In addition, the conclusions from previous studies in China have not yet reached consensus on the influence of education level on travel behaviour. A recent study pointed out that when controlling for other basic personal attributes and family attributes, education level does not have a statistically significant influence on decision-making on travel modes among heterogeneous groups (Li, 2018). Another study took Shanghai’s central urban area as an example to analyse the influencing factors for older people’s travel behaviour, finding that the time older people spent on travel and their trip frequency had nothing to do with their gender and education level (Huang et al., 2016). Thus, the relationship between education level and

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289

individual travel behaviour is still shrouded in mist according to existing studies. Unlike other basic attributes, the impact of education level on travel behaviour may be more complicated and requires more empirical evidence to identify and examine the effect.

9.1.2 Research Gaps In summary, many studies of empirical cases in different countries have paid attention to the influence of education level on individuals’ travel behaviour. However, several research gaps remain. Firstly, on the whole, previous studies mainly consider education level as a variable of individual socioeconomic attributes in the explanatory model, along with age, gender, income and other attributes. The results may show that education level has a significant impact on travel behaviour, but further and deeper analysis and explanation are often lacking. The internal mechanism of education’s impact on travel behaviour has not yet received enough attention. Secondly, there is still some debate about the influence of education level on travel behaviour. There is as yet no consensus on whether education plays a role or the direction of any influence. In addition, the conclusions of existing studies often only consider one aspect of travel behaviour. Few studies comprehensively discuss the effects of education level on residents’ behaviour looking at multiple aspects of travel patterns (including travel mode, travel purpose, trip departure time, etc.). Thirdly, compared with study results from other countries, related studies on the impact of education level on travel are even scarcer in China. As a key issue of population quality that is related to social economic development and social equity, education level has not yet received enough attention through exploration of the determinants of people’s travel behaviour in China. Combining education level with social background including regional imbalances and unfairness of educational resources and analysing the population differentiation of travel characteristics from the perspective of education level are necessary to provide more evidence for the promotion of humanity and equity in China’s transport services. This study addresses the research gaps in the following three ways. Firstly, it focuses on the variable of population cultural quality (education level) to analyse differences in residents’ travel behaviour related to education. It discusses the reasons for this relationship from the perspective of direct impact and possible indirect impact. Secondly, multiple characteristics of residents’ travel behaviour, including the departure time of one day’s travel activities, travel mode choice, travel purpose are all taken into consideration to compare the different travel behaviour between different education groups more comprehensively. Thirdly, as a typical megacity in China, Beijing has been taken as an example for discussion, as it could provide empirical evidence and reference for guiding the people-oriented and sustainable development of China’s transport system as well as for better promoting regional transport equity.

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9 Relationship Between Education and Travel Behaviour 1.15%

14.31% Primary school or below

36.68% High school or technical school College or above

Uneducated 47.86%

Fig. 9.1 Education levels of surveyed respondents

9.2 Data and Methods 9.2.1 Data This chapter takes Beijing’s comprehensive survey of city traffic issues in 2015 as the main data source. Based on the survey results, it discusses the potential impact of individuals’ education levels along with other socioeconomic attributes on their travel behaviour. Details about the main contents of this survey project were introduced in the third section of Chap. 1. As previously stated, 49,253 residents were male, and 52,562 residents were female. The gender ratio (male: female) was 93.7. Figures 9.1, 9.2 and 9.3 represent more statistical characteristics about respondents’ education levels, residence location and income levels. According to the survey results, the education level of the surveyed residents in 2015 was mainly junior high school, senior high school, and technical secondary school, followed by junior college and above. The proportion of uneducated respondents was only 1.15%. The distribution pattern of education level seems quite balanced, and it can truly reflect the overall situation of Beijing population quality. To conclude, the samples obtained from the survey are suitable for further discussion and comparison of the differences in travel behaviour among groups with different education levels.

9.2.2 Methods In this chapter, we conducted a descriptive statistical analysis based on the survey data from Beijing. The advantages and common usage of descriptive statistical analysis

9.2 Data and Methods

3.65%

291

3.53% 0.80%

13.60%

9.69% 2.99%

10.71%

13.66%

1.76% 1.72% 1.47% 2.43%

5.99%

Dongcheng Fengtai Daxing Miyun Pinggu Yanqing Huairou Fangshan Changping Chaoyang Haidian Shijingshan Xicheng

5.50%

14.28% 8.21%

Fig. 9.2 Residence (divided by districts) of surveyed respondents

50

Fig. 9.3 Family annual income of surveyed respondents. Unit 10,000 CNY

have been described in detail in Chap. 6. According to the previous literature, individuals’ travel behaviour is often affected by various factors, including both their own attributes and those of their families. In this chapter, the focus is the level of education, and respondents are divided into several groups based on their highest educational level to identify differentiated travel behaviour and transport demand. The travel behaviour of different groups was analysed through comparing the departure times of daily trips, choice of travel mode, and composition of travel purposes. Based on comparison analysis from these three main perspectives, this chapter explores

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the relationship between characteristics of travel behaviour and travellers’ education achievements, considering other relevant personal socioeconomic attributes and investigating the travel habits and travel preferences of different groups.

9.3 Analysis 9.3.1 Departure Time According to the statistical results, the departure time of residents’ travel activities in Beijing is mainly between 7:00 and 8:00, which is in the morning peak. During the morning peak, the urban transport system is usually faced with the huge traffic pressure of carrying a large number of passengers, which may lead to severe traffic congestion problems. Meanwhile, there seem to be differences in the distribution pattern of departure times among groups with different education levels. According to the distribution pattern of the residents’ departure time for daily activities, the morning peak of residents with education level being primary school or even lower appears to be around 7:00 (Fig. 9.4). This means that most people with relatively low qualifications tend to begin the first trip of a day at around 7:00 in the morning. As time goes by, the proportion of residents starting their trips gradually reduces. For the three time points of 7:00/7:30/8:00, the distribution of residents’ departure times appeared to follow a high-medium-low pattern. For residents with junior/senior high school or technical secondary school education, the morning peaks turned out to be at both 7:00 and 8:00, while the proportion of residents travelling at 7:30 was somewhat lower (Fig. 9.5). For the three time 40 35 30

%

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Time Fig. 9.4 Departure times of residents with low education levels

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20 18 16 14

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Time Fig. 9.5 Departure times of residents with median education levels

points of 7:00/7:30/8:00, the distribution of residents’ departure times appeared to follow a high-low-high pattern. For residents with junior college or higher education levels, the morning peak also occurred at both 7:00 and 8:00, like the group with middle education level (Fig. 9.6). What is different, however, is that the proportion of residents beginning their first trip of the day at 7:30 also seems to quite high. For the three time points of 7:00/7:30/8:00, the distribution of residents’ departure times appeared to follow a high-high-high pattern. For the group of residents who did not complete any systematic education in schools, survey results have shown that the morning peak occurs at around 8:00 and 25 20

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Time Fig. 9.6 Departure times of residents with high education level

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9:00, which is a little later than that of other education groups. One explanation may be that uneducated people often have difficulty in obtaining permanent employment opportunities from formal companies due to their lack of high academic qualifications and professional skills. Instead, they are more likely to engage in relatively temporary, flexible and diversified work, which may not require regular commuting in the morning, such as self-employed businesses, part-time workers or freelancers. The gaps in employment status and labour participation among different education groups may also be responsible for the uneducated group’s more scattered distribution pattern of departure times during the day. According to the results, their departure time for daily trips was not highly concentrated on certain time points, unlike other groups. At the time points of 6:00/6:30/7:00/7:30/8:30, quite a few people begin to travel for the first time that day. Some respondents did not begin their first trip of the day till around 15:00 (Fig. 9.7). Another possible reason is that uneducated respondents do not include preschool children or students who are receiving education; thus, the pattern of departure time is not influenced by schooling trips early in the morning. As a result, their travel times might be more flexible and more likely to be under their own control. However, previous studies on the relationship between education and people’s travel patterns have paid little attention to the characteristics of departure time, and there is still a lack of empirical evidence on the deeper determinants of education-group differences in departure time. 25 20

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Time Fig. 9.7 Departure times of uneducated residents

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9.3.2 Mode Choice According to the statistical results, the most commonly used travel modes in Beijing are walking, cars, buses and bicycles. However, groups with different levels of education seem to have different preferences for their main travel mode. 1) Higher education level, more preference for public transport According to the survey results, groups with higher education levels appeared more likely to take public transport in their daily trips. Based on the collected information, we divided the commonly used travel modes into four main categories: public transport (e.g., buses and subways), motorised vehicles (e.g., private cars and taxis), non-motorised vehicles (e.g., private bicycles and share bicycles) and walking. The usage rate of each category among groups with different education levels are shown in Table 9.1. The results show that people with higher education levels are more likely to use public transport as their travel mode. For the uneducated sample, only 10.16% of trips were made by public transport. For the group with median educational level, 20.99% of trips were made by public transport. For the high education level (junior college and above) group, public transport accounted for 38.43% of travel. Taking buses as an example, the usage rate of residents with primary school or lower education level was less than 9%, while the group with junior/senior high school and technical secondary school education level took 14.32% of their trips by bus. That proportion was 18.84% among those with college and higher education levels. Taking the subway as another example, the usage rate of residents with primary school or lower education level was less than 1%. Among them, uneducated residents rarely used the subway (0.08%) in their travel activities. For those with junior/senior high school and technical secondary school education levels, 1.92% of the trips were completed through the subway. The trips of residents with college and higher education level were the most dependent on the subway, with the usage rate being 9.14%. The significant differences in travel mode preferences among residents with different education levels has shown that although the coverage of public transport in China has become wider and the construction of facilities has been largely improved, most users appeared to have median or high education levels. It is possible that residents with low educational backgrounds and relatively inferior educational Table 9.1 Usage rates of different travel modes (%) Travel mode

Uneducated

Primary school and lower

Public transport

10.16

10.75

20.99

38.43

3.59

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13.25

25.95

Non-motorised

11.81

22.59

24.29

12.65

Walking

74.43

58.66

41.46

22.97

Motorised

Junior/senior high and technical secondary school

Junior college and higher

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qualities have more limited access to public travel modes. Considering the operation and management of the public transport system in a megacity such as Beijing, the relatively complex route integration and the diverse combinations of entrances and exits may cause some difficulties for residents with relatively low education levels. At the subway transfer stations or the major stations on the circle line, some passengers are still asking passers-by how to find the right exit and are not sure about the correct direction of the line. As a result, some uneducated residents may not be able to complete daily travel through the subway due to difficulties in identifying the location of subway stations, obtaining route information, and purchasing tickets independently. It is necessary to promote the humanised operation of urban public transport service system further to meet the diversified travel demands of these vulnerable residents with difficulties. 2) Higher education level, more likely to drive a car According to the survey results, groups with higher levels of education had significantly higher dependence on motorised vehicles such as cars and taxis than groups with lower levels of education. The proportions of residents using cars as their travel mode for daily trips among the groups with no education, with elementary school education, with junior/senior high school or technical secondary school education, and with college education or even higher, were respectively 3.01%, 7.34%, 12.00% and 22.81%. There seems to be a positive correlation between educational level and car usage rate. Most respondents in the group with relatively lower education levels used public transport or other non-motorised vehicles (such as bicycles or electric bicycles) or just walked, while residents with a college or higher education level had the highest preference for traveling by cars. In addition, the usage rate of bicycles did not show a significant difference among different educated groups. For uneducated residents, bicycles accounted for a relatively lower proportion of 6.67% in their daily trips, while the usage rates of the other three groups were quite similar within the range 10%–16%. Compared with private cars and taxis, bicycles are often more flexible and easier to operate, as well as being cheaper and more environmentally friendly. For daily trips in megacities, traveling by bicycles is available and accessible to most residents whether their education level is relatively high or low. Previous studies have also found some significant relationship between people’s education levels and their preferences for travel modes. For commuters, evidence from Nanjing in China demonstrated a close relationship between education, gender, occupation, income and people’s commuting travel mode choice (Lv et al., 2013). Another study on commuter trips involved an online survey to investigate the contributing demographic characteristics and their travel behaviour for commuting trip patterns by female EV owners. The results showed that education and income level both played a significant role in commuting travel behaviour (Nickkar et al., 2019). Based on household interview survey data in Dhaka, Bangladesh, Rahman (2020) reached a similar conclusion. Statistical analysis indicated that mode preference for walking and rickshaw use gradually decreased with increases in educational level. This is consistent with our result that higher education levels bring more car use and lower education levels bring more walking trips. Taking Ghent in Belgium

9.3 Analysis

297 Walk

Car

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Bus

Bicycle

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100 90 80 70

%

60 50 40 30 20 10 0

Primary school or below

High school or technical school

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Education level

Fig. 9.8 Travel mode choices of groups with different education levels

as a case, students also experienced a transition of travel behaviour from secondary schools to higher education such as colleges. More specifically, Paepe et al. (2018) found a significant difference in car use. What is more, people with higher education are more likely to use new information technology relating to taking travel. A study based on user data from Facebook found that more educated people are more active in getting and sharing information about trips (Boi & Jovanovi, 2017) (Fig. 9.8). Despite the significant effect of education, few studies explored the reason for education’s effect on travel mode choice further. One possible explanation links the influence with these groups’ different lifestyles (Paepe et al., 2018). In general, highly educated people tend to have more opportunities to access various travel modes, and they are more likely to be capable of using technological transport vehicles. As a result, owning a driving licence or getting accustomed to car sharing seems much easier for people with higher education levels. However, this explanation may not always be effective. In addition, there is still a lack of deeper explorations for causal inference. With the rapid development of China’s society and increase in education level, making it more difficult to obtain a driving licence and to buy a car may make sense to discourage car use.

9.3.3 Travel Purpose According to the statistical results, the common types of travel purposes among these groups have been similar, all including travel for working, leisure and entertainment, shopping and picking up others. Other less common travel purposes include traveling for dinner and parties, for personal affairs, and for visiting friends and relatives.

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Meanwhile, there are still differences among groups of different education levels in terms of the composition structure of their travel purpose. 1) Highly educated people have more commuting trips According to the survey results, groups with higher education levels appeared to take more trips for work or official affairs. For uneducated respondents, only 9.18% of trips were made for work, while for the group with junior/senior high school or technical secondary school education level, that proportion was 35.53%. For the group with junior college or higher education, 63.7% of the trips were for work. For those with relatively lower education levels, it might be much more difficult to find a fixed occupation in the city, especially with educational thresholds getting higher and higher. Some of them could only be employed in part-time jobs or more flexible types of work, leading to more varied travel purposes on weekdays. Meanwhile, most people with higher education levels were employed in some fixed occupations; thus, a much higher proportion of their trips on weekdays were for commuting. Based on the survey results, there are significant differences among groups with different educational qualifications, after excluding respondents who were still receiving education at the survey time (including full- and part-time students). Of the group with no systematic education, 45.77% were retirees, while only 4.88% were engaged in full-time, permanent jobs. For the group with primary school as the highest educational level, the share of retirees was 56.58%, while the proportion of full-time workers was 12.16%, higher than that of the uneducated group but still at a low level. Since the group with lower education levels was mostly composed of retirees, it is reasonable for them to have little demand for commuting. For the group with junior/senior high school or technical secondary school as the highest educational level, the largest numbers were retirees and full-time workers, accounting for 42.45% and 40.58% respectively, while the unemployed accounted for less than 10%. For the group with junior college or higher education (including college, undergraduate, master’s, and doctor’s degrees), full-time employees were the main body, accounting for 75.57%, and retirees accounted for only 17.00%. The total of other personnel categories (including part-time workers, the unemployed and others such as housewives) was less than 10%. This indicates that residents with higher education levels were mostly employees with permanent jobs, resulting in a larger demand for commuting trips (Table 9.2). In addition, these differences in travel purpose and employment status may contribute to the differentiation in lifestyles and travel habits. 2) Less educated people have more flexible trips According to the survey results, although leisure, entertainment and shopping have occupied a significant position among travel purposes of these four groups of residents, there are still differences in their preferences. The group with lower education levels seemed more likely to travel for leisure and entertainment, or for shopping. Taking trips for leisure and entertainment as an example, 38.68% of the trips by uneducated residents were made for this purpose. As the education level increases, this proportion seems to reduce gradually, and it was only 7.12% among the group with highest education level. For shopping and consumption, 29.82% of the trips by

9.3 Analysis

299

Table 9.2 Employment status of groups with different education levels (%) Type of travel mode

Uneducated

Primary school and below

Junior/senior high and technical secondary school

Junior college and above

Full-time workers

4.88

12.16

40.58

75.57

Part-time workers

0.51

0.93

1.63

0.94

Retirees

45.77

56.58

42.45

17.00

Housewives

11.89

11.33

5.36

2.08

Unemployed

34.90

18.38

9.62

4.17

2.05

0.61

0.36

0.25

Others

uneducated residents were for this purpose, while that proportion was only 9.62% for the group with junior college or higher education levels. Taking into account the differential distribution of employment status among the groups with different levels of education, the better educated residents with college degrees or above are mostly those with fixed occupations (part-time or full-time work). As a result, their travel activities on weekdays may rely more on work and official affairs, while trips for their leisure and entertainment held a less important position and mostly took place during holidays (Fig. 9.9). Dining

Go to work

Go to school

Personal affairs

Housework

Entertainment

Go shopping

Visit friends

Deliver goods

Others

100 90 80 70

%

60 50 40 30 20 10 0

Primary school or below

High school or technical school

College or above

Education level

Fig. 9.9 Travel purposes of groups with different education levels

Uneducated

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9 Relationship Between Education and Travel Behaviour

9.4 Conclusion The quality structure of the population is an important part of the population structure, even though it has received less research attention than other aspects of population structure. In general, population quality includes physiological quality (such as health status and life length) and cultural quality (such as education level and ideology). With the improvement of China’s socioeconomic development, the overall quality of the population has been continuously improving, with the average lifespan length longer and the average education level increasing significantly. The optimisation of the population quality structure is likely to bring about huge changes in people’s living habits and travel demands. To build a high-quality, people-oriented and sustainable transport system based on the characteristics of population growth and its structure, it is necessary to pay attention to population quality and its relationship with people’s travel behaviour. This chapter has taken education level as an indicator for population quality, and analysed the relationship between travel pattern and residents’ education levels using data from Beijing’s comprehensive survey of city traffic issues conducted in 2015. The comparison mainly focuses on three aspects: the departure time of the daily trips, the choice of travel mode, and the composition of travel purpose. According to the statistical results, there are significant differences in the travel habits of residents with different education levels in the following aspects: (1) The morning peak time of Beijing residents is mainly between 7:00 and 8:00, but the travel times of residents with different education levels are different. Preschool children are affected by school time, and their morning peak of travel is mainly around 7:00, while the morning peak time of residents with junior high school and college education is mainly at 7:00/7:30/8:00. However, the morning peak time of uneducated residents is not that obvious, and their departure times are relatively evenly distributed within a day. This indicates that the travel activities of uneducated residents are relatively free and more flexible, which may be related to their employment status and occupational types, as they are mostly migrant workers and freelancers. (2) There are differences in the choices of travel modes among travellers with different levels of education. The results show that the level of education is positively correlated with residents’ dependence on public transport and private cars, but it is negatively correlated with their dependence on walking. That is, people with higher levels of education are more likely to use public transport or cars to travel, and people with lower levels of education are more likely to travel by other means such as walking and cycling. Education level may influence the choice of travel mode in many ways, such as travel concept, environmental protection awareness, expectation for comfort and safety, consideration of travel efficiency, travel cost, etc. This difference in travel characteristics shows that population quality, especially educational level, has a close relationship with travel behaviour. A transport system that adapts to population development should pay attention to the travel needs of different populations.

