Road Safety in China: Key Facts, Risk Analysis, Policy Impacts in Guangdong, Hong Kong and Macau 9811607001, 9789811607004

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Guangnan Zhang Qiaoting Zhong   Editors

Road Safety in China Key Facts, Risk Analysis, Policy Impacts in Guangdong, Hong Kong and Macau

Road Safety in China

Guangnan Zhang · Qiaoting Zhong Editors

Road Safety in China Key Facts, Risk Analysis, Policy Impacts in Guangdong, Hong Kong and Macau

Editors Guangnan Zhang Center for Studies of Hong Kong Macao and Pearl River Delta, Institute of Guangdong, Hong Kong and Macao Development Studies Sun Yat-sen University Guangzhou, China

Qiaoting Zhong Center for Studies of Hong Kong Macao and Pearl River Delta, Institute of Guangdong, Hong Kong and Macao Development Studies Sun Yat-sen University Guangzhou, China

ISBN 978-981-16-0700-4 ISBN 978-981-16-0701-1 (eBook) https://doi.org/10.1007/978-981-16-0701-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 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

Acknowledgments

This publishing of the book was supported by the National Natural Science Foundation of China (grant No. 35171573286) and Ministry of Education Project for Humanities and Social Sciences Research (16JJDGAT006).

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Contents

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Road Traffic Safety in China: Status Quo, Existing Achievements, and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guangnan Zhang, Qiaoting Zhong, Yinchang Fu, and Ying Tan

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Typical Characteristics of Road Traffic Safety Management in Guangdong, Hong Kong and Macao . . . . . . . . . . . . . . . . . . . . . . . . . . Guangnan Zhang, Ying Tan, Qiaoting Zhong, and Yinchang Fu

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Part I 3

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Economic Development and Road Traffic Safety in China: A Status Quo Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yinchang Fu, Qiaoting Zhong, and Qingxuan Yang Road Traffic Safety and Differences in Regional Economic Development: Evidence from Random Effects Model and Shapley Value Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yanyan Li and Guangnan Zhang

Part II 5

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Traffic Safety Facts in China 21

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Risk Factor Analysis: Traffic Accidents, Traffic Violations and Related Severities in Guangdong Province of China

Traffic Violations in Guangdong Province of China: Speeding and Drunk Driving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guangnan Zhang, Kelvin K. W. Yau, and Xiangpu Gong

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Risk Factors Associated with Accident Severity in China: Speeding- and Drunk Driving- Related . . . . . . . . . . . . . . . . . . . . . . . . . . Qiaoting Zhong, Qingxuan Yang, Ge Yang, and Guangnan Zhang

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Overloading Among Crash-Involved Vehicles in China: Identification of Factors Associated with Overloading and Crash Severity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guangnan Zhang, Yanyan Li, Mark J. King, and Qiaoting Zhong

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Factors Influencing Traffic Signal Violations and Related Severities by Car Drivers, Cyclists, and Pedestrians: A Case Study from Guangdong, China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Guangnan Zhang, Ying Tan, and Rong-Chang Jou

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The Moral Hazard of Compulsory Automobile Liability Insurance: An Empirical Study on Traffic Accidents in the Pearl River Delta Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Xiangpu Gong and Guangnan Zhang

Part III Road Safety Policies and Practices in China 10 Research on Road Safety Policy in the Guangdong-Hong Kong-Macao Greater Bay Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Ying Tan, Yuan Zhang, Qiaoting Zhong, Guangnan Zhang, and Yinchang Fu

Contributors

Yinchang Fu Department of Chinese Language and Literature, University of Macau, Macau, China Xiangpu Gong Center for Studies of Hong Kong, Macao and Pearl River Delta, Sun Yat-sen University, Guangzhou, China Rong-Chang Jou College of Science and Technology, National Chi Nan University, Taiwan, China Mark J. King Queensland University of Technology, Brisbane, QLD, Australia Yanyan Li Center for Studies of Hong Kong, Macao and Pearl River Delta, Sun Yat-sen University, Guangzhou, China; Department of Civil Engineering, Nagoya University, Nagoya, Japan Ying Tan Guangdong University of Finance, Guangzhou, China; Center for Studies of Hong Kong, Macao and Pearl River Delta, Institute of Guangdong, Hong Kong and Macao Development Studies, Sun Yat-sen University, Guangzhou, China Ge Yang Center for Studies of Hong Kong, Macao and Pearl River Delta, Institute of Guangdong, Hong Kong and Macao Development Studies, Sun Yat-sen University, Guangzhou, China Qingxuan Yang Center for Studies of Hong Kong, Macao and Pearl River Delta, Institute of Guangdong, Hong Kong and Macao Development Studies, Sun Yat-sen University, Guangzhou, China Kelvin K. W. Yau Department of Management Sciences, City University of Hong Kong, Hong Kong, China Guangnan Zhang Center for Studies of Hong Kong, Macao and Pearl River Delta, Institute of Guangdong, Hong Kong and Macao Development Studies, Sun Yat-sen University, Guangzhou, China