References

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(3) There are differences in the purposes of travel among travellers with different education levels. The results show that the purpose of travel for people with high educational levels is mainly related to work and official duties, and therefore this group has more significant morning and evening peak characteristics, while the travel purposes of people with relatively low education appear to be more diversified. Travel purposes such as leisure, entertainment and shopping all account for higher proportions among low education groups than among high education groups. The data have shown the differentiated travel characteristics and living habits of residents with different education levels. By analysing the relationship between population quality and travel behaviour with the level of education as an indicator, this chapter provides an empirical basis for the development of a higher quality and humanised transport service system. With the general improvement in the quality of China’s population, hightech and emerging modes of travel facilities or transport services have gradually been promoted. However, the education levels in some remote areas or areas lacking educational resources are still uneven, and the travel demands of some people with low education or relatively disadvantaged groups need to receive more attention. To make future transport better adapted to the characteristics of the population structure, to meet the differentiated and refined travel demand of different groups of people better, and to give play to the role of transport services in caring for disadvantaged groups and promoting social equity, it is necessary to pay attention to the differences in the travel behaviour of residents with different education levels, and to discuss the impact of population quality on travel behaviour.

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Koesu, E., Nechet, F. L., & Zou, Z. (2017). Ling rong yu tong qin qing kuang xia shi fou hui you geng shao de ren shi yong xiao qi che chu xing? [Will fewer people travel by car with zeroredundancy commuting? An exploration of the Paris metropolitan area]. Urban Planning Forum (04), 122–123. Li, Y. (2018). Jia ge tiao kong ce lve dui cheng shi ju min chu xing fang shi jue ce xing wei de ying xiang yan jiu. (Ph.D.), Southeast University. Lin, Z., & Zuo, Z. (2017). Dalian xin jian di tie dui gong jiao chu xing xuan ze de ying xiang. [Impact of new built subway on public transport mode choice behavior in Dalian]. Transport Research, 3(01), 31–38. https://doi.org/10.16503/j.cnki.2095-9931.2017.01.005. Liu, G., Ma, J., Chai, Y., & Guan, M. (2019). Ju min ri chang chu xing te zheng yu kong qi wu ran bao lu dui chu xing man yi du de ying xiang. [The impact of residents’ daily travel characteristics and air pollution exposure on travel satisfaction: A case study of Beijing]. Urban Development Studies, 26(09), 35–42+124. Lv, T., Lin, L., & Gu, Y. (2013). Suburban commuter’s choice behavior of travel mode: A case study in Nanjing. In Paper presented at the International Conference on Transportation Engineering. Nickkar, A., Shin, H. S., & Farkas, A. (2019). Analysis of ownership and travel behavior of women who drive electric vehicles: The case of Maryland. Paepe, L. D., Vos, J. D., Acker, V. V., & Witlox, F. (2018). Changes in travel behavior during the transition from secondary to higher education: A case study from Ghent, Belgium. Journal of Transport Land Use, 11(1). Rahman, F. I. (2020). Analysing the factor influencing travel pattern and mode choice based on household interview survey data: A case study of Dhaka city, Bangladesh. Scientific Journal of Silesian University of Technology Series Transport, 109, 153–162. Rayle, L., Dai, D., Chan, N., Cervero, R., & Shaheen, S. (2016). Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco. Transport Policy, 45, 168–178. https://doi.org/10.1016/j.tranpol.2015.10.004. Verma, M., Manoj, M., & Verma, A. (2016). Analysis of the influences of attitudinal factors on car ownership decisions among urban young adults in a developing country like India. Transportation Research Part F Traffic Psychology & Behaviour, 42(pt.1), 90–103. Veterník, M., & Gogola, M. (2017). Examining of correlation between demographic development of population and their travel behaviour. Procedia Engineering, 192, 929–934. https://doi.org/10. 1016/j.proeng.2017.06.160. Yin, C., Shao, C., Wang, X., & Xiong, Z. (2021). Kao lv kong jian yi zhi xing de jian cheng huan jing dui tong qin fang shi xuan ze de ying xiang. [Imfluence of built environment on commuting mode choice considering spatial heterogeneity]. Journal of Jilin University(Engineering and Technology Edition), 45(05), 840–845. Zhao, C., Nielsen, T. A. S., Olafsson, A. S., Carstensen, T. A., & Meng, X. (2018). Urban form, demographic and socio-economic correlates of walking, cycling, and e-biking: Evidence from eight neighborhoods in Beijing. Transport Policy, 64, 102–112. https://doi.org/10.1016/j.tranpol. 2018.01.018. Zhao, P., Yu, Z., & Jia, Y. (2020). Zhong guo cun zhen ju min kua qu yu chu xing yu xiang cun di yu xi tong diao cha yan jiu. [Cross-regional travel and regional system of rural China]. Scientia Geographica Sinica, 40(04), 498–508. doi:https://doi.org/10.13249/j.cnki.sgs.2020.04.001.

Chapter 10

Policy Implications

10.1 Current Policies Review 10.1.1 People-Oriented Transport Services 1) An overall introduction to the relevant policies Since the 18th Party Congress, putting people first and pursuing comprehensive, balanced and sustainable development have played a crucial role in developing socialism with Chinese characteristics. They are important strategies put forward by the party based on China’s reality in the new century and new stage. Hu’s report to the 18th Party Congress pointed out that As we advance toward the future, the whole party must more purposefully take putting people first as the core requirement for thoroughly applying the scientific outlook on development. We must always make realising, safeguarding and developing the fundamental interests of the overwhelming majority of the people the starting point and goal of all the work of the party and country. We must respect the people’s creativity, protect their rights and interests, and make continued progress in enabling the people to share in the fruits of development and in promoting the well-rounded development of the person. (18th Party Congress, 20121 )

It is obvious that the demand of people should be and has been of great importance since people are the fundamental and vital basis for guiding the construction and development of all aspects of China’s society and economy. On October 20, 2011, the Ministry of Transport of the People’s Republic of China issued the Outline of the 12th Five-Year Development Plan for the Road Transport Industry (Ministry of Transport, 2011, No. 590).2 This document has emphasised 1

Data source: China Daily, http://language.chinadaily.com.cn/news/2012-11/19/content_1594 1774_2.htm, visited on April 15, 2021. 2 Data source: Ministry of Transport of the People’s Republic of China, https://xxgk.mot.gov.cn/ 2020/jigou/zhghs/202006/t20200630_3319628.html, visited on April 15, 2021. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Zhao and D. Yuan, Population Growth and Sustainable Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-7470-0_10

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the important position of the people-oriented concept in the development of the integrated transport system. During the 12th Five-Year Plan period (2006–2010), China’s transport infrastructure construction has made major achievements, and the quality of transport services has greatly improved. However, given people’s ever-growing needs for a better life, there are still problems to be solved such as insufficient transport capacity and difficulty in effectively adapting to economic and social development. Based on the reality, the development plan for the 12th Five-Year period (2011–2015) pointed out the great importance of “adapting to the need of social and economic development,” and it had regarded it as the core requirement of improving the transport infrastructure network, building a comprehensive transport system and deepening the reform of the transport system in this key period. It is essential to take being people-centred and putting security first as the basic principles for developing a better transport system in China. With decades of hard work, socialism with Chinese characteristics has crossed the threshold into a new era. In the report at 19th Party Congress, President Xi pointed out that As socialism with Chinese characteristics has entered a new era, the principal contradiction facing Chinese society has evolved. What we now face is the contradiction between unbalanced and inadequate development and the people’s ever-growing needs for a better life.… The needs to be met for the people to live better lives are increasingly broad. Not only have their material and cultural needs grown; their demands for democracy, rule of law, fairness and justice, security, and a better environment are increasing.

To achieve the goal of developing China into a great modern socialist country that is prosperous, strong, democratic, culturally advanced, harmonious, and beautiful, we must focus on this principal contradiction. It has also been pointed out that Building on continued efforts to sustain development, we must devote great energy to addressing development’s imbalances and inadequacies and push hard to improve the quality and effect of development. With this, we will be better placed to meet the ever-growing economic, political, cultural, social, and ecological needs of our people, and to promote well-rounded human development and all-round social progress. (19th Party Congress, 20173 )

From this report, it is obvious that the concept of putting people first plays an essential role in realising socialist modernisation, improving living standards and achieving modernisation. On February 3, 2017, the State Council of the People’s Republic of China issued the 13th Five-Year Development Plan for a Modern and Comprehensive Transport System (State Council, 2017, No. 11).4 It pointed out that the period of the 13th Five-Year plan will be critical for supporting the building of a moderately prosperous society in all respects, the key period for optimising the network of transport, and the transformation period for improving quality and efficiency. The new starting 3

Data source: Xinhua, http://www.xinhuanet.com/english/special/2017-11/03/c_136725942.htm, visited on April 15, 2021. 4 Data source: National Development and Reform Commission, https://www.ndrc.gov.cn/xxgk/ zcfb/qt/201703/t20170302_967109.html, visited on April 15, 2021.

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point of development puts forward new requirements on the transport. The plan has emphasised that “transport is to serve the people” as one of the guiding ideologies for transport development. It is obvious that the diversified needs for a better life have become an important basis for guiding the optimisation and improvement of transport services in the new era. On September 19, 2019, the Central Committee of the CPC and the State Council issued the Outline for Building China into a Country with Strong Transport Network. Against the background of the new era of building a modern and powerful socialist country, the overall process of building China into a country with a strong transport network requires “adhering to the people-oriented development concept” We must build up a country with strong transport network that satisfies the people, has strong guarantees, and makes China the leading power in the world. Only in this way can the transport system provide strong support for the modernisation of society and the realisation of China’s great dream. 2) A detailed overview of the relevant policies Based on the people-oriented development concept and the guiding ideology of serving the people, there have been many relevant policies and documents. These policies have responded to how the transport service system can establish a closer relationship with the basic needs of people’s lives, and they have provided guidance on solving the main social contradictions in the new era of China’s development. In this section, we summarise representative policies and documents, which are in Table 10.1. According to the relevant policies and documents issued in recent years, it is obvious that serving the people has increasingly become an important guiding ideology and planning principle for promoting the development of China’s transport system. The development of China into a country with a strong transport network must adhere to the core position of people-oriented and promote the transport system to meet the actual needs of residents in daily travel activities in the new era better. It should be taken not only as a guiding ideology, but also as a code for accurately identifying residents’ needs for better trips and better life, promoting the construction of a high-quality transport service system in the new era, enhancing residents’ happiness and sense of gain and contributing to building a country with strong transport network. On November 24, 2015, the Basic Department of National Development and Reform Commission issued the Plan for a Comprehensive Transport Network in Urbanised Areas (Basic Department of the NDRC, 2015, No. 2706). The plan emphasised that the optimisation of the development of the comprehensive transport network in urbanised areas should be based on the development levels of the areas, industrial characteristics, population distribution, spatial patterns, resource endowments, scientific planning and development goals, network layout, key tasks, policy measures etc. In addition, the process of development should start from reality, distinguish the priorities, reasonably determine the construction sequence, and select economical and applicable technical standards, facilities and equipment etc. The emphasis on regional population distribution has reflected the basic principle of

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Table 10.1 The people-oriented concept in the policies and documents on China’s transport development in recent years Issue date

Document name

Relevant policies

March 31, 2015

Implementation Opinions on Better Serving Migrant Workers in the Transport Industry (Highway Bureau of the Ministry of Transport, 2015, No. 39)

“Actively support local migrant workers to find employment nearby and increase the actual income of rural migrant workers.”

November 24, 2015 “Plan for a Comprehensive Transport Network in Urbanised Areas (Basic Department of the NDRC, 2015, No. 2706)

“Optimisation development of the comprehensive transport network in urbanised areas should be based on the development level of the areas, industrial characteristics, population distribution, spatial patterns, resource endowments, scientific planning and development goals, network layout, key tasks, policy measures, etc.”

July 25, 2016

Outline for the 13th Five-Year Development of Urban Public Transport (Ministry of Transport, 2016, No. 126)

The plan takes “people-oriented” and “improving quality and efficiency” as the basic principles, and it will “make efforts to build an efficient, convenient, safe and comfortable, economical and reliable, green and low-carbon urban public transport system.”

February 13, 2017

Plan for the National Civil Transport “To build an overall layout of Airport Layout (Basic Department airports coordinated with of the NDRC, 2017, No. 290) population distribution and resource endowment and adapted to the national land development as well as urbanisation pattern.”

August 8, 2017

Guiding Opinions on Promoting the Healthy Development of Small-Micro Rental Cars for the car rent industry (Ministry of Transport, 2017, No. 110)

November 20, 2017 Plan for the 13th Five-Year Development of Railway Transport (Basic Department of the NDRC, 2017, No. 1996)

“To comprehensively consider factors such as population size, economic development level, residents’ travel demand and urban traffic conditions to formulate a development plan for small and micro rental cars.” “To highlight the concept of promoting convenience and benefit to the people” while improving the quality of railway transport services “To adapt to the travel demand for integrated and high-quality services.” (continued)

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Table 10.1 (continued) Issue date

Document name

December 31, 2017 Guiding Opinions on Accelerating the Development of Integrated Transport for Travellers (Ministry of Transport, 2017, No. 215)

Relevant policies “Adhere to the people-oriented development concept to comprehensively improve the service efficiency and quality of the entire travel chain and all links of the passenger travel and effectively improve the experience of travellers.”

July 12, 2018

Three-Year Plan for Safer Transport “To adhere to putting people first, (2018–2020) (General Office of the firmly establish the concept of safe Ministry of Transport, 2018, No. 86) development, and deepen the construction of safer environment.” “To promote reform and innovation and improve the safety system.” “To resolutely curb serious and serious work safety accidents.”

May 31, 2019

Green Travel Action Plan “To improve the experience of (2019–2022) (Ministry of Transport, public travel, provide the public 2019, No. 70) with accurate and reliable information of buses such as real-time location and estimated arrival time.” “To provide humanised and refined road space and traffic design.”

November 24, 2019 Implementation Opinions on Improving the Traveling Environment on Holidays and Promoting Tourism Consumption (NDRC, 2019, No. 1822) February 10, 2020

“To optimise the travel environment on holidays” and “to further alleviate traffic congestion caused by concentrated travel on holidays.”

Development Strategy for Smart car “With the goal of building China Innovation (NDRC, 2020, No. 202) into a country with strong smart car industry, and through the way of promoting the development of industrial integration.” “To move forward to opening up new models, cultivating new business formats, improving the basic capabilities as well as the level of the industrial chain, and meeting the people’s growing needs for a better life.” (continued)

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Table 10.1 (continued) Issue date

Document name

Relevant policies

February 19, 2020

Notice on Providing Point-to-Point Assurance for Rural Migrant Workers Returning to Work (MHRSS, 2020, No. 4)

On the basis of “fully understanding people’s travel demand” to “provide pre-departure services and organise return-to-work services.” “To formulate a special chartered transport plan based on the needs of rural migrant workers to return to work.” “To provide ‘point-to-point’ assurance services for rural migrant workers with a large scale and similar destinations”

March 7, 2020

Notice on “Point-to-Point” Healthcare Services for Rural Migrant Workers Returning to Work (MHRSS, 2020, No. 15)

“To do a good job in healthcare during traveling for rural migrant workers” and “to provide point-to-point” healthcare services for rural migrant workers to ensure that migrant workers can safely and efficiently complete their return trips

April 2, 2020

Plan for Higher Quality Integrated Development of Transport in the Yangtze River Delta (Basic Department of the NDRC, 2020, No. 529)

It is pointed out that “adaptability to land and space, population distribution, and industrial layout needs to be strengthened.” “To promote higher quality integrated development and to realise the coordinated development of ‘transport, urban morphology, and population layout.’”

April 10, 2020

Opinions on Promoting the Connection of Rail Transport Between Hubs and Airports (Basic Department of the NDRC, 2017, No. 576)

“To firmly establish a people-oriented development idea, to ensure that the development of rail transport at the hub airport matches the diverse travel demand of the people and build a modern comprehensive transport system.”

June 8, 2020

Notice on Making Efforts in Transport to Promote Consumption Expansion and Quality Improvement (General Office of the Ministry of Transport, 2020, No. 26)

“In response to the difficulties and pain points faced in terms of travel services, it is necessary to provide faster and more convenient travel services, promote green and efficient development of freight logistics services, open up the ‘blocking points’ that restrict consumption in the transport field, and enhance consumption vitality.”

10.1 Current Policies Review

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being people-oriented in the development of comprehensive transport networks in urbanised areas. The population distribution of an area could reflect various aspects of the population structure, and it is related to the travel demand of the area. In formulating the development plan for the transport network, full consideration of the regional population distribution is an important manifestation of promoting the quality and efficiency of comprehensive transport services by putting people first. a. Land transport services In terms of land transport services, on July 25, 2016, the Ministry of Transport of the People’s Republic of China organised the compilation and issuance of the Outline for the 13th Five-Year Development of Urban Public Transport (Ministry of Transport, 2016, No. 126). The development plan was designed to promote the in-depth implementation of the strategy, which prioritises the development of urban public transport, gives full play to the role of urban public transport in improving urban transport’s quality and makes contributions to coordination and sustainable development. The plan takes being people-oriented and improving quality and efficiency as the basic principles, and it will “continuously satisfy the basic needs of the people, as well as realise, protect, and develop the basic travel rights of the people.” The plan and goals have fully reflected the concern for people’s travel demands and travel experience in the development of public transport. Like urban public transport, China’s railway construction has also taken people’s actual needs as the basis for development. On November 20, 2017, the Basic Department of the National Development and Reform Commission issued the Plan for the 13th Five-Year Development of Railway Transport (Basic Department of the NDRC, 2017, No. 1996). It pointed out that the development of China’s railways in the new era should “highlight the concept of promoting convenience and benefit to the people” while improving the quality of railway transport services. The plan requires railway transport to “adapt to the travel demand for integrated and high-quality services” and to “actively develop interline transport services for passengers.” Under the guidance of this concept, the high-quality development of China’s railways during the 13th Five-Year period has led to remarkable improvements. Supply capacity, service quality, and safety levels of railway transport continue to improve. From 2015 to 2019, the national railway passenger volume increased by 9.6% annually, and the freight volume increased by 6.9% annually. The passenger and freight transport capacity have been greatly improved, and travel has become more convenient and energy-efficient. The transport of key materials such as energy and resources has been strongly guaranteed. Tickets for passengers and freight have all become electronic. The protection capabilities for emergencies have been significantly enhanced. The concept of being people-oriented has been effectively embodied in the construction and development of China’s transport network. b. Air transport services In terms of air transport services, China’s airport construction also fully embodies the concept of being people-oriented and serving the people. On February 13, 2017, the Basic Department of the National Development and Reform Commission issued

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the Plan for the National Civil Transport Airport Layout (Basic Department of the NDRC, 2017, No. 290). The plan clarified the principles for the layout of civil transport airports, including in accordance with the development of “One Belt One Road”, the coordinated development of Beijing-Tianjin-Hebei, the Yangtze River Economic Belt and related regional development strategies, as well as the strategy for functional zoning and new urbanisation which is people-oriented, and overall consideration of the convergence between economic and social development and various modes of transport.

Based on these principles, the layout of airports must be built coordinated with population distribution and resource endowment and adapted to national land development as well as the urbanisation pattern. The consideration for population distribution has reflected the importance of the regional population structure and demographic characteristics for airport construction. On April 10, 2020, the Basic Department of the National Development and Reform Commission issued the Opinions on Promoting the Connection of Rail Transport between Hubs and Airports (Basic Department of the NDRC, 2017, No. 576), which further embodies the people-oriented development concept. It requires the establishment of a people-oriented development idea to ensure that the development of rail transport at the hub airport matches the diverse travel demand of the people, and to build a modern comprehensive transport system. c. Emerging forms of transport services For emerging forms of transport services, population demand is also an important basis for guiding the healthy, effective, and high-quality development of these services. On August 8, 2017, the Ministry of Transport issued the Guiding Opinions on Promoting the Healthy Development of Small and Micro Rental Cars for the car rent industry (Ministry of Transport, 2017, No. 110). It points out that the development of small and micro rental cars should be based on the “local economic and social development and residents’ travel demand.” It is necessary to consider factors such as population size, economic development level, residents’ travel demand and urban traffic conditions comprehensively to formulate a development plan for small and micro rental cars. Under such context, many cities have actively promulgated corresponding policies to clarify their development plans for new models of transport services. On May 31, 2018, the Shanghai Municipal Transport Commission issued the Management Implementation Rules for Shanghai’s Small and Micro Rental Cars, which made detailed regulations on the operation, management, and supervision of these rental cars so that the legitimate interests of the lessee, the operator and other related groups are protected. On February 10, 2020, the Industry Department of the National Development and Reform Commission issued the Development Strategy for Smart Car Innovation (NDRC, 2020, No. 202). For the development of the smart car industry, it is necessary to take “opening up new models, cultivating new business formats, improving the basic capabilities as well as the level of the industrial chain, and meeting the people’s growing needs for a better life” as the guiding ideology and the work goal for the development of smart cars.

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d. Key economic development regions For key economic development regions, relevant policies have also shown the significant position of the regional population layout and the people’s needs in terms of developing transport. On April 2, 2020, the Basic Department of the National Development and Reform Commission issued the Plan for Higher Quality Integrated Development of Transport in the Yangtze River Delta (Basic Department of the NDRC, 2020, No. 529), pointing out that the current development of the comprehensive transport system in the Yangtze River Delta has not overcome the problem that “adaptability to land and space, population distribution, and industrial layout needs to be strengthened.” It is necessary to take this as the focus of work to promote higher quality integrated development and to realise the coordinated development of “transport, urban morphology, and population layout.” Regional population distribution plays an important role in the optimisation and promotion of the future transport system. Paying attention to population distribution is the only way to make up for the shortcomings of current transport services. e. The needs and demands of certain groups of people On the needs and demand of certain groups of people, policies and documents have provided guidance for the development principles and goals of transport services. For the safety of travel, the Three-Year Plan for Safer Transport (2018–2020) (General Office of the Ministry of Transport, 2018, No. 86) emphasises some guiding principles for the development of a safer travel environment. It is requires the authorities to “adhere to putting people first, firmly establish the concept of safe development, and deepen the construction of a safer environment” and to “promote reform and innovation and improve the safety system” to protect the vital interests of people’s travel and to create a reliable and safe travel environment. In terms of travel experience, the Green Travel Action Plan (2019–2022) (Ministry of Transport, 2019, No. 70) emphasises that the establishment of a comprehensive transport service network should “improve the experience of public travel, provide the public with accurate and reliable information of buses such as real-time location and estimated arrival time” and “carry out humanised and refined road space and traffic design” to improve the quality of residents’ travel. In terms of travel for tourism, the Implementation Opinions on Improving the Traveling Environment on Holidays and Promoting Tourism Consumption (NDRC, 2019, No. 1822) emphasises the necessity to “optimise the travel environment on holidays” and “further [to] alleviate traffic congestion caused by concentrated travel on holidays,” and it regards “meeting the people’s growing needs for a better life” as the main principle. f. Particular groups of travellers For some travel groups, such as rural migrant workers, a group that has made huge contributions to the process of urbanisation in China, existing policies have incorporated their travel characteristics and travel demand. On March 31, 2015, the Ministry of Transport issued the Implementation Opinions on Better Serving Migrant Workers in the Transport Industry (Highway Bureau of the Ministry of Transport, 2015, No.

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39), encouraging relevant departments and units to “actively support local migrant workers to find employment nearby and increase the actual income of rural migrant workers” in areas such as infrastructure construction, maintenance and transport services. During the global spread of COVID-19 in 2020, relevant policies on transport services also focused on protecting rural migrant workers’ travel rights. On February 19, the Ministry of Human Resources and Social Security of the People’s Republic of China issued the Notice on Providing Point-to-Point Assurance for Rural Migrant Workers Returning to Work (MHRSS, 2020, No. 4). It pointed out that on the basis of “fully understanding people’s travel demand,” all regions should “provide predeparture services and organise return-to-work services,” and all transport departments must “formulate a special chartered transport plan based on the needs of rural migrant workers to return to work,” and “provide ‘point-to-point’ assurance services for rural migrant workers with a large scale and similar destinations.” On March 7, the Ministry of Human Resources and Social Security further issued the Notice on ‘Point-to-Point’ Healthcare Services for Rural Migrant Workers Returning to Work (MHRSS, 2020, No. 15), emphasising “doing a good job in healthcare during traveling for rural migrant workers” and requiring “point-to-point” healthcare services for rural migrant workers to ensure that migrant workers can safely and efficiently complete their return trips. To sum up, in the previous policies and documents for the development of China’s transport service system, the concept of being people-oriented has been embodied in all transport departments, all types of industries, all kinds of travel demand and all groups of travellers. In the future, the high-quality development of the transport service system in China should still adhere to the guidance of people’s needs, adapt to the characteristics of people’s travel behaviour, promote the improvement of the quality and efficiency of transport services in every region and adapt to the diversified needs of people and differentiated travel characteristics. 3) China’s practice of people-oriented transport service a. Improving transport services in rural areas On March 4, 2014, President Xi put forward the development policy of Construction, Management, Maintenance and Operation of Roads in Rural Areas (or Four Aspects of Better Rural Roads), requiring that the construction of roads in rural areas should be adapted to local conditions, people-oriented, and compatible with the optimisation of the layout of villages and towns, the development level of rural economy and the travel habits of local farmers. It is designed to improve the construction, management, maintenance and operation of rural roads gradually to eliminate the bottleneck of transport restricting rural development, and to provide a better guarantee for the majority of farmers to escape poverty and become prosperous. The proposed policy provides guidance for the improvement of the quality and efficiency of the transport system in rural areas of China. It has been an important hallmark of the social changes in rural China in the new era. On May 26, 2015, the Ministry of Transport issued the Opinions on Promoting the Construction, Management, Maintenance and Operation of Roads in Rural Areas

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(Highway Bureau of the Ministry of Transport, 2015, No. 73), providing detailed suggestions for the optimisation of rural roads based on local conditions. It is necessary to promote the construction of roads in rural areas by “transforming centralised tackling difficulties from focusing on connectivity to improving quality and safety, and from focusing on construction to coordinated development of construction, management, maintenance and operation, from adapting to development to leading development.” These opinions emphasised the need to “significantly optimise the structure of rural road network and improve the quality.” At the end of 2015, the Ministry of Transport issued the Administrative Measures for the Maintenance of Road in Rural Areas to make clear arrangements and institutional guarantees for the optimisation and improvement of rural road maintenance to achieve the goals of standardisation, specialisation and mechanisation. Many cities and local governments have actively promulgated corresponding policies to improve the maintenance of roads in rural areas. Efforts have been made to improve quality and efficiency, to optimise and beautify the travel environment through formulating a construction plan based on local conditions for the maintenance of roads in rural areas. Taking Nanjing as an example, there have been various measures to promote the improvement of roads in rural areas. At a press conference held by the Nanjing Municipal Party Committee and Government on November 7, 2019, the progress of promoting the Construction, Management, Maintenance and Operation of Roads in Rural Area: One District, One Brand in Nanjing was reported. It revealed that the upgrading of roads and bridges and other infrastructure in rural areas of Nanjing have had remarkable results. By the end of October 2019, Nanjing had completed the upgrading of 371 kms of rural roads, 41 bridges, and 82 kms of connection roads in economically weak and underdeveloped villages, and the construction of 320 kms of safety assurance projects. The management and maintenance level of transport infrastructure and the quality of transport services have been significantly improved in Nanjing. The Promoting the Construction, Management, Maintenance and Operation of Roads in Rural Areas policy will help to improve the regional coordination of transport between urban and rural areas, improve the living environment and travel experience of rural residents, promote the development of rural industries and increase residents’ income levels. In addition, the districts in Nanjing have innovated the development model of rural road + (the integration of rural roads and other local brands). Combined with the advantages of ecological resources, urban industries and local culture, many roads have been improved and upgraded with those local brands. For example, the New Jiangbei & New First-line brand in Jiangbei New District, Nanjing has made efforts to build a road with diversified culture and convenient services. The brand of the Danfeng Rural Road in Qixia District is positioned for ecological agriculture and tourism. The Ningjing Zhimei road brand in Jiangning District has created a special scenic line of rural roads. The Returning Pu for the Origin and Truth brand in Pukou District promotes the integrated development of rural roads and ecological culture. The Jasmine Rural Road brand in Liuhe District has actively promoted the integrated development of rural roads and cultural and sports industries. The Healthy Green and Road brand in Lishui District gives full play to the role of rural roads in connecting

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arterial highways. The vigorous construction of branded rural roads in those districts has effectively supported the development strategy of Rural Revitalisation, promoted the development of rural areas and narrowed the gap between urban and rural areas. By 2020, Nanjing planned to complete the upgrading of 456 kms of rural roads, the rebuild of 19 new bridges in rural areas, the construction of 141 kms of connection roads in economically weak and underdeveloped villages, and the construction of 542 kms of safety assurance projects. Since the policy of Promoting the Construction, Management, Maintenance and Operation of Roads in Rural Areas was proposed and actively implemented, significant achievements have been made in the construction of transport infrastructure in the vast rural areas in China (see Fig. 10.1 for an example). The convenience of travel for rural residents has been greatly improved, the transport relationships between villages, towns and urban areas have been strengthened, and the gap between urban and rural facilities has been further narrowed. According to the differentiated travel demand and travel characteristics of urban and rural residents, the future transport service system, particularly in rural areas, should give more consideration to the actual travel demand of rural residents to ensure the further optimisation of their travel environment. At the same time, the waste of transport facilities in rural areas with low population densities should be avoided. While optimising the infrastructure construction in rural areas, customised services should be provided for rural residents’ regular and special travel activities to promote centralised and efficient usage of the transport resources in rural areas. b. Promoting energy conservation and emission reduction in transport On June 28, 2020, the Shanghai Municipal Commission of Transport issued the Notice on the Solicitation of New Technologies, New Products, New Materials, and Operation Laws for Energy Conservation and Emission Reduction in the Transport Field, based on the relevant requirements of the Plan for the 13th Five-Year Development on Shanghai’s Green Transport. Projects for transport energy conservation and emission reduction were openly collected from all departments and units in Shanghai. The main requirements for the solicited projects were: (a) the technology Fig. 10.1 Zuodi road named as 2022 Beijing’s most beautiful Rural Road in Shunyi, Beijing. Data source Beijing Daily, https://news. bjd.com.cn/2022/11/10/102 17144.shtml, visited on Nov. 10th, 2022

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used is mature, safe and reliable; (b) the effect of energy saving and emission reduction after implementation can be remarkable; (c) it conforms to the laws of the market economy, and the payback period of investment for energy saving and emission reduction is short and economical; and (d) There have been practical applications, and it has the value of promotion and demonstration. The launch of the solicitation will help to accelerate the practical application of green and low-energy vehicles and related technologies in urban transport services, and it will help to form a good atmosphere for a sustainable transport network and sustainable human settlements. On July 1, 2020, the Shanghai Municipal Commission of Transport issued the Notice on Declaring the 2020 Special Support Funds for Shanghai’s Transport Energy Conservation and Emission Reduction (SMTC, 2020, No. 469). This notice provided a reliable guarantee for the standardised, efficient and orderly application of the 2020 special support funds for Shanghai’s transport energy conservation and emission reduction. It clearly states that the projects for application must “have an obvious effect on energy-saving and emission-reduction effects, and have no negative impact on public interests.” Every district has provided strong support for the promotion and application of local low-energy transport technologies and projects through policy guidance and financial support to optimise the residents’ travel environment and to build a sustainable transport service system that is people-oriented. c. Developing a sharing economy in the transport system On September 15, 2017, the Beijing Municipal Commission of Transport and other government departments formulated the Guiding Opinions on Encouraging and Regulating the Development of Shared Bicycles in Beijing (Trial). Development principles, responsibility sharing, service standards, management measures and guarantee mechanisms of Beijing’s shared bicycles have been made clearer. These opinions pointed out that a shared bicycle “is part of the green travel and slow-moving transport system in the city, and is a transport service method that makes shortdistance travel much more convenient.” Similarly, it is required that the scale of shared bicycles must match the short-distance travel demands of citizens. Shared transport serves the citizens and should be oriented by people’s demand. These opinions clearly stipulate the development of shared bicycles from operation to management, from construction requirements to guarantee mechanisms, and helps to promote a more standardised and orderly application of shared bicycles in Beijing. On August 13, 2019, the Shanghai Municipal Transport Commission issued the Measures on the Evaluation of Online Rental Bicycle Services in Shanghai (SMTC, 2019, No. 510). These measures delineated the evaluation standards for online rental bicycles, including users’ satisfaction, vehicle capacity and quality, etc., and determined a total of 25 indicators including vehicle parking order, vehicle capacity, car capacity, corporate service quality, vehicle usage efficiency, technology and management innovation etc. Through various evaluation methods such as on-site observation, user satisfaction surveys, centralised scoring by management departments and daily inspections, statistical evaluation of information platform data, etc., the evaluation of various online rental bicycles could be more realistic. The assessment result serves as an important basis for the dynamic adjustment of the number of vehicles launched by

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operating companies. The release of these measures enabled Shanghai to improve the operation and management mechanism for online rental bicycles effectively, promote the further development of shared transport in the city, and ensure the operating efficiency of the market. In addition, these measures could make contributions to avoiding an imbalance of supply and demand caused by too little investment, overuse of space or waste of resources. It also makes contributions to maximising the benefits of shared transport represented by online rental bicycles by alleviating the traffic pressure in big cities and adapting to residents’ travel demand. d. Improving the quantity and quality of bicycle lanes On May 25, 2019, the Beijing Municipal Public Security Bureau and the Beijing Municipal Commission of Transport issued the Notice on the Traffic Management of the Bicycle Lane from Huilongguan to Shangdi. This notice provided clear regulations for the traffic management of this first bicycle lane in Beijing. It pointed out that “the bicycle lane is an urban road that only serves non-assisted bicycles, so pedestrians, electric bicycles and other vehicles are prohibited from entering.… No unit or individual can set up, move, occupy, or damage the bicycle lane without permission.” It also clearly stipulated the travel behaviour of cyclists on the bicycle lane. The various measures proposed by Beijing on the design, construction, usage and management mechanism of bicycle lanes are effective attempts to promote the construction of slow traffic systems and create a more humane and green travel environment (Fig. 10.2). Since the opening of Beijing’s first bicycle lane (from Huilongguan to Shangdi) on May 31, 2019, it has provided a safe, green, convenient, comfortable and efficient travel choice for the great commuting demand between Huilongguan residential area and Shangdi Software Park (which has provided a large number of jobs). It will help to

Fig. 10.2 The first bicycle lane in Beijing. Data source https://www.sohu.com/a/317706509_ 114988, visited on November 15, 2020

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drive the optimisation of the slow traffic system in the surrounding areas and the entire city, improve the travel satisfaction of travellers, and form a good trend of green and sustainable commuting. In 2020, to meet the last mile travel demand of commuters to and from the Zhongguancun Software Park (which has also provided large number of jobs), Beijing started a westward extension project for the bicycle lane. According to a notice issued by the Beijing Municipal Commission of Transport, the length of the road for the westward extension project is 3.8 kms, and the length of the bicycle road after the westward extension will reach 10.3 kms. The upgraded bicycle lane could allow residents living in Huilongguan and Tiantongyuan residential areas to ride bicycles to the Software Park where they are employed. So, the actual travel demand of more commuters could be effectively met. In addition, the project adopted oscillating markings and raised road signs to ensure the separation of motorised lanes from non-motorised lanes, and it added a second pedestrian crossing island to ensure the safety and continuity of the travel of cyclists and pedestrians. The upgraded bicycle lane has fully applied the transport concept of “slow traffic first, public transport first and green vehicles first.” Through road configuration and facility design, the “interaction, smoothness and connectivity” of the slow traffic system could be better guaranteed. It is an effective effort to encourage citizens to use walking + bicycle riding mode of travel more often. e. Building a civilised and harmonious environment for traveling On May 15, 2019, the Beijing Municipal Commission of Transport issued the Regulations for Passengers of Beijing Rail Transport, focusing on the travel behaviour of passengers near the entrances, passages, halls, platforms and subways of Beijing rail transport. The policy has helped to make clear regulations on behaviour, guide civilised travel, and create a safe, convenient and harmonious trip environment. These regulations provide requirements for ticket purchasing, waiting, and getting on/off the subway. Detailed rules on prohibited behaviour are listed in the document Passengers are Prohibited from the Following Behaviour. For encouraged behaviour, there are nine detailed rules in the title Passengers Should Consciously Maintain the Environment and Order in the Stations and Carriages. Most of the common uncivilised travel behaviour in rail transport is clearly prohibited in these regulations, such as “making loud noises or playing musical instruments, playing music without headphones,” “one person occupying several seats at the same time, stepping on the seats,” “eating in the train compartment,” “promoting products,” and other behaviour. The rail transport operator will have the right to stop, ask to leave or refuse to provide any services to passengers who violate the provisions and engage in uncivilised travel behaviour as a disciplinary measure. The release of these regulations has played an important role in regulating the trip environment of rail transport in Beijing and improving the safety, comfort, and satisfaction of travellers. However, the disadvantage is that the restrictions on prohibited behaviour and non-promoting behaviour are still only enforced by the operator or its managers for daily travel, especially in the morning and evening peak hours, but some prohibited behaviours are difficult to identify, and often they are not effectively punished.