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Contributors

Yuan Zhang Center for Studies of Hong Kong, Macao and Pearl River Delta, Institute of Guangdong, Hong Kong and Macao Development Studies, Sun Yat-sen University, Guangzhou, China Qiaoting Zhong Center for Studies of Hong Kong, Macao and Pearl River Delta, Institute of Guangdong, Hong Kong and Macao Development Studies, Sun Yat-sen University, Guangzhou, China

Chapter 1

Road Traffic Safety in China: Status Quo, Existing Achievements, and Challenges Guangnan Zhang, Qiaoting Zhong, Yinchang Fu, and Ying Tan

The growing Chinese population and rapidly developing economy have led to the fast growth of the number of motor vehicles and the increasing types of road users, challenging the management of traffic safety. China has seen a continuous decline in traffic accidents and higher road traffic safety thanks to an improved system of road traffic management laws and regulations, the rapid construction of road traffic infrastructure, and the application of new technologies in public transportation. However, road traffic safety in China faces challenges. On one hand, typical traffic violations such as speeding, overloading, driving under the influence (DUI), and running a red light still exist. On the other hand, new forms of business and new technologies have complicated traffic management in China.

Road Traffic Safety in China: Status Quo High Risks of Potential Traffic Injuries Due to the Large Number of Road Users China’s improvement in economic development and urbanization has led to more road users and higher travel demand since the reform and opening up. By the end of G. Zhang · Q. Zhong (B) Center for Studies of Hong Kong, Macao and Pearl River Delta, Institute of Guangdong, Hong Kong and Macao Development Studies, Sun Yat-sen University, Guangzhou, China e-mail: [email protected] Y. Fu Department of Chinese Language and Literature, University of Macau, Macau, China Y. Tan Guangdong University of Finance, Guangzhou, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 G. Zhang and Q. Zhong (eds.), Road Safety in China, https://doi.org/10.1007/978-981-16-0701-1_1

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2019, China had a population of 1.40005 billion and a highway length of 5,012,500 kilometers; car ownership increased from 16.09 million owners in 2000 to 260 million owners in 2019, and the number of motor vehicle drivers increased from 76.56 million in 2000 to 435 million in 2019.1 Vulnerable road users such as pedestrians and motorcyclists account for a high proportion among the large number of road users, and they are exposed to higher risks of potential traffic injuries. From 2013 to 2018, traffic accidents and casualties involving motorcyclists, pedestrians, and passengers all increased. Specifically, the number of motorcycle traffic accidents increased from 40,858 in 2013 to 45,868 in 2018, and the number of casualties increased from 61,098 in 2013 to 65,734 in 2018. Traffic accidents involving pedestrians and passengers increased from 2,088 such accidents in 2013 to 3,045 such accidents in 2018, and the number of casualties increased from 2,271 in 2013 to 3,293 in 2018 (National Bureau of Statistics, PRC 2014a, 2019). Moreover, there are more riders of electric bicycles and modified vehicles on urban roads (especially in central cities) corresponding to the development of takeout and delivery industries, resulting in a rapid increase in traffic accidents. These road users pose potential risks to road traffic safety due to various reasons such as industry regulations, traffic habits, safety awareness, relevant norms, and supervision. In the first half of 2019, for example, 325 traffic accidents happened involving express delivery and take-out industries in Shanghai, resulting in five deaths and 324 injuries, with one casualty every 1.8 days on average.2

Significant Improvement in Road Traffic Safety, with Regional Differences Road traffic safety has improved significantly in China, and traffic accidents have been declining dramatically over the past 20 years. The number of crashes dropped from 616,971 in 2000 to 200,114 in 2019, and no serious traffic accidents have happened since 2012. In 2019, road traffic accidents killed 1.8 people per 10,000 vehicles, a decrease of 6.7% (National Bureau of Statistics, PRC 2020). 1 Source:

Website of National Bureau of Statistics, PRC. See https://data.stats.gov.cn/easyquery. htm?cn=C01. 2 “Shanghai Release”—Official WeChat account of the Information Office of the Shanghai Municipal People’s Government: Traffic accidents in the city’s express delivery industry in 2019H. Announced on July 6, 2019. See http://www.shanghai.gov.cn/nw2/nw2314/nw8750/nw13879/nw2 6288/u21aw1023691.html?id=F3C2F5BFF3854631.