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On August 5, 2020, to give full play to the value of citizens’ supervision and public forces to promote the formation of a civilised and orderly transport environment, the Beijing Municipal Public Security Bureau followed the Procedures for Handling Illegal Behaviour on Road Traffic Safety and the Regulations on the Promotion of Civilised Behaviour in Beijing by building an online platform named Suishou Pai (which can take and upload pictures at any time) for Beijing’s Traffic Police. With the help of this platform, citizens can report illegal behaviour through the 3-step operation of real-name authentication, logging into the platform, and taking pictures. The operator will review and check, investigate and verify those reports one by one, and apply punishments for the illegal behaviour in a reasonable time. Behaviour that can be reported on this online platform includes motor vehicles occupying emergency lanes, bus lanes and non-motorised lanes, and bad parking. In addition, citizens can report various failures of the transport system while traveling to the operating department through the platform, such as “traffic signal lights being damaged, not bright enough, obstructed, or tilted,” so that the operators can take timely measures to reduce potential safety risks. The use of the Suishou Pai online platform is a powerful measure for the Beijing Traffic Management Department to collect people’s opinions and suggestions to meet people’s travel demands, innovate governance models, and improve the travel environment. It promotes traffic governance through everyone’s participation and everyone’s supervision. In the future, with the maturity and improvement of this public participation and supervision platform, this type of model can be applied to a wider range of transport scenarios, such as rail transport, public transport, etc., to build a full-coverage platform for everyone’s participation and everyone’s supervision. It could be further promoted in other large, medium and small cities across the country, and it could be adapted to the characteristics of travel demand according to local conditions to build a people-oriented, harmonious and beautiful travel environment. f. Providing safety guarantees for travellers Travel safety is a vital basis for residents’ daily travel activities. The construction of a people-oriented and high-quality transport service system is closely related to the guarantee of travel safety. Safe and convenient transport services are specific requirements for building a harmonious society, enhancing residents’ happiness, and promoting social progress. In recent years, the construction of China’s transport system has made extensive progress in improving travel safety, safeguarding the vital interests of travellers, and reducing traffic risks. Take Shanghai as an example. In the early 21st century, the process of motorisation in Shanghai had just started, and some travellers still lacked safety awareness and knowledge of the law. As a result, irregular travel behaviour, mixed traffic of people and vehicles, and mixed traffic of motorised and non-motorised vehicles were quite common problems. Residents were faced with many safety risks when traveling. The death rate per 10,000 vehicles was much higher than the average level of developed cities in other countries. Road traffic accidents had become the primary cause of abnormal deaths. Strengthening traffic safety seemed to be the primary task of

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improving transport services at that time. In 2002, the Shanghai Municipal Government promulgated the White Paper on Shanghai’s Urban Traffic for the first time, emphasising the importance of safety management of urban traffic construction, and regulating the travel behaviour of people and vehicles on the road through special design in the transport system. Many efforts have been made to reduce traffic accidents and to improve travel safety. Since then, Shanghai has continuously improved its policy based on practical experience to provide effective guarantees of residents’ travel safety. In May 2004, the Law of the People’s Republic of China on Road Traffic Safety was officially promulgated and implemented, and it was revised in 2011. Under the unified deployment of the government, Shanghai has actively implemented and strengthened control over the behaviour of drivers to avoid improper driving behaviour (such as drunk driving, fatigued driving, unlicensed driving etc.) that threatens the safety of passengers, pedestrians and drivers themselves. Since 2005, Shanghai has delegated the goal of controlling the number of road traffic accident deaths to every district (county) and incorporated it into the evaluation standards for those government departments. In 2016, the new version of the Regulations of Road Traffic Management in Shanghai was issued. The new version clarified the development strategy of adapting to the travel characteristics of big cities, adhering to the concept of green transport, adhering to management based on law, and adhering to the people-oriented concept. Through strict and scientific management, convenient services, people’s conscious law-abiding behaviour and strict and standardised law enforcement, various efforts have been made to promote the safer development of the transport management system in big cities. In addition, Shanghai has attached great importance to the implementation of traffic civilisation education to strengthen the guarantee of travel safety. From 2010 to 2012, Shanghai implemented the Three-Year Acting Plan for More Civilised Transport, which aimed to enhance citizens’ awareness of the laws and regulations as well as their awareness of safety and traffic civilisation, reduce the incidence of traffic accidents, standardise the enforcement of traffic laws and regulations, improve traffic order, and improve the long-term mechanism of civilised transport. Under this plan, all districts and counties in Shanghai organised citizens and volunteers to participate in the activity named ore Care for Life, More Civilised Travel.5 The activity focused on promoting publicity and education on civilised roads, civilised driving, civilised parking, civilised queuing, and other travel behaviour. In 2016, to advocate for the concept of civilised transport in a modern society and to establish a new atmosphere of civilised transport in Shanghai, the Shanghai Civilisation Office and other relevant departments held a joint meeting on the construction of Shanghai’s transport civilisation. During the meeting, the Three-Year Acting Plan for Safer Transport in Shanghai (2016–2018) was discussed and finally issued. The new round of the threeyear acting plan for civilised transport was committed to expanding the benefits of civilised transport and promoting the development of Shanghai’s green and smart 5

Data source: China News, https://www.chinanews.com/expo/news/2010/02-26/2140590.shtml, visited on October 30, 2020.

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transport services on the basis of existing experiences. Under the guidance of the action plan, Shanghai has actively developed 160 civilised intersections, 16 civilised routes, and 16 civilised commercial blocks as a demonstration. Promotional activities for civilised transport have been widely carried out around the city’s subway stations, bus hubs, bus departure stations, non-motorised vehicle parking points and other places, to help to form a new atmosphere of civilised transport in Shanghai.

10.1.2 Refined and Customised Transport 1) An overall introduction to the relevant policies With the continuous promotion of the development concepts of being people-oriented and serving for the people in the transport system, the differences in travel characteristics caused by the preferences of different travel groups make it difficult for traditional universal transport services to meet the increasing needs and diverse living habits of people in the new era. The traffic congestion problems of big cities have obstructed the normal operation of the city. The complexity and mix of different travel purposes and travel habits in big cities means that the homogenised traffic services need to be refined and customised. In recent years, refined and customised transport services have gradually received more attention in China’s transport development planning and related policies. In 2016, the Outline for the 13th Five-Year Development of Urban Public Transport (Ministry of Transport, 2016, No. 126) pointed out that people’s travel demands tend to be diversified and individualised, so public transport services should carry out targeted and precise innovations in response to the changing trends of travel demand. Transport services need to achieve “balanced development of quantity and quality and comprehensive development.” In the same year, the “Guiding Opinions on Steadily Promoting the Integration of Urban and Rural Transport and Improving the Quality of Public Service (Ministry of Transport, 2016, No. 184) further pointed out that transport services need to be combined with the actual travel demand of different regions, especially for rural areas. It suggested providing advance booking, along with customised and personalised transport services for passengers. The release of these opinions has implemented the concepts of refined and customised transport on the spatial level, considering the different travel demands caused by urban and rural differences. It has shown that refined and customised transport services are necessary and significant in adapting to people’s actual travel demands. By 2020, the refined and customised orientation of transport services had been discussed and emphasised in a number of development plans and policy documents in China. More specific practical measures had also been put forward. The Plan for Higher Quality Integrated Development of Transport in the Yangtze River Delta (Basic Department of the NDRC, 2020, No. 529) presented plans to improve the quality and efficiency of transport services in the Yangtze River Delta, and to encourage a layout of the transport network that gives more attention to the changing

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travel demand, to “actively adapt to the needs of personalised, diversified travel and the development of new business models.” On April 10, 2020, the Opinions on Promoting the Connection of Rail Transport between Hubs and Airports (Basic Department of the NDRC, 2017, No. 576) pointed out that rail transport can improve the ability to adapt to changes in passenger flow, with the help of dynamic traffic resource allocation to provide refined services. On June 8, 2020, the General Office of the Ministry of Transport issued the Notice on Making Efforts in Transport to Promote Consumption Expansion and Quality Improvement (General Office of the Ministry of Transport, 2020, No. 26), which required future transport to “meet the special needs of the disabled and older groups.” It provides suggestions for the refined and customised development of transport services from the perspective of differences in travel groups. To sum up, refined and customised transport has received attention in China’s transport development planning and related policies. Suggestions and measures have been made based on the spatial perspective (such as considering the urban–rural gap), the temporal perspective (such as considering the changes of traffic flow over time), and the people-oriented perspective (such as caring for special groups). However, the specific measures for refined and customised transport have not yet been built into a mature system, and the practice of corresponding measures is still in its infancy. There seems to be a lack of an adequate policy guarantee for their implementation. In the future, the refined and customised development of transport services should rely on the identification of the differences in travel characteristics among different travellers to provide specific services according to the actual travel demand, and it should ensure the accuracy and high efficiency of transport services, making the development of transport better benefit all the people. 2) A detailed overview of the relevant policies In terms of the development concepts of refined and customised transport services, China has issued a large number of policy documents in recent years. Most of the policies and measures are based on the personalised and diversified characteristics of people’s travel needs, which make contributions to developing a high-quality transport system (Table 10.2). a. Public transport system On July 25, 2016, the Ministry of Transport issued the Outline for the 13th Five-Year Development of Urban Public Transport (Ministry of Transport, 2016, No. 126), which pointed out that in the new era of China’s development, significant changes have taken place in terms of people’s travel structure and mode choice in urban areas. Travel demand will become more diversified and individualised. Thus, the public transport services in urban areas need to be improved in both quantity and quality to achieve comprehensive development. It is necessary for urban public transport to “adapt to the basic travel demand of the people.” This shows that the concepts of refined and customised development of public transport have been focused and applied.

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Table 10.2 The refined and customised concept in the policies and documents about China’s transport development in recent years Issue date

Document name

Relevant policies

July 25, 2016

Outline for the 13th Five-Year Development of Urban Public Transport (Ministry of Transport, 2016, No. 126)

“Significant changes have taken place in terms of people’s travel structure and mode choices in urban areas. Travel demand will become more diversified and individualised. Thus, the public transport services in urban areas need to be improved in both quantity and quality to achieve comprehensive development.”

November 9, 2016

Guiding Opinions on Steadily Promoting the Coordination of Urban and Rural Transport and Improving Public Service Quality (Ministry of Transport, 2016, No. 184)

For towns and rural areas with good road conditions, transport services could be more standardised and of higher quality by “encouraging the extension of urban public transport network or the transformation of transport lines.” For remote areas with relatively lower and more dispersed travel demand, it is more suitable to encourage personalised services

January 12, 2018

Implementation Opinions on Further Improving Travel Services for Older People and the Disabled (Ministry of Transport, 2018, No. 8)

It suggests promoting the “provision of special-person escorts throughout the journey, appointment of customised services, and permission for relatives to pick up and drop off at stations, etc.” Providing vehicles for service, wheelchairs and other convenient equipment to ensure the safety and convenience of passengers with disabilities is also of great significance

December 31, 2017 Guiding Opinions on Accelerating the Development of Integrated Transport for Travellers (Ministry of Transport, 2017, No. 215)

Services should be provided with the goal of “better meeting the individualised and diversified travel demand of travellers” and taking “enhancing the people’s sense of gain, happiness and security” as the guiding ideology

May 31, 2019

Green Travel Action Plan “Encourage the launch of (2019–2022) (Ministry of Transport, municipal types of public transport 2019, No. 70) tickets such as weekly, daily, and secondary cards suitable for travellers.” “Encourage transport companies actively to develop diversified bus services such as customised buses, night buses, and community buses.” (continued)

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Table 10.2 (continued) Issue date

Document name

Relevant policies

April 2, 2020

Plan for Higher Quality Integrated Development of Transport in the Yangtze River Delta (Basic Department of the NDRC, 2020, No. 529)

“Actively adapt to individual, diverse travel characteristics and the development needs of new models, optimise the layout of the transport network, comprehensively improve the quality of transport operation and provide safe and reliable transport services with higher efficiency.”

April 10, 2020

Opinions on Promoting the Connection of Rail Transport between Hubs and Airports (Basic Department of the NDRC, 2017, No. 576)

Rail transport should be arranged “scientifically based on changes in aviation passenger flow,” and there could be “fast lines” and “slow lines” operated alternately to meet the travel demands of different passengers

June 8, 2020

Notice on Making Efforts in Transport to Promote Consumption Expansion and Quality Improvement (General Office of the Ministry of Transport, 2020, No. 26)

The improvement of transport services needs to “continue to meet the travel demand of the disabled and older people as well as other special groups for better promotion of barrier-free environment for traveling

On May 31, 2019, the Green Travel Action Plan (2019–2022) (Ministry of Transport, 2019, No. 70) formulated by the Ministry of Transport and twelve other departments further implemented customised public transport services. More specific guidance was made to “encourage transport companies actively to develop diversified bus services such as customised buses, night buses, and community buses.” Driven by this action plan, some cities have launched diversified bus services to meet the various travel demands of residents. Take Shanghai as an example. In September 2019, the first domestic AI No. 9 customised bus from Songjiang to Zhangjiang was put into operation. Unlike conventional buses, this AI customised bus is available through the Alipay system for passengers to make reservations and book tickets, ensuring one seat for each person. This route can also cover the first kilometre before boarding and the last kilometre after getting off when passengers take traditional buses. The AI customised bus can achieve point-to-point service from the departure point to the destination, and it can meet individuals’ travel demand. It is an effective attempt to promote the refined development of public transport in China. b. Integration of different modes On the integration of different travel modes during a complete trip, there have also been some effective measures. On February 1, 2018, the Ministry of Transport issued the Guiding Opinions on Accelerating the Development of Integrated Transport for

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Travellers (Ministry of Transport, 2017, No. 215), which pointed out that it is necessary to “accelerate the healthy, orderly and rapid development of integrated transport for travellers.” The services should be provided with the goal of “better meeting the individualised and diversified travel demand of travellers” and take “enhancing the people’s sense of gain, happiness and security” as the guiding ideology. On April 10, 2020, the Basic Department of the National Development and Reform Commission issued the Opinions on Promoting the Connection of Rail Transport between Hubs and Airports (Basic Department of the NDRC, 2017, No. 576). It proposed that rail transport should be arranged “scientifically based on changes in aviation passenger flow,” and that there could be fast lines and slow lines operating alternately to meet the travel demands of different passengers. Through these measures, rail transport should serve travellers more efficiently and provide more convenient and accurate services. c. Regional gaps in travel demand Since the characteristics of travel demand might differ among different areas and regions, there have been policies promoting the customised development of transport services. On November 9, 2016, the Ministry of Transport issued the Guiding Opinions on Steadily Promoting the Coordination of Urban and Rural Transport and Improving Public Service Quality (Ministry of Transport, 2016, No. 184). It proposed that for towns and rural areas with good road conditions, transport services could be more standardised and of higher quality by “Encouraging the extension of the urban public transport network or the transformation of transport lines.” For remote areas with relatively lower and more dispersed travel demand, it is more suitable to encourage personalised services. The guiding opinions showed the application of the development concept of providing refined and customised transport for adapting to the travel habits in different regions. On April 2, 2020, the Basic Department of the National Development and Reform Commission issued the Plan for Higher Quality Integrated Development of Transport in the Yangtze River Delta ( Basic Department of the NDRC, 2020, No. 529), emphasising the necessity for higher quality integrated development to “adapt actively to individual, diverse travel characteristics and the development needs of new models, optimise the layout of the transport network, improve the quality of transport operation comprehensively, and provide, safe and reliable transport services with higher efficiency.” The significance of adapting to people’s various and special travel demands has been clearly emphasised for optimising transport services in key economic development regions. d. Special care for certain groups For groups with special travel demands, the concept of providing refined and customised transport services has been also applied through paying attention to vulnerable groups such as older people and the disabled. On January 12, 2018, the Ministry of Transport issued the Implementation Opinions on Further Improving Travel Services for Older People and the Disabled (Ministry of Transport, 2018, No. 8), stating that the specific content of improving the quality of travel services

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should include “provision of special-person escort throughout the journey, appointment of customised services, and permission for relatives to pick up and drop off at stations, etc.” It also encouraged the provision of service vehicles, wheelchairs and other convenient equipment to ensure the safety and convenience of passengers with disabilities. On June 8, 2020, the Notice on Making Efforts in Transport to Promote Consumption Expansion and Quality Improvement (General Office of the Ministry of Transport, 2020, No. 26) also pointed out that the improvement of transport services needs to “continue to meet the travel demand of the disabled and older people as well as other special groups for better promotion of barrier-free environment for traveling.” In summary, among the present policies for the better development of China’s transport service system, promoting refined and customised development is an important strategy to deal with the differentiation, individualisation and diversification of people’s travel demands. For different transport modes, different regions and different groups of travellers, refined transport can help to meet various needs accurately and efficiently, and it can improve people’s sense of happiness in travel. To date, China’s refined transport measures have achieved some practical results, but to improve the transport service system for higher quality, it is necessary to continue to summarise previous experience, accurately to identify different travel demand and to combine information and intelligent technology to perfect the development of refined and customised transport. 3) China’s practice of developing a refined and customised transport service system to satisfy the differentiated travel demands of people a. Promoting car sharing With the gradual promotion of bike sharing and other shared electric vehicles in various cities of China in recent years, sharing economics seems to be becoming more popular in the field of transport. On August 8, 2017, the Ministry of Transport issued the Guiding Opinions on Promoting the Healthy Development of Small and Micro Rental Cars for the car rental industry (Ministry of Transport, 2017, No. 110), pointing out that small and micro cars are emerging forms that can satisfy the individual travel demands of the people, such as travel for business and official activities, shopping and leisure. The car sharing industry is an innovation in its serving model, the technology, and its operation and management model, which provides a new choice for travellers and may help to reduce the willingness of individuals to buy cars. If the positive role of small and micro rental cars is given full play, the diverse travel demands of the people can be better satisfied. The promulgation of these guiding opinions gave the emerging form of car sharing policy recognition and support for the first time in China. It clarified the industry concept and operation requirements, providing policy guidance for the promotion and improvement of car-sharing services. Under the guidance of these guiding opinions, the Shanghai Municipal Transport Commission issued the Management Implementation Rules for Shanghai’s Small and Micro Rental Cars, which made detailed regulations on the operation, management, and supervision of small and micro rental

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Fig. 10.3 The shared car operated in Shanghai. Data source http://www.xinhua net.com/photo/2017-02/16/ c_1120480344.htm, visited on July 10, 2022

cars. It emphasised that shared cars (or rental cars) must be equipped with face recognition devices, as well as on-board satellite positioning and emergency alarm devices that meet specified standards. These measures have shown Shanghai’s consideration of safety and intelligence in the development of shared cars (see Fig. 10.3 for an example). According to the Research Report on Car Sharing Industry in 20196 released by iResearch, there were more than 1,600 registered car sharing companies in China in February 2019, and the number of shared cars in operation was approximately 110,000–130,000. The overall market size was 2.85 billion CNY. It predicted that with the expansion of car sharing infrastructure and the gradual maturity of local policy support for this industry, the number of users in the car sharing market would continue to increase. The platform will be more complete in terms of vehicle launch, price management, vehicle utilisation and turnover rate control, which may lead to a continuous growth in the market size. According to iResearch Consulting, the time and money cost of taking a shared car is between the costs of public transport modes and private cars, and it may be much more convenient, flexible, comfortable and economical without much extra cost. Compared with traditional private cars, car sharing has a significant cost advantage, and its promotion will help to improve the utilisation of resources and reduce residents’ travel burdens. On August 6, 2020, the Ministry of Transport issued a notice on the Administrative Measures for the Management of Small and Micro Rental Cars (Draft for Solicitation of Comments) to solicit feedback from the public. This shows that the government has made efforts to promote the application of shared cars, and it is calling for the public’s participation in the process of the promotion. The document pointed out that the government “encourages the implementation of large-scale, networked, and brand-based operations in the promotion of small and micro rental cars” and “supports the use of new energy vehicles to promote small and micro rental cars.” It would be possible to build a service platform to provide better car-sharing services through the mobile Internet, satellite positioning and other information technologies. 6

Data source: http://report.iresearch.cn/report_pdf.aspx?id=3347, visited on September 30, 2020.

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With minutes or hours as the unit of measurement, the platform could also help to complete the process of reservations, adoption and return, and fee settlement while using shared cars. The measures and suggestions mentioned in the document make contributions to regulating the development of car sharing, maintaining the market order, and promoting the healthy environment of emerging forms of transport. As an emerging form of transport that meets the individualised travel demands of the public, car sharing plays an important role in building a modern transport service system that is well-planned, safe and efficient, green and environmentally friendly, and resource-sharing. At present, the service market for car sharing in China is still immature, and only residents of a few large cities have access to such new forms of services. To meet the travel demand of urban residents to achieve greater benefits, car sharing should also allow for the differentiated travel demands of different groups of travellers. On the basis of sharing concept, it is important to encourage the refined utilisation of resources. For families with young children, cars equipped with child safety seats and intelligent hazard identification devices could be provided. For families with older members, automatic driving cars could be provided. If the refined development of car sharing is promoted, people can benefit more from travel services that are more humane and more adapted to people’s actual demand. b. Piloting ladies-only carriages Since the travel demands of female travellers may differ from those of male travellers and seems to be more varied, some countries and cities have created ladies-only carriages or ladies-only vehicles as a special travel service for female travellers. Since the beginning of the 21st century, several subway companies in Japan have set up female-only carriages. During peak hours, these carriages can only be used by females to avoid possible conflict between male and female passengers. In Islamic cities such as Tehran, Iran, which are affected by religious customs and considerations for travel safety, public transport systems such as buses and subways often implement a split system for male and female passengers, and male and female travellers are strictly separated. The application of ladies-only carriages in some countries and cities could help to separate male and female passengers, reduce sexual assaults, protect women’s travel rights, and enhance women’s sense of safety. However, the effective implementation of this measure requires the support of urban public transport management systems. In actual implementation, there may be potential social problems such as wastage of ladies-only resources, improper supervision, and gender antagonism. Therefore, the application and promotion of ladies-only carriages in China should be based on the characteristics of the people’s travel demand in different regions and among different groups. Full market research is required before the promotion of ladies-only carriages and vehicles. At the Eleventh Session of the Beijing Municipal Committee of the Chinese People’s Political Consultative Conference, Wang Zhuo submitted a proposal named Suggestion to Establish Ladies-Only Carriages for Metro Trains. He pointed out

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that Beijing’s Metro Lines 1 and 2 are too crowded during rush hours. It is difficult for female travellers to get on the subway, so setting up ladies-only carriages in the middle of the subway during the rush hour is of great significance to ensure the equal provision of travel services. An investigation by the Beijing Metro Operation Company among citizens found that the application of this form of ladies-only carriages still lacks supporting conditions and would be difficult to operate. It may not be able to solve the problem of female travellers’ riding, but instead it may bring more social contradictions. In 2017, Su Zhongyang, a member of the Guangdong Provincial Committee of the Chinese People’s Political Consultative Conference, proposed that the subway should set up female-only carriages (for mothers and babies and pregnant women), male-only carriages (for the old, weak, sick and disabled), and mixed carriages to ensure the safety of travellers especially females. Humanised management measures such as the division of men and women during some periods and non-division during others could provide more protection for female travellers while avoiding unnecessary gender antagonism. In response to this proposal, the Guangzhou Metro stated that the application of female-only carriages may place more pressure on rail transport, intensify congestion, and not be conducive to the evacuation of passengers during peak hours. They agreed that there could be other forms of service to protect the rights and interests of female travellers, but the feasibility of setting up female-only carriages still needs further discussion. Shenzhen Metro also pointed that ladies-only carriages may cause some social problems such as interference with the original operation order; thus, its implementation needs to be carried out gradually after proper pilot operations and in accordance with the actual development of the city. c. Dealing with health emergencies At the beginning of 2020, most countries across the world faced the threat of COVID19. Since public transport has characteristics of high density and large flows of passengers, it has been a focus and a difficulty in strengthening the prevention of COVID-19 and stopping the fast spread of the virus across the country. With the influence of this public health emergency, it was important to adjust the operation of public transport, improve the quality of service, and satisfy residents’ personalised travel demand as much as possible quickly during the critical period. In February 2020, when the COVID-19 pandemic was still spreading globally, the number of new cases of coronavirus was increasing, and the medical service system was entering a state of emergency; thus, the commuting travel of medical staff needed to be guaranteed urgently. On February 8, the Hangzhou (in Zhejiang province) Public Transport Group launched five measures to satisfy the travel demand of medical staff better during the critical period: (a) provide a dedicated bus line for medical staff commuting between major hospitals and subway stations to allow them to go to hospitals to carry out treatment for patients after leaving the subway station; (b) extend the service times of main lines around the hospitals to meet the travel demands of a large number of medical personnel, with the application of big data to understand the distribution characteristics of their travel demands; (c) optimise the route of the dedicated lines around hospitals. During the prevention period for

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COVID-19, there were adjustment to some lines to increase the number of bus stops near hospitals; (d) increase the shift density of dedicated lines around hospitals in a timely manner to meet the needs of workers; (e) Provide commuter-only lines from the residences of medical staff to each hospital to satisfy the point-to-point travel demand of medical staff effectively. The implementation of these various public transport measures has made important contributions to controlling the spread of COVID-19. During the critical period of COVID-19, in addition to the travel demands of medical staff, the travel demands of citizens who had to return to work and resume production also urgently needed to be guaranteed. Measures were also taken in cities of China. According to the Notice on Adjusting the Operation of Urban Subway and Public Transport issued by the Hangzhou Municipal Transport Bureau on February 9, 2020, four measures were introduced to satisfy citizens’ travel demand in accordance with the resumption for production of enterprises7 : (a) actively use big data to adjust the operation strategy of bus routes dynamically to meet the travel demands of residents while avoiding excess capacity; (b) strengthen the connection between bus stations and railway stations and arrange emergency trains to ensure that sudden passenger flows can be evacuated in time; (c) Provide customised bus services for enterprises who need to resume production and satisfy the commuting demands of employees while minimising cross-infection; (d) fully disinfect vehicles of public bicycle companies before putting them into operation to provide safe and convenient services for people. It also provided a shared bicycle service called “small red bicycle”. On February 14, 2020, the Changchun Transport Bureau of Jilin Province issued nine measures to provide various transport services in the city during COVID-19. In terms of passenger cross-city travel demand, Changchun and several other cities jointly formulated plans for the route design of returning workers. The route design was supposed to be based on the travel demand of returning workers. Measures such as online advance ticket sales, real-name ticket purchases, passenger information registration, and keeping vehicle load ratio below 50% were suggested. In terms of public transport, the Changchun Municipal Transport Bureau actively dispatched 24 bus operating companies in the city and made corresponding adjustments to Line 1 and Line 2 of the metro system as well as the nearby bus lines to adapt to people’s actual travel demand. Changchun City also strengthened the service guarantee for the personalised travel demand of taxis, organised relevant enterprises to increase transport capacity and focused on serving areas with higher population densities and more diverse travel demand, such as railway stations, airports and bus stations. Various efforts have been made to provide safe, comfortable, convenient, personalised and customised travel services. On February 27, 2020, the Shanghai Municipal Transport Commission reported that from February 28th, the Shanghai Metro would launch code scanning and registration measures for passengers to help to prevent the spread of COVID-19 in rail 7

Data source: Hangzhou Public Transport Group, http://www.hzbus.com.cn/hzbus/detail/36/1048, visited on November 15, 2020.

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transport. To respond to the travel demands of a large number of local and non-local residents to return to workplaces, and to ensure the safety of passengers in the public transport environment, the Shanghai Municipal Transport Commission cooperated with Shentong Metro Group, Alipay and AutoNavi Maps. They applied information technology and big data and built up an anti-epidemic registration QR code system, which they promoted in Shanghai’s subway. By posting a special QR code in the carriages of the subway, the goal of one car, one code was realised. Passengers were required to scan the special QR code when taking rail transport to register their ride information, so that the government and other relevant departments could perform more detailed investigation work by using the tracking information for all passengers. These measures played an important role in curbing the spread of COVID-19 and stabilising the social order in terms of the transport system. d. Promoting tidal lanes On January 7, 2019, the Public Security Bureau and Beijing Municipal Commission of Transport jointly issued the Notice on the Installation of Tidal Lanes on the Main Road of Lianshi East Road. It pointed out that the innermost lane of the main road of Lianshi East Road (from Nanshawo Bridge to Lugu Avenue) would be set as a tidal lane. It stipulated that during weekdays, the tidal lane would be driven from east to west from 17:00 to 20:00 every day. The rest of the time, motor vehicles were only allowed to drive from west to east. In the event of rain, snow, fog or other severe weather and special circumstances, vehicles should follow the direction indicated by the signal lights and traffic signs on the road to ensure that the tidal lane can accurately respond to specific situations and specific travel demand. As the first tidal lane on an urban express road in Beijing, this lane is a refined measure of the transport system to deal with the separation of workplaces and residences. Through the promotion of a tidal lane, urban roads and other facilities gained the flexibility to change according to the differentiated travel characteristics of the population. To promote the refinement and improvement of the urban transport system, cities should formulate targeted plans based on the actual travel demand of the people, and they should promote diversified measures such as tidal lanes, shared lanes, and other special lanes.

10.1.3 Inclusive and Fair Transport 1) Overall introduction to the relevant policies The increasingly diverse travel demands of different travel groups have put forward new requirements for the diversity and inclusiveness of transport services and for transport equity. A single transport service model cannot meet the fast-paced and high-frequency travel activities of residents in high-density large cities any more. In addition, the new stage of economic development has brought about many changes such as diversification of employment types, diversification of social division of

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labour and diversification of lifestyles. These characteristics make the transport demands of travellers more diversified in timescale, spatial span, and expectations for travel experience. In line with this, the modern transport service system should also be improved and diversified promptly to ensure transport equity in meeting the diverse demand. In the earlier proposed policies in China, the coordinated development of the transport system between urban and rural areas had already been paid attention to promoting transport equality. The Plan for National Highway Network (2013–2030), announced on June 21, 2013, emphasised the necessity to accelerate urbanisation, coordinate the development of urban and rural areas and improve the quality of public transport services. It showed that the differences in residents’ travel demands in urban and rural areas had been taken into account. On November 24, 2015, the Basic Department of the National Development and Reform Commission issued the Plan for Comprehensive Transport Network in Urbanised Areas (Basic Department of the NDRC, 2015, No. 2706), focusing on the diverse characteristics of people’s travel demand in urbanised areas, and it pointed out that “there are many commuting traffic flows on weekdays and many tourists on weekends or other holidays.” The transport service should accommodate the changes of travel purposes by residents in urbanised areas, and it should be more comfortable, punctual and convenient. The successively issued transport development plans and policy documents show that the diversified travel demand of residents relating to urban–rural gaps have been recognised more consistently. Promoting the equality of urban and rural transport services and meeting the diversified demands of urban and rural residents are important contents for further improvement of transport system. On April 10, 2020, the Basic Department of the National Development and Reform Commission issued the Opinions on Promoting the Interconnection of Rail Transport at Hub Airports (Fagaiji, 2020, No. 576). It also pointed out that the intermodal configuration of hub airports and rail transport must “meet the people’s diversified travel needs.” The current policies on adapting to the diversified travel demand and advancing the realisation of traffic fairness mainly focus on the different needs caused by urban– rural differences. Empirical studies have shown that differences in family attributes, gender difference, and population quality differences may cause travellers to prefer different options. Different and diversified travel characteristics are generated in the distribution of travel purposes, travel intensity, and travel space span. Further transport development should be based on the diverse travel needs under the influence of multidimensional factors to ensure that the needs of travellers are met to a greater extent, thereby improving social inclusion and the fairness of transport services. 2) A detailed overview of the relevant policies Based on the differences in travel demand brought about by urban–rural gaps, China’s existing transport development policies have all emphasised the concept of coordinating the development of urban and rural areas and promoting transport equality. The Plan for the National Highway Network (2013–2030), approved and issued by the State Council on June 21, 2013, pointed out that in future, China will accelerate the implementation of the strategy for coordinated regional development and the

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strategy for functional zoning, and it will accelerate the coordination of transport between urban and rural areas. It must also promote “overall planning for the coordinated development of urban and rural areas, and improving the quality of public services for highway transport.” The Outline for the 13th Five-Year Development of Urban Public Transport (Ministry of Transport, 2016, No. 126) issued on July 25, 2016 proposed the development of a vision of China’s urban public transport during the 13th Five-Year period, including to “achieve the equalisation of basic public services for urban and rural travellers, and to make people’s trips more efficient, more convenient, safer, more comfortable, more economical and reliable, and more friendly to the environment,” as well as to “promote public transport to become the prioritised choice for most travellers.” It showed that the coordinated development of transport services between urban and rural areas and transport equality are important parts of improving the quality of transport in China. On November 9, 2016, the Ministry of Transport issued the Guiding Opinions on Steadily Promoting the Coordination of Urban and Rural Transport and Improving Public Service Quality (Ministry of Transport, 2016, No. 184), further directing attention to the satisfaction of urban and rural travel needs, stating that promoting the coordination of urban and rural transport and improving the quality of public transport services are “urgent requirements for accelerating the overall coordination of urban and rural areas, narrowing the gap in regional development, and achieving targeted poverty alleviation.” It is also “important for advancing the construction of new and people-oriented urbanisation and achieving the goal of building a moderately prosperous society in all respects. These opinions emphasised the need to “accelerate the connection of urban and rural transport infrastructure and the coordinated construction of urban and rural transport services” and to “promote the sharing of resources including supply and marketing, tourism, and electricity between urban and rural areas.” These are taken as the basic principles to realise a coordinated pattern of regional development for urban and rural areas. For urbanised areas, the Basic Department of the National Development and Reform Commission issued the Plan for Comprehensive Transport Network in Urbanised Areas (Basic Department of the NDRC, 2015, No. 2706) on November 24, 2015. To adapt to the diverse travel demand of people in urbanised areas at different times better, it is necessary to “provide point-to-point direct services to connect different stops, as well as comfortable, punctual, and convenient high-quality transport services.” In view of the lack of transport facilities in some rural areas, China has made great efforts to promote the improvement of the quality and efficiency of rural roads, and to satisfy travel demand in rural areas as well as it does in urban areas. The General Office of the Ministry of Transport issued the Opinions on Developing the National Demonstration County in the Construction, Management, Maintenance and Operation of Roads in Rural Areas (General Office of the Ministry of Transport, 2017, No. 90), stressing that national demonstration counties should engage in “completing the construction of a rural road network that adapts to economic and social development, and vigorously promoting the operation of village buses, the coordination of urban and rural transport.” It also mentioned that promoting the

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improvement of the transport system can help “better [to] serve the economic and social development of the region and make contributions to the goal of Targeted Poverty Elimination.” Table 10.3 summarises the main points of these documents. 3) China’s practice of promoting transport equality based on the various travel demands of people a. Promoting barrier-free transport services In January 2018, the Ministry of Transport, together with other government departments jointly issued the Implementation Opinions on Further Improving Travel Services for Older People and the Disabled (Ministry of Transport, 2018, No. 8). It pointed out that the short-term goal of the barrier-free construction of China’s Table 10.3 The consideration on diversified travel demand in the policies and documents about China’s transport development in recent years Issue date

Document name

Relevant policies

June 21, 2013

Plan for the National Highway Network (2013–2030)

In the future, “China will accelerate the implementation of the strategy for coordinated regional development and the strategy for functional zoning and accelerate the coordination of transport between urban and rural areas.” It is necessary to promote “overall planning for the coordinated development of urban and rural areas and improving the quality of public services for highway transport.”

November 24, 2015

Plan for a Comprehensive Transport Network in Urbanised Areas (Basic Department of the NDRC, 2015, No. 2706)

“Provide point-to-point direct services to connect different stops, as well as comfortable, punctual, and convenient high-quality transport service.”

July 25, 2017

Outline for the 13th Five-Year Development of Urban Public Transport (Ministry of Transport, 2016, No. 126)

The development is expected to “achieve the equalisation of basic public services for urban and rural travellers, and make people’s trips more efficient, more convenient, safer, more comfortable, more economical and reliable, and more friendly to the environment,” as well as to “promote public transport to become the prioritised choice for most travellers.” (continued)

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Table 10.3 (continued) Issue date

Document name

Relevant policies

November 9, 2016

Guiding Opinions on Steadily Promoting the Coordination of Urban and Rural Transport and Improving Public Service Quality (Ministry of Transport, 2016, No. 184)

“Promoting the coordination of urban and rural transport and improving the quality of public transport services are ‘urgent requirements for accelerating the overall coordination of urban and rural areas, narrowing the gap in regional development, and achieving targeted poverty alleviation.’” “Accelerate the connection between urban and rural transport infrastructure and the coordinated construction of urban and rural transport services.”

February 13, 2017

Plan for the National Civil Transport Airport Layout (Basic Department of the NDRC, 2017, No. 290)

“Optimising the layout structure should start from the comprehensive transport system, give full play to the advantages of civil aviation in safety, speed, comfort, and flexibility, effectively connect railway and other transport networks, and take efficiency and equality into account.”

July 23, 2017

Opinions on Developing the National Demonstration County in the Construction, Management, Maintenance and Operation of Roads in Rural Areas (General Office of the Ministry of Transport, 2017, No. 90)

The national demonstration counties should “complete the construction of a rural road network that adapts to economic and social development, and vigorously promote the operation of village buses, the coordination of urban and rural transport.”

April 10, 2020

Opinions on Promoting the Connection of Rail Transport between Hubs and Airports (Basic Department of the NDRC, 2017, No. 576)

“Ensure that the rail transport development of the hub airport can satisfy the actual and diverse travel demand of the people, make up for shortcomings, and create a modern comprehensive transport system.”

transport services includes the following aspects.8 By 2020, the barrier-free travel service system of transport would be basically formed, the quality of barrier-free travel services, the quality of aging travel services and the quality of service equalisation would be significantly improved, and the quality of barrier-free transport facilities and equipment would be also significantly improved, to meet people’s travel 8

Data source: Government of the People’s Republic of China, http://www.gov.cn/xinwen/2018-01/ 22/content_5259285.htm, visited on November 15, 2020.

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Fig. 10.4 A barrier-free public bus in Shanghai. Data source http://www.venuesinc hina.com/venuesinchina_cn/ travel-class/news-detail.php? id=17029, visited on November 15, 2020

demands continuously. By 2035, a comprehensive barrier-free travel service system will be basically completed, a comprehensive, coordinated, safe and comfortable barrier-free travel service environment will continue to improve, and the quality of barrier-free travel services will be significantly improved to satisfy the travel demands of older people and the disabled better for a better life. Under the guidance of these opinions, many provinces and cities in China have actively promulgated relevant policies to strengthen the promoting of barrier-free transport networks. On August 19, 2018, the Shanghai Municipal Commission of Transport issued the Notice on Promoting the Construction of a Barrier-Free Environment in the Transport Industry in Shanghai, proposing a number of specific work arrangements to promote barrier-free transport of transport services9 : (a) the newly built rail transport stations and newly purchased rail transport vehicles should be equipped with barrier-free facilities in accordance with standards. Existing lines should be upgraded and adapted to local conditions to improve the barrier-free travel environment gradually; (b) In terms of buses, the updated or newly added pure electric buses in the city centre must be barrier-free vehicles, all of which have low floors. Buses in suburban areas can also be upgraded according to the actual conditions and actual needs; (c) in terms of taxis, 500–1,000 barrier-free taxis will be launched in the city within 3 years, based on the 200 barrier-free taxis launched for the first stage; (d) in terms of road traffic, all stations in the city must set up priority for older people and the disabled. Obstacle-free ticket-purchasing windows and green channels that give convenience to older people and the disabled should be promoted. Stations should have service signs for older people and the disabled in eye-catching locations, be equipped with wheelchairs and other barrier-free service equipment and set up a team of volunteers to provide oneon-one assistance to vulnerable travellers. Through these measures, Shanghai has vigorously promoted the barrier-free process for urban transport services, adapted to the diverse demand of various groups such as older people, the young, the disabled, and pregnant, and ensured the equality of supply of transport services (see Fig. 10.4). 9

Data source: Shanghai Municipal Transport Commission, http://jtw.sh.gov.cn/zxzfxx/20180930/ 0010-25575.html, visited on November 15, 2020.

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b. Developing bus–metro integration for the last mile In recent years, many cities in China have implemented the priority development of public transport, promoted the improvement of public transport services, improved people’s travel experience of public transport, and expanded the coverage of public transport. Many cities have formed a complete public transport system, with rail transport as the framework, ground public buses as the main body and other public transport services as supplements. With the rapid development of the social economy and the diversification of people’s travel demands, the problem of connecting public transport with other modes, as well as the problem of the last mile has become increasingly prominent. Different travel groups have significant differences in access to public transport. The large amount of time cost and the investment of energy necessary for connection and transfer has weakened the advantages of cheapness and convenience for public transport. In response to this, some cities have actively introduced relevant measures to facilitate bus–metro integration through adjusting their operation strategies. In September 2020, Hangzhou (in Zhejiang Province) successively implemented plans for long-distance, medium-distance, and short-distance travel to accelerate the digital development of public transport further, adapt to the diverse demands of people and create a public transport travel environment with a smooth bus–metro integration. Various forms of measures have been taken to help to connect people’s destinations with metro/bus stations, and to improve the public transport service system. Specific measures include: (a) shortening the travel time and providing convenience for residents in residential areas far away from the metro stations. To solve the problem that residents living near the Zonglv Road and Moganshan Road have to travel a long way to access public transport and suffer from the last mile issue, Hangzhou Public Transport Group adjusted the route of Bus No. 97 from Zijingang near Zhejiang University to Jihongjiayuan (which is a residential area), saving transfer time for residents far from the Baiyang metro station; (b) strengthen the connection from each high-density residential area to the metro stations to provide more efficient services for residents to commute to and from work. New bus lines have been developed such as Line 2 from Dezejiayuan (which is a residential area) to the Baiyang metro station; (c) provide special bus service to cover the last mile of travel by metro, set up stops near metro lines to serve people’s travel demand for commuting trips on weekdays better. The number of buses and the location of stops should be set based on the travel characteristics of residents in the morning and evening peaks to guarantee diversified travel demand in different periods and at different times. c. Piloting paid sharing of parking facilities On December 16, 2019, the Beijing Municipal Leading Group for Comprehensive Transport Management issued the Guiding Opinions on Promoting Paid Sharing of Parking Facilities in the City based on the Regulations for the Parking of Motor Vehicles in Beijing to promote effective use of parking facilities and parking spaces (called shared parking). The policy was expected to help to alleviate the contradiction between the limited parking space and of the large demand for parking. These

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opinions gave instructions on the basic principles, implementation methods, safeguards and other aspects of shared parking. The supply and demand parties of shared parking must sign an agreement on shared parking and obey it. Basic information about the users and shared parking vehicles, price for sharing, charge method and management requirements, the allowed parking period and the parking area should be clearly defined in the agreement. If any person fails to park the vehicle according to the agreement, or if the supplier fails to provide shared parking spaces according to the agreement, he or she must bear the responsibility for breach of contract to ensure the effective implementation and management of shared parking. These opinions also encouraged the suppliers of parking facilities to establish cooperation mechanisms with relevant companies to publish the idle status of parking spaces through an internet platform, so that residents with parking demand can learn about parking facilities quickly.10 It is better to promote online information exchanges and promote shared parking gradually to become market-oriented and scaled. The Beijing Municipal Finance Department and other relevant departments have formulated financial support policies for shared parking. The management method of paid parking facilities may effectively promote the flexible turnover of existing parking facilities in the city, improve the utilisation efficiency of limited transport resources, and help to meet the diverse parking demands of different residents at different times and for different uses, thus helping to alleviate the traffic pressure of scarce resources in big cities. However, shared parking is still in its infancy in some cities in China, and the related measures for management, operation, and supervision are not yet mature. In reality, parking facilities often have low utilisation efficiency, some car owners are unwilling to share and parking is chaotic. As a result, it is difficult to ensure true sharing for some shared parking spaces. According to related surveys,11 the signs of some shared parking spaces have been damaged, making it difficult for car owners to find the corresponding parking spaces quickly. Some shared parking spaces are even locked by the owners and cannot be shared by others any more. In response to the various problems in the actual operation of shared parking, the relevant managers and operators need to improve the rules and regulations for paid sharing of parking spaces, clarify the responsibilities and rights of all stakeholders, supervise the effective cooperation of all participants and realise more effective sharing of parking facilities. d. Regulating travel behaviour to build a civilised travel environment On May 15, 2019, the Beijing Municipal Transport Commission formulated and implemented the Opinions on the Implementation of Recording Personal Credit as Bad on Uncivilised Travel Behaviour in Rail Transport, which clearly stipulated the specific scope and manage measures of uncivilised travel behaviour to help to build up a good operation order and travel environment. The uncivilised travel behaviour included the following five aspects: (a) use illegal methods such as getting into/out 10 11

Data source: https://www.sohu.com/a/235480676_100116298, visited on November 15, 2020. Data source: https://www.sohu.com/a/235480676_100116298, visited on November 15, 2020.

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of the gates, forging ticket cards, etc., to evade payment of the fare; (b) one person occupying multiple seats at the same time on the train; (c) eating on the train, except babies and patients; (d) promoting products or engaging in marketing activities; and (e) playing videos or music loudly without a pair of earphones. Beijing has arranged staff, including full-time safety inspectors, civilised travel supervisors, rail operators, and attendants on rail transport to discourage and stop the above-mentioned uncivilised behaviour and to report it to the relevant public security and traffic departments. These measures could help to combat uncivilised behaviour and encourage the formation of a civilised travel environment. However, despite Beijing’s active formulation of the management measures, the number of management personnel who have the right to implement governance is still limited. The scope of supervision has been quite limited for many trains, so that some uncivilised behaviour still exists and has not been fully tackled. In addition, the publicity on governance measures against uncivilised behaviour is still insufficient, and some residents have relatively weak awareness of in the rules for actual trips and lack sufficient awareness of the above-mentioned management methods. When formulating and implementing those management measures for uncivilised travel behaviour, local governments should strengthen the publicity and promotion of their management systems, give play to the role of public supervision, and promote the formation of a clear and transparent civilised travel environment to ensure the full implementation of punitive measures and to help to realise transport equality for residents.

10.2 Experience and Lessons 10.2.1 Pay More Attention to Vulnerable Groups 1) Improve transport services in rural areas The different time allocation patterns and travel mode preferences of urban and rural residents have brought about differences in travel demand. On the one hand, urban residents seem to spend a larger proportion of their daily time on leisure, entertainment and social interaction activities, resulting in higher demand for traveling for leisure, and more diversified travel chains, while rural residents usually spend more time on doing housework and caring for children, and their demand for leisure activities is relatively smaller. Daily travel activities in rural areas mainly focus on purchasing livelihood necessities and selling agricultural products to make a living. Rural residents’ cross-regional travel from villages to towns and counties is more likely to come from rigid needs such as shopping and medical treatment. On the other hand, studies have shown that the car ownership and utilisation rates of residents in large cities, especially megacities, are significantly higher than those in rural areas. The fast-paced and high-intensity urban life usually makes people pay more attention to the convenience and efficiency, comfort and flexibility of transport, while in

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the context of China’s dualistic urban–rural structure, the level of socioeconomic development, the construction of facilities and the accessibility of life services have shown significant urban–rural gaps across many regions. The majority of rural areas especially those in the underdeveloped cities of western China have not built up a mature and popularised system of subways, light rail and other rail transport, and they have not promoted new forms of travel services such as shared bicycles, shared cars and online car booking. As a result, low-cost public transport that connects villages, towns and counties is still the mostly commonly used travel mode for long-distance trips, while short-distance trips of rural residents are usually made on foot. According to the empirical evidence, it is obvious that rural residents are relatively disadvantaged in terms of choice of travel mode, opportunities for cross-region trips, and the accessibility of travel service facilities compared with urban residents. In response to the huge urban–rural gap, regions need to improve the spatial layout of transport services to improve the quality of public transport services in rural areas. To build high-quality rural public transport, more attention needs to go to the connection between villages or rural residential zones and towns, counties and other commercial centres. If conditions permit, it is advisable to provide door-to-door or point-to-point delivery services for rural residents, especially those who have no cars and who cannot afford daily trips. 2) Give more support to migrant families Residents’ family attributes will affect their decision-making behaviour for individual travel activities. Among them, significant influencing factors include family size, family type (composition), family economic level, and family vehicle ownership. Studies have shown that single-person households have a higher trip frequency, more and diverse travel demand, and a higher proportion of trips related to leisure activities, while larger families and those with higher incomes are more likely to own at least one private car, although bicycles seem to be less used for daily travel. In general, family size, family income level, ownership of transport vehicles, and composition of family members such as having a young child and living with older relatives all have significant influence on residents’ travel behaviour. Considering the changing trend of China’s population growth and its structure, with family size getting smaller and family type getting more diverse, high-quality transport services should pay more attention to the different travel characteristics of different families to provide customised and personalised travel services that match the actual needs of population. 3) Pay more attention to female travellers Extensive evidence has shown that male and female residents have significant differences in various aspects of travel behaviour. Affected by the traditional cultural concepts in some areas as well as the different physiological characteristics, men and women often appear to have different characteristics in the social division of labour, living habits and family roles. The traditional Chinese social outlook has been known as the family model of men dominate the outside, women dominate the inside, which means that female members mostly take care of housework and raising children at home, and some even choose to leave their jobs to be full-time

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wives, while male members in the family usually earn money to support the family. The different family roles and social situations in the labour market make the daily travel chains of male and female residents largely different. Taking Beijing as an example, male residents are more likely to be engaged in travel activities associated with work and are more dependent on private transport. Compared with women, male residents have a relatively higher trip frequency and a relatively wider area for travel activities, while female residents’ leisure travel activities account for a significantly higher proportion than male residents’, and women are more likely to rely on public transport for their daily trips. Based on the significant gender difference in travel behaviour, a comprehensive transport service system needs to provide different and personalised travel services to protect women’s travel rights and to promote traffic fairness based on the temporal and spatial characteristics of the population’s gender structure. The quality of public transport should be actively improved giving full consideration to the travel characteristics of female residents. Public transport could be provided and operated within a female-friendly travel environment that is safer and more comfortable for female travellers, and special and customised services are necessary for pregnant women and those with limited mobility. 4) Care for low-education level people The difference in travel behaviour of residents with different education levels is mainly reflected in the choice of travel mode and the purposes of travel. In recent years, the overall quality of the population in China has steadily improved. The physical quality of the population has gradually improved, and the cultural and educational quality of the population has also largely improved. However, there are still some significant gaps among different regions in China in terms of education level. The education level of the eastern coastal cities is generally higher, while that in the western regions is relatively low, especially in underdeveloped rural areas. The allocation of educational opportunities and educational resources is also unbalanced in some western areas. The level of education affects the population’s lifestyles and ideology, which leads to differences in travel behaviour. Residents with relatively higher levels of education tend to have fairly fixed and stable employment positions in the labour market, their daily trips are mainly commuting activities, and they are more inclined to use public transport or cars. However, those with relatively lower levels of education might have no permanent jobs, some might have part-time jobs or even be unemployed, and thus their daily travel chains are relatively complex and changeable. The travel activities of these residents with lower education levels might be related to activities such as leisure, entertainment, shopping and consumption, and walking and cycling are more likely to be their travel mode, while the utilisation rate of public transport and cars is not as high as that of residents with relatively higher education levels. In view of the differences in the travel characteristics of residents with different education levels, a high-quality transport service system that adapts to population development should provide refined and customised transport services for different groups of people, improve the inclusiveness of public transport, and

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provide convenience for residents with low education levels who might have difficulty in using some smart and advanced vehicles. Easy-to-operate travel services need to be provided to promote the transport equality and social fairness.

10.2.2 Advance the Comprehensive Transport System 1) Construct better facilities To build a transport system that is compatible with the development of China’s population, it is necessary to build a hierarchical transport network that is compatible with the spatial distribution of the population. It is necessary to promote the coordination of transport hubs and mixed land use, and to improve the construction of commercial service complexes based on transport hub. It is necessary to promote and provide customised public transport services to achieve a precise docking of services supply and people’s actual demand. It is necessary to optimise the design and planning of urban road network and other traffic facilities to improve the travel environment. It is necessary to encourage refined design of transport service facilities based on the different and various daily needs of the population. It is necessary to promote the large-scale application of green and low-energy vehicles to make contributions to creating a sustainable living environment. It is necessary to promote the diversified construction of transport infrastructure and encourage the diversification of the forms of public transport services. 2) Optimise transport services The quality of transport services should be optimised and improved based on the people-oriented concept. It is necessary to provide diversified and customised transport services based on the different travel demands of different groups of people. To move forward with this goal, it is advisable to pay attention to the differences in travel characteristics between urban and rural areas, and to improve the transport system according to local conditions and individual conditions. It is necessary to pay attention to differences in travel characteristics among different types of families, and to promote the improvement of car-sharing services. It is necessary to pay attention to differences in travel characteristics between male and female travellers, and to protect the travel rights of women. It is necessary to pay attention to differences in travel characteristics related to population quality, and to provide transport assistance services for those with difficulties and disadvantages to promote the seamless and integrated development of multiple types of public transport modes. It is necessary to build a monitoring and dynamic forecast platform for people’s travel demand to improve the resilience of public transport services to meet the actual needs of people. It is necessary to build a comprehensive transport big data platform to identify diverse travel demand accurately. It is necessary to pay more attention and to give more care to disadvantaged groups, and to provide diversified and inclusive transport services for disadvantaged groups and travellers with special purposes.

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3) Improve the management To build an inclusive transport system, it is necessary to improve the quality planning and management of the transport system comprehensively. It is necessary to provide flexible and adjustable measures for diversified travel demand in a timely manner. It is necessary to encourage and improve the coordination and cooperation of different fields in transport. It is necessary to promote the improvement and popularisation of shared transport services, perfect the market regulations and management methods. It is necessary to improve the safety management system of transport, and strictly to punish travel behaviour that violates laws and regulations. It is necessary to strengthen the supervision of the public transport system with new technologies to improve the ability to respond to public health emergencies. It is necessary to build an interconnection and communication platform between the traffic management departments and travellers to promote the refinement of the transport management system. It is necessary to improve the traffic management system and strictly to prohibit behaviour that disrupts traffic order and endangers the public environment, as well as to establishing a civilised travel credit mechanism to maintain transport fairness.

10.3 Policy Recommendations: Building Sustainable Transport for All 10.3.1 People-Oriented Transport Services (1)

(2)

Develop a hierarchical transport network in harmony with the characteristics of population distribution. Make efforts to build a multi-level comprehensive transport network cross regions and form a comprehensive transport service system that is coordinated and unified at the macro level and differs according to the population distribution at the micro level to adapt to the distribution of population size, population density and population structure. Always implement the people-oriented concept, strengthen the coordination between service provision and the characteristics of people’s demand, and provide convenient, efficient, high-quality, and green travel services that adapt to the modern development environment and people’s growing needs for a better life. Give priority to the development of public transport and adapt to the characteristics of regional development. Promote the optimisation of the public transport network especially in high-density and high-congestion cities, adjust routes and train schedules over time to adapt to changes in travel demand caused by the changes from urban development and population structure, and further improve the usage rate of public transport in residents’ daily trips. Promote the construction of public transport facilities in low-density, remote suburbs and rural areas, effectively connect different residential areas and central urban

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(3)

(4)

(5)

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areas. Help to make public transport the main travel mode for local residents and narrow the urban–rural gap in bus accessibility. Strengthen the application of intelligent technology in urban public transport management. Promote the orderly linking of the 4-level transport network, including the networks between cities, inside cities, between urban and rural areas, and in rural areas. Extend the bus lines in central urban areas to the suburbs, and thus expand the coverage of public transport. Promote the seamless connection and coordinated development of multiple public transport modes. Adapt to the diversified trend of the population’s lifestyle and travel habits and enhance the ability of public transport to meet residents’ travel demands and achieve precise services such as door-to-door and point-to-point transport. At the hardware level, comprehensively manage the infrastructure around stations for multiple travel modes such as buses, subways, public bicycles, etc., to achieve seamless connections. At the software level, comprehensively promote the interconnection of various types of transport card, accept different payment methods such as flash payment, virtual card payment, mobile payment and other non-cash methods, shorten the transfer time, and provide discount tickets for interline rides, and transfer trips. Encourage the use of public transport throughout the journey and create a green environment for combined uses of various public transport modes. Improve the quality of public transport services and give more care to the travel demands of disadvantaged groups. Promote barrier-free urban public transport, and realise the full coverage of barrier-free facilities along the whole journey by public transport, including ticket purchasing, waiting at platforms, getting on/off the train/bus, transferring and getting in/out of stations to provide more convenience for older people, and the disabled, pregnant women, and children. Apply internet and intelligent technologies such as electronic stop signs and mobile phone real-time inquiry services that provide the public with accurate and reliable information such as the location of vehicles and estimated arrival time. To reduce the travel cost and travel burden of disadvantaged groups. Pay more attention to transport equality and provide inclusive and diverse transport services to satisfy the travel demands of different groups of travellers. Build a monitoring and dynamic forecast platform focusing on people’s travel demands and improve the resilience of public transport services to meet the actual demand of the people. Build an intelligent real-time monitoring platform for travel information, dynamically predict the travel demand of different districts and different stations, update the carrying capacity and the number of available facilities in time, and appropriately increase the supply of services for travel routes with intensive demand and shortage of supply. Through these measures, make the public transport system more resilient to adapt to the dynamic changes of people’s travel demand, coping with traffic congestion in peak hours and high-density areas. Help to build a convenient and efficient environment for all kinds of people to take public transport during different hours and periods.

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(6)

Promote the improvement and popularisation of shared transport and rental travel services and improve the quality of market management. Adapt to the changing trend of smaller family sizes by strengthening the development of shared transport such as rental cars and shared bicycles. Expand the coverage of shared transport services and appropriately add more space and facilities for developing shared bicycles, such as intelligent docking stations. Encourage technological innovation to develop humanised and intelligent transport facilities for sharing, such as Bluetooth search devices, fingerprint verification devices and automatic alarm devices. Use intelligent supervision technology to promote the standardisation of the market for bike and car sharing. Improve the relevant laws and regulations to crack down on illegal travel behaviour promptly and use the real-name system and credit system for shared transport if necessary. Promote the large-scale application of green and low-energy vehicles to promote a sustainable environment. Develop urban public transport, rental taxis and sharing of parking facilities to increase the promotion and application of energy-saving technologies and new energy vehicles further. Improve policies and regulations for industry operation and accelerate the elimination of high-energy-consumption, high-emission vehicles and other vehicles that are harmful to the environment. Expand the configuration of convenient, efficient and moderately advanced charging stations for electric vehicles to provide guarantees for the safe and smooth travel of green vehicles. Optimise the planning and design of the road network and relevant transport facilities and improve the travel environment. Provide more humanised and refined road space and traffic design, and build a safe, green and comfortable slow traffic system for cyclists and pedestrians. Intensify the construction of non-motor vehicle lanes and walking lanes to ensure travel space for non-motor vehicles and pedestrians. Speed up the separation of motor vehicles and non-motor vehicles and reduce interference among pedestrians, bicycles and motor vehicles. Construct and improve pedestrian stop zones, safety islands and other cross-street facilities and 3-dimensional traffic facilities such as pedestrian bridges and underground passages in accordance with relevant standards. Plan and construct pedestrian corridors, overpasses (cross-street bridges) and underground passages in areas with high-density travellers, such as streets near commercial districts, schools, hospitals, and transport hubs to form a relatively independent and much safer walking environment. Promote the coordination of facilities for transport hubs and land use and improve the construction of transport hubs based on the diverse needs of travellers for shopping, catering, electronic vehicle charging, and information inquiries during trips. The coordination of transport facilities and mixed use of land should be promoted as a whole, relying on the high density and high traffic flow of passengers’ transport hubs such as transfer stations to activate the usage of spatial resources. Promote the building of complexes that may provide transport services, consumer services, catering services, internet

(7)

(8)

(9)

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services and other services to ensure that various daily or emergency needs can be met during trips, and thus optimising people’s travel experience. (10) Improve the management system for transport safety, and strictly punish illegal travel behaviour using relevant laws and regulations. Promote a more civilised transport environment and establish more comprehensive guidelines and regulations for civilised travel behaviour. Evaluate the quality and efficiency of local traffic, implement targeted encouragement or crackdown policies. Encourage civilised and orderly travel behaviour and stop uncivilised and impolite travel behaviour during people’s trips in time. For example, eating food, begging, fare evasion and jumping in line in public transport should be strictly forbidden. Depending on the seriousness of the circumstances, violators should be severely fined or punished through other methods. Severely crack down on behaviour that disrupts public transport order, such as theft or sexual harassment especially on the subways and buses. Equip trains and buses with real-time alarm devices and encourage the public to report and expose illegal behaviour. Increase the punishment for traffic crimes. Through these measures, the cost of crimes in transport could be increased. Areas with high traffic crime rates and relatively more serious problems could be designated as keys for restraint governance to promote a better travel environment and to ensure travel safety while developing a people-oriented transport system.

10.3.2 Refined and Customised Transport (1) Pay more attention to the differences in travel characteristics between urban and rural areas and improve the transport system according to local conditions and individual preferences. In view of the differences in population structure, occupation and family types, resource endowments and other differences in different geographical environments, build multi-level, refined and customised transport network services that are adapted to the actual demands of cities, towns and villages. For urban agglomerations, large cities or high-density central areas, focus on satisfying people’s demand for comfort, patency, and efficiency caused by high-density built environment. Strengthen the seamless connection between different modes of public transport, and between public and private transport. To reduce the money cost and time cost for transferring from one mode to another mode, ensure the smooth operation of the transport network especially in high-density areas and to improve the comfort and safety for people’s trips while taking account of efficiency. For suburbs, villages or other areas with lower population density, less travel demand and more scattered locations, place emphasis on enhancing the connectivity and convenience of transport services and improving the accessibility of public transport for scattered residential areas. Set up customised public transport lines according to residents’ commonly chosen travel routes and main travel purposes. Connect villages or residential areas to city centres, passenger transport hubs, urban areas and other

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places, and accurately provide door-to-door and straight to the destination public transport services based on the local conditions. Pay more attention to the differences in travel characteristics among different types of families and promote the improvement of car sharing services. Adapt to the differentiated travel habits of families with different sizes, different economic conditions and different composition of members, and provide customised transport services. Focus on providing convenient and efficient public transport services for small households, encourage families to buy fewer or no cars and thus reduce the car ownership rate, and increase the usage rate of public transport modes. For households with more members, provide car sharing, which could include short-term and long-term sharing to reduce car usage and to alleviate traffic congestion in big cities as well as promoting public transport. Formulate multiple charging standards for families with different income levels, provide appropriate ticket discounts of public transport for lowincome families, and make use of transport services for narrowing the gap between the rich and the poor. For families with young children, strengthen the promotion of child safety seats, enforce safety measures and provide green travel channels for dealing with emergencies such as seeking medical treatment. Pay more attention to the differences in travel characteristics between male and female travellers and protect women’s travel rights. Given that women are more likely to use public transport and have more diverse travel purposes, promote customised services for women in public transport, such as providing special seats for mothers and infants, safety seats for pregnant women on trains and rest space for pregnant women and infants at bus stops, etc. Promote the application of female-only safety carriages if necessary. Deploy multi-directional monitoring devices in public transport stations and trains with high density and high traffic flow. Eliminate the blind spots in public places, and strictly curb illegal behaviour that infringes women’s travel rights. Apply intelligent recognition technology to screen the public transport travel space in real time, identify possible dangerous situations and take timely measures to avoid possible accidents that may threaten the safety of female travellers. Pay more attention to the differences travel characteristics related to population quality and provide targeted transport assistance services for those with difficulties. Based on the differences in the travel demand of people with different levels of education or physical fitness, provide groups who have relatively lower population quality with universal guidance signs about new vehicles and facilities to promote the use of intelligent systems and information systems. Taking account of the possibly difficult and inconvenient situations of some travellers, provide alternative measures to expand the coverage of achievements of China’s modern transport development. Give full play to the value of helping relatively disadvantaged groups in people-oriented transport services. Reduce the difference in travel rights and interests among different groups of people to avoid possible social discrimination and psychological isolation. Promote and improve customised public transport services. Encourage the development of customised buses, dedicated lines and other travel services,

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and provide targeted services for the travellers with specific demand. Use online application, online reservation, two-way selection between bus operators and users and usage recording and post-use evaluation, etc. to standardise the service process of customised public transport. Provide differentiated customised services for special groups, such as providing full barrier-free services for older people and the disabled, requiring drivers to have first aid knowledge, and providing safe, comfortable and interesting facilities for infants and young children. Provide a slower and more stable travel experience for pregnant women and encourage shared cars/bicycles, taxis and public transport to be equipped with medical supplies to respond to emergencies. Prioritise pilot projects and gradually promote and improve the customised transport service system and the precise docking of services, and people’s travel demand will be realised. (6) Build refined transport service facilities and optimise the transport design according to the differentiated travel demand of people. Consider people’s difference in travel preference in the design of traffic service facilities, such as reasonable layouts for pedestrians, intersection positions for bicycles and motor vehicles, road width, road smoothness, number and spacing of street lights, rest seats in streets, vehicle parking devices, etc. For communities with high numbers of older people, improve the barrier-free level of street design to create safer neighbourhoods. For communities near schools or with high proportions of children, set up environments that promote slow travel modes (such as walking and cycling) to avoid threats to children’s safety. At the same time, provide appropriate facilities for children’s leisure activities in the travel environment to enhance travel happiness. (7) Strengthen the supervision of the public transport system and improve the ability to respond to public health emergencies. Implement the real-name system and credit system for public transport, make residential point-to-point travel records traceable and file travel information, and respond to the traceability requirements when faced with public health emergencies. Make the public transport environment more queryable, traceable and transparent. For the travel demands of some certain regions or areas, some specific groups, and some specific purposes brought about by emergencies, enable public transport to formulate special travel services in time, adhere to the people-oriented concept and ensure social safety and public order. (8) Build a communication platform for encouraging communication between the traffic management department and travellers. Comprehensively promote the application of big data platforms and information technology, build and improve a communication platform where the traffic management department can know about the changes in people’s travel demand in real time and provide multiple online and offline accesses, such as online through mobile apps, computer webpages, TV publicity, offline through the use of electronic browsing devices, electronic message boxes etc. near bus stops. Promote the disclosure of operational information about public transport to travellers, and at the same time

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enable feedback on travellers’ differentiated travel demand, helping to refine transport services further. (9) Promote the refinement of the transport management system. Strengthen the ability to monitor, analyse and predict traffic conditions, encourage the installation of one-way lanes, adjustable lanes, tidal lanes, shared lanes and other facilities on roads if possible, promote intelligent use of urban road traffic signal lights, and improve the efficiency of road traffic. Build an intelligent traffic management system that integrates command and dispatch, signal control, traffic monitoring, traffic law enforcement, vehicle management, and information release. Promote the interconnection, exchange and sharing of traffic information between departments of management and different travel modes. Make efforts to respond as much as possible to the differentiated and diversified travel demands of different groups of people that are dynamically changing.

10.3.3 Provide Inclusive and Fair Transport (1) Build up a comprehensive transport big data platform to identify people’s diverse travel demands accurately. Deeply promote the sharing and opening of transport big data. Encourage government departments, transport operators, and travellers to participate in the exchange of information, such as information on the operating capabilities, development plans, and actual travel demand. Apply 5th-generation mobile communication technology (5G), intelligent information technology such as satellite communication networks, to identify and monitor traffic problems, and collect bottom-up information feedback to learn more about the actual needs of different groups of people. By maximising the coverage of these technologies, improve the quality of transport services and advance the realisation of transport equality. (2) Promote the diversification of the forms of public transport services. Encourage transport companies actively to expand diversified bus services such as customised buses, night buses, and community buses to adapt to the tidal distribution over time and unbalanced distribution across different areas in terms of traffic flow and travel demand. Strengthen the combination of multiple public transport modes and improve the efficiency of transfers. Promote the coordination of urban and rural public transport, while at the same time providing different types of specific transport services for urban residents and rural residents, such as short-distance commuting, long-distance commuting, crossregional travel into the centre, and cross-regional travel inside the suburbs. Promote the seamless connection between the urban and rural transport systems, reduce the gap in travel opportunities between urban and rural areas, encourage cities to extend bus lines into the suburbs, and improve the level of equalisation of public transport services. (3) Promote the social inclusiveness of public transport and make efforts to form transport equality. Encourage full consideration of disadvantaged groups and

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special groups in public travel spaces such as at stations and in buses/trains. Promote the application of low-floor buses, child safety seats, pregnant women’s safety seats and ramps for wheelchairs in public transport, and create a more inclusive travel environment. Use voice prompts, Braille signs, Braille fare lists, emergency aid devices and other facilities to ensure the travel safety of vulnerable groups. Strengthen the publicity of social care in the public transport service system, encourage the cultivation of a civilised and courteous social atmosphere where travellers help each other, and thus make contributions to promoting transport equality. (4) Promote the diversified construction of transport infrastructure. Encourage the installation of one-way lanes, variable lanes, tidal lanes and other transport facilities on urban roads if possible and necessary. Improve the flexibility of using hardware facilities to adapt to the diverse travel demands of various groups and provide high-quality and more diversified transport facilities. Encourage the installation of 3-dimensional pedestrian facilities such as safety islands, overpasses, and underground passages to optimise the slow traffic system. Separate the flow of motorised and non-motorised traffic and separate the travel space for vehicles and passengers based on the actual conditions. Through the combined configuration of multiple transport infrastructures, ensure the smooth flow of vehicles and the travel safety of pedestrians. (5) Provide a variety of transport service charging rules and types of discounts. Comprehensively promote the interconnection of urban transport cards and promote the popularisation of non-cash payment methods such as flash payment, virtual card payment, and mobile payment in public transport, bicycle sharing, parking fees and other travel situations. Promote the implementation of various forms of discounts such as tiered ticket fares, discounts for transfers and cumulative discounts. In view of the diversified characteristics of travel purposes and trip frequency of different groups of people, provide daily, monthly, quarterly and annual cards to reduce the overall travel cost of travellers who have more travel demands and higher trip frequencies, such as commuters. In view of diversified travel routes, promote the coordination of multiple public transport charging systems and encourage people to integrate bus and rail transport with bicycle through ticket discounts. (6) Optimise the traffic management system, and severely crack down on behaviour that disrupts traffic order and endangers the travel environment. Accelerate the implementation of laws and regulations relating to comprehensive transport services, strengthen the popularisation of those traffic rules and regulations, and promote a civilised and harmonious travel environment. Increase penalties and increase the cost of illegal driving. For illegal driving phenomena such as red light running, speeding, illegal parking and other illegal driving behaviour, the reduce driving licence score and require criminal responsibility when necessary. At the same time, strengthen inspections through various methods such as increasing inspection sites at peak hours in roads with high traffic flow so that violations can be definitely and strictly punished and the fluke psychology in traffic driving behaviour can be eliminated.

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(7) Establish a civilised travel credit mechanism to maintain transport equality. Supervise the travel behaviour of travellers through the credit record mechanism, delineate the rating standards for uncivilised behaviour, conduct credit scoring for travel behaviour and give rewards and punishments to combine the credit records with personal rights such as credit loans and credit mortgages. Encourage travellers to regulate their travel behaviour consciously and improve public supervision and complaint mechanisms in public transport to ensure transport fairness and the popularity of credit mechanisms. Build a travel atmosphere that is in consistent with the growing needs of people for a more harmonious, civilised and beautiful life.

Chapter 11

Summary

Interaction between population growth and transport is a primary driver for regional development, and matches between population growth and transport supply significantly influence transport efficiency. Among the rich context of population growth, population structure is the key to determine the structure of transport demand and investment in transport facilities for a country or a region. The characteristics of population structure play a significant role in the country’s development process, and this is a significant reflection of social stratification. At the same time, population structure undergoes dynamic changes and spatial heterogeneity. Changes in population structure are major and important elements of social transformation, and they can have great impacts on the characteristics of transport demand. To improve the service quality, a country’s investment in transport facilities and management should both be considered based on the temporal and spatial characteristics of the population. Coupling of population growth and transport is an important part of human-land harmonisation, as well as the basis for developing people-oriented and sustainable transport especially in developing countries with large populations. This book has made comprehensive observations on the temporal and spatial characteristics of China’s population structure as well as a deep exploration of the relationship between population structure and sustainable transport. The main purpose of this book is to provide readers with a comprehensive but deep look at China’s population and its great importance for the development of a modern, high-quality, peopleoriented and sustainable transport system. To achieve these goals, the focus has been on three questions: what is going on with China’s transport and population, how closely does the population structure match transport demand and what we can do to build a sustainable comprehensive transport system based on the characteristics and changes in population.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Zhao and D. Yuan, Population Growth and Sustainable Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-7470-0_11

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Unlike most previous studies discussing the possible influencing factors of travel behaviour, this book focuses on the effects of population structure. To analyse China’s population structure comprehensively, four aspects, namely urban-rural structure, family structure, gender structure and quality structure of population are discussed. The analysis of the relationship between these aspects of population structure and travel behaviour has provided extensive empirical evidence that has made significant contributions to the existing literature. In terms of China’s urban-rural structure, previous studies have mainly focused on comparisons of the travel behaviour of urban and rural residents, without discussing the possible reasons behind these differences. The standards for dividing urban and rural areas vary largely between different countries and regions. This book pays close attention to the classification methods of urban and rural, and central area, suburbs and outer suburbs. The urban-rural gap in travel behaviour and transport demand have been discussed from three different research perspectives: the nation-level comparison between urban and rural residents, the citylevel comparison between urban and rural residents, and the within-city comparison between central areas and suburbs. In terms of China’s family structure, although there have been many studies providing evidence in Europe, cases in China are much fewer and conclusions are still not that clear. Attributes such as family type remain underexplored. In this book, we have taken the potential impact of family members into consideration (including having a child and having older members) in addition to normal attributes like family size and family income. In terms of gender structure, previous studies in developing countries such as China seem to have focused mainly on certain aspects of travel behaviour or they have only discussed certain groups of people. Discussions on the possible reasons for gender differences in travel behaviour are still unclear. In this book, multiple aspects of male and female residents’ travel behaviour have been compared, including travel purpose, mode choice, trip frequency and the spatial range of daily trips. In terms of quality structure of population, education level has acted as an accessible and quantitative measurement for quality. This book has provided more evidence and possible explanations for the significant gaps between groups with different education levels to make contributions for guiding the sustainable development of China’s transport system and better promoting social equity. To provide a comprehensive understanding of China’s population growth and transport development as a basis for further discussion on people-oriented sustainable transport, this book has discussed the historical achievements, current characteristics and future changing trends of the transport system and population structure from Chaps. 3 to 6. By integrating various sources of statistical data, it has shown that both the transport and population have come into a critical period of transition. For transport development, China has made a great achievement in building a modern and high-quality comprehensive transport system through three periods in the past few decades. Since the 13th Five-Year Plan, China’s transport corridor network has been characterised by 10 north–south and 10 east–west corridors, and it has entered a stage of improvement. The total fixed-asset investment in most transport sectors has gradually shifted from mass implementation of new projects to an equal emphasis on implementation of new projects and adjustment of existing projects. However, the

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matching level between the incremental distribution of transport investment and that of the population still needs to be strengthened, especially in the railway system and highway system, according to our analysis. For population structure, typical characteristics and changing trends have been found in all four aspects of urban-rural structure, family structure, gender structure and education structure. Since the founding of the People’s Republic of China, the total population has shown a stable trend of linear growth. However, the overall natural population growth rate appears to be reducing and the population growth as begun to slow down. With the introduction and advancement of the new urbanisation concept (which means people-oriented urbanisation), China’s urbanisation development in the future will shift from increase to qualitative improvement. Meanwhile, China’s fertility policy has gone through great changes and been optimised, which is certain to have further impacts on the family structure, making it more varied and complex. Gender ratio remains at a relatively high level. However, with China’s great efforts at promoting gender equality, the demands and needs of female groups have been and should be given more consideration in the provision of transport services. There is also evidence that the average education level of the population is continuing to increase, and the illiteracy rate is gradually decreasing, with gender difference in education levels tending to shrink. In addition, quantitative analysis of the service level and accessibility of China’s transport system has been conducted considering the spatial pattern of population. In general, the spatial distribution of the rail network, expressway network and aviation hubs is consistent with the population’s spatial distribution, divided by the Hu Huanyong Line. However, there are still large inter-regional differences in the direct service levels of various transport facilities across the country, and mismatches between facility construction and the population’s spatial layout still exist in some areas. Like the service level, the spatial pattern of regional accessibility is clearly higher in the east and lower in the west. To sum up, this indicates that the stage of filling in gaps and making up for shortcomings in China’s transport system has basically completed. Further improvement should be directed to matching the construction of transport facilities and provision of services with the temporal and spatial characteristics of the population structure. In terms of the relationship between demographic factors and travel demand, four aspects of population structure were explored to explore the difference travel behaviour among different groups of people from Chaps. 7 to 10. Taking Beijing as an example for city-level analysis, we found that the gap between urban-rural residents, central-suburbs residents, families with different socioeconomic attributes and individuals with different education levels are all significant and deserve further discussion. For example, whether they are divided by urban-rural, city-town-village or centre-suburban attributes, residents far from the city centre are more likely walk or to use e-bikes, motorcycles and other autonomous modes than those living near the city centre. Rural residents have also shown quite different characteristics in the travel purposes of their daily trips. In terms of family structure, evidence from Beijing has shown that small families have a higher proportion of trips relating to leisure activities, which may have something to do with the greater social communication needs of single-person and 2-person households. In addition to family size, family

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structure also has an impact on residents’ travel patterns, and families with older members tend to use buses or walk more frequently. For gender difference, female and male residents have significant differences in life habits, travel behaviour, and travel preferences. In general, men are more likely to drive a car for commuting, and women are more likely to take a bus or subway. There is also evidence that men are more likely to take trips for work and official activities, while women have a higher demand for activities relating to leisure and shopping. In terms of the relationship between travel behaviour and education level, the morning peak time of Beijing residents is mainly between 7:00 and 8:00, but the departure times of daily trips of residents with different education levels appear quite different. This indicates that the travel activities of uneducated residents are relatively free and more flexible. This finding may be related to their employment status and occupational types, which are usually more flexible with lower employment rates than residents with higher education levels. In addition, travel purposes for highly educated people mainly relate to work and official affairs, while activities such as leisure, entertainment and shopping account for a higher proportion of trips for low education groups than for high education groups. Evidence from Beijing has provided a clearer and more comprehensive understanding of the different travel behaviour and travel preferences among groups with different demographic attributes. Based on this analysis and the discussion on the varied travel behaviour and transport demands among different population groups with different demographic and socioeconomic attributes, we have identified policy implications in this chapter. It focuses on three aspects of building a people-oriented sustainable transport system as the development goal, namely developing a people-oriented and high-quality transport system, promoting refined and customised transport that adapts to differences in population travel characteristics, and paying attention to the diversity of people’s travel demands and supporting vulnerable groups to promote transport equity. For each aspect, a review on the relevant policies, a summary on the advanced measures and practice of some cities and suggestions on further development according to the previous research conclusions are organised to show comprehensive policy implications. Through detailed review and discussion, the research conclusions in previous chapters are developed into actual guidance for the country’s sustainable transport development. To conclude, through analysing the temporal and spatial evolution of China’s population and transport system based on empirical evidence and policy practice, this book has comprehensively depicted the current characteristics of the population structure in China, as well as looking forward to the temporal and spatial changing trends in future population growth. Characteristics of social differentiation associated with the travel behaviour and transport demand have been summarised. Compared with the existing literature, we have made further steps by analysing possible explanations for the formation of social differentiation in travel behaviour as well, which can provide empirical guidance for providing transport services according to local conditions and people’s actual transport demands. Furthermore, this book has applied the results and findings to develop policy implications aimed at the people-oriented

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and sustainable development of future transport in China. The results and conclusions in this book are also likely to make contributions to policymaking and transport development in other developing countries and regions in the world, by taking China as a typical case. Only when the characteristics and changing trends of population become the vital basis will the comprehensive transport system of the country or the region become better quality, more people-oriented and more sustainable.

Index

A Air transport, 35, 54 Alipay, 323, 330 AutoNavi Maps, 330 Average life expectancy, 59, 62, 63

B Bachelor’s, Master’s and PhD degree, 136, 140, 141 Beijing Comprehensive Travel Survey (BCTS), 25, 26 Beijing Municipal Commission of Transport, 25 Beijing Municipal Office on Aging, 106 Beijing Municipal Public Security Bureau, 316, 318 Beijing’s Comprehensive Survey of City Traffic Issues, 199, 226 Beijing–Tianjin–Hebei region, 147, 150 Brazil, 156 Built environment, 233, 238, 246

C Central area, suburbs and outer suburbs, 171, 206, 207, 210, 216, 221 Central Committee of the Communist Party of China (CPC), 15 Chengdu–Chongqing region, 147, 150 China, 5, 6, 8, 11, 13–24 China Family Panel Studies (CFPS), 20–22 China Household Survey Yearbook, 85 China’s 1-child, 2-child and 3-child fertility policy, 97, 105, 120

China’s household registration system, 66 China’s National Census, 107, 109, 115, 123, 130 China Statistical Yearbooks, 23 China’s Traffic Power Strategy, 15 Chinese People’s Political Consultative Conference, 327, 328 Comprehensive Transport Corridor, 31, 34–40, 56, 57 COVID-19, 312, 328–330

D Departure time, 289, 291–294, 300 Dependent variable (explained variable), 174 Descriptive statistical analysis, 173, 186, 191, 205 Detailed Australian time-use data, 279 Discrete Choice Models (DCMs), 174 Disposable income per capita, 68–70, 72–75, 77, 91–93, 95 Dualistic urban–rural structure, 65–67, 77, 81, 94, 95 Dummy variable, 254, 257, 261

E Education level, 287–290, 292, 293, 295–301 18th Party Congress, 15 Energy Conservation and Emission Reduction, 314, 315 Estimated coefficient, 255, 258–261 European Member States, 271

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Zhao and D. Yuan, Population Growth and Sustainable Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-7470-0

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358 Expressway, 150–152, 157, 164, 165

F Family planning, 101, 108–111, 114–118, 120–122, 131 Family structure, 233, 234, 236–238, 240, 255, 262, 263 Fertility policy, 61, 94 5th-generation mobile communication technology (5G), 348 Five-Year Plan, 31, 33, 34, 37, 38, 54 Fixed-asset investment, 42–57 Four Aspects of Better Rural Roads: Construction, Management, Maintenance and Operation, 312 Frequency distribution graph, 179 Full-time jobs and part-time jobs, 274, 276, 279, 282

G Gender difference (Gender gap), 267–273, 276, 278–283 Gender equality (Gender inequality), 270, 271 Gender ratio, 119, 123–127, 132–134, 144 Gini index, 159 Guangzhou Metro, 328

H Hangzhou Public Transport Group, 329, 336 Happy Family Farm ("nongjiale" in Chinese), 81 High-Speed Rail, 152, 163 Highway Transport, 42–45 Hong Kong, Macau and Taiwan, 159 Hu Huanyong Line, 153–155, 164 Huilongguan and Tiantongyuan residential areas, 317 Hukou, 5, 13 Human-land harmonisation, 351

I Illiteracy rate, 128, 129, 136, 139–144 Income from wages and salaries, 71, 76 Independent variable (explanatory variable), 174 Industrial complex, 215, 216 Inner Mongolia, 151, 158, 159, 165 Integrated Transport, 304, 307, 322, 323

Index J Japan, 149, 156 Jing–Hang Grand Canal, 32 Joint family, 104–106

L Labour force participation rate, 131, 132, 144 Ladies-only carriages, 327, 328 Left-behind children, 110, 111 Left-behind older people, 111, 112 Log odds ratios, 259, 260 Logistics industry, 55 Lorenz curve, 159–161

M Main family (direct family), 104 Megacities, 171, 188, 194, 221 Migrant children, 109–111 Ministry of Housing and Urban–Rural Development, 24 Modern socialist country, 304 Motorised vehicles, 295, 296 Multicollinearity problem, 184 Multinomial logit (MNL) regression, 173 Multiple ordinary least squares (OLS) regression, 173

N Nanjing, 296 National Bureau of Statistics, 23 National Detailed Survey of Small Towns, 24 National Health and Family Planning Commission, later renamed National Health Commission, 108, 111 Net business income, 68, 73, 75, 76, 78, 79 Net income from properties, 68, 76 Net income from transfer, 68, 76 New information technology, 297 Nigeria, 279 Nuclear family, 104–106, 143

O OD pattern, 280, 281 Online Rental Bicycle Services (in Shanghai), 315

Index P Peking University Geographic Data Platform, 23 People’s Daily, 54 Planning Scheme for the Overall Layout of Major Water Transport Corridors in China, 32 Point-to-point service, 323 Population agglomerations, 147, 150, 163, 165 Population and Family Planning Law, 116, 117 Population quality, 289, 290, 300, 301 Population structure, 4–8, 10–21 Postgraduate, 256 Prefecture-level cities, 147, 149, 151, 152, 158–161 Private cars, 288, 295, 296, 300 Provincial capitals, autonomous regions and centrally administered municipalities, 32 Public transport, 288, 295, 296, 300

R Railway transport, 34, 38, 49–53, 56 Rapid transport hubs, 163 Reform and opening-up, 13 Regional development, 1, 3, 16, 19, 23 Rental Cars, 306, 310, 325, 326, 344 Rural migrants, 67, 81, 83, 85–87

S Shanghai, 288 Shared transport, 315, 316, 342, 344 Shekou Industrial Zone, 82 Shentong Metro Group, 330 Single family, 105, 106 Single-parent family, 105, 106 Social equity, 289, 301 Socialism with Chinese characteristics, 303, 304 Social transformation, 11, 13 Spatial heterogeneity, 62 State Council, 15 Suishou Pai online platform, 318

T Targeted Poverty Elimination, 333 The 18th Party Congress, 303

359 The Basic Department of National Development and Reform Commission, 305 The Beijing Traffic Management Department, 318 The Belt and Road Initiative, 33 The Changchun Municipal Transport Bureau, 329 The development strategy of building China’s strength in transportation, 273 The Institute of Population and Labour Economics of the Chinese Academy of Social Sciences, 63 The Ministry of Housing and Urban–Rural Development, 288 The Ministry of Human Resources and Social Security, 312 The morning peak, 292, 293, 300 The Nanjing Municipal Party Committee and Government, 313 The National Development and Reform Commission, 35, 38 The New Jiangbei & New First-line brand in Jiangbei New District, Nanjing, 313 The Paris metropolitan area, 288 The Rural Revitalisation Development Strategy, 71 The Shanghai Civilisation Office, 319 The Shanghai Municipal Transport Commission, 310, 315, 325, 329, 330 The state-owned economy and the non-state-owned economy, 70 The State Railway Administration, 149 The Third Plenary Session of the 11th Central Committee of the CPC, 67 The United States, 149 The Western Development Strategy, 33, 35 The Yangtze River Delta, 308, 311, 320, 323, 324 The Yangtze River Economic Belt, 91 Three-Year Acting Plan, 319 Tidal Lanes, 330, 348, 349 Transport equity, 11 Transport infrastructure, 149, 157, 161, 164, 165 Transport network, 1–3, 15, 16, 19 Travel demand, 3, 4, 6, 8, 14, 15, 19 Travel mode choice, 182, 186, 210 Trip chains, 270, 280

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Index

U Undergraduate, 241, 253, 256, 257 UNESCO, 129, 136 Urban agglomerations, 2, 3 Urban areas, townships and villages, 188, 194 Urumqi, 272 Utility theory, 174

W Warehousing and postal service, 55 Waterway Transport, 44–50 Women of childbearing age, 59 World Population Prospects, 62

V VIF value, 184

Z Zhongguancun Software Park, 317

Y Yangtze River, 32–34, 36, 41