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Regional differences are evident in the road traffic safety of China, showing that traffic accidents are concentrated in East China and tend to increase in Central China. In 2010, East China had the highest number of traffic accidents and the highest death rate; within the region, Guangdong, Zhejiang, Jiangsu, and Shandong had the highest number of traffic fatalities (Traffic Administration, Ministry of Public Security, PRC 2011). By 2017, Guangdong had 23,900 traffic accidents, ranking first in China, in sharp contrast to Tibet, which was at the bottom (329), and Shanghai, which was second to last (709). Accidents in Jiangsu, Zhejiang, and Shandong ranged from 12,000 to 13,000. Hubei and Anhui registered 11,661 and 11,506 accidents, respectively (National Bureau of Statistics, PRC 2018).

Road Traffic Safety in China: Existing Achievements Improving Laws and Regulations on Road Traffic Management China has promulgated laws and regulations, worked out plans and programs, and established a road safety committee evaluation system. In 2003, China promulgated the Law of the People’s Republic of China on Road Traffic Safety, which established China’s values, main systems, and basic principles concerning traffic safety management, marking a new stage in China’s legislation in this field. The subsequent Draft Plan of National Road Traffic Safety set a target of having fewer road traffic deaths during the “11th Five-Year Plan” period (2006–2010) than the average annual road traffic deaths during the “10th Five-Year Plan” period. It covers a wide range of issues such as road users, vehicles, road infrastructure, emergency roadside assistance, information systems, and research, and provides for the establishment of a road safety committee’s annual evaluation system. China has also increased the intensity of punishments, clarified the basis of punishment, and improved the punishment system. Traffic management authorities have increased penalties for traffic violations such as drunk driving, and the punishment system has improved from administrative punishment to civil and criminal punishment. In 2010, the Supreme People’s Court issued the Sentencing Guidelines for People’s Courts, which clarified the minimum sentencing and benchmark of

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sentencing for traffic accident crimes.3 In 2011, the Amendment VIII to the Criminal Law came into force, serving as the legal basis for the criminal punishment of dangerous driving crimes. In 2013, the relevant government authorities issued the Opinions on Several Issues Concerning the Application of Laws in Handling Criminal Cases of Drunk Driving of Motor Vehicles, which specified eight cases of heavier punishment for driving under the influence (DUI).

Rapid Progress in the Construction of Road Traffic Infrastructure China has made rapid progress in the construction of road traffic infrastructure with the continuous development of its economy and society. By 2018, China’s highway length was 4,846,500 kilometers (National Bureau of Statistics, PRC 2019). According to the goals and requirements of the “13th Five-Year Plan” of China, the construction of an efficient, diversified, and modernized integrated transportation system and a road traffic infrastructure network will continue, and the construction and improvement of road traffic infrastructure will continue to advance among urban agglomerations in order to adapt to the trend of further urbanization.

Promotion and Use of New Technologies in Public Transportation China has built an intelligent transportation system and improved intelligent transportation technology. Since the 1990s, the country has made great efforts to promote 3 The

provisions are as follows: Article 4 of the Sentencing Gidelines for People’s Courts sentencing for common crimes

I.

Traffic accident crime 1.

2.

In the event of a traffic accident crime, the minimum sentencing may be determined within a range depending on the following different circumstances: i. If a traffic accident crime causes serious injury or death to a person or causes heavy losses to public or private property, the minimum sentencing may be a fixed-term imprisonment of six months to two years. ii. In the event of hit-and-run or other especially bad circumstances, the minimum sentencing may be a fixed-term imprisonment of three to four years. iii. Where hit-and-run causes the death of one person, the minimum sentencing may be a fixed-term imprisonment of seven to eight years. On the basis of minimum sentencing, the penalty can be increased and the benchmark punishment can be determined according to the extent of responsibility, the number of people seriously injured or killed, or the amount of property loss and criminal facts such as escape affecting the constitution of a crime.

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the construction and development of an intelligent transportation system. The transportation industry and the intelligent transportation management system have been identified as a national key area and a priority subject, respectively (State Council, PRC 2006). “Intelligent transportation technologies” has been listed as a high-tech field supported by the country. The application of technologies such as intelligent traffic control, speed measurement, and high-definition road cameras has played an important role in road construction and traffic safety. China has also popularized intelligent transportation technologies and further enhanced the ability to warn people of risks. By 2018, China’s intelligent transportation management system industry had a market size of approximately 72 billion yuan, and China’s 10-million-yuan intelligent transportation projects (excluding highway informatization) had achieved a market size of approximately 20.856 billion yuan (Forward business information Co., Ltd., Shenzhen 2020). The application of new technologies such as intelligent tolling, high-definition cameras, speed measurement, and intelligent parking has greatly improved the efficiency of road use and regulation in China by enhancing the intelligence and information level of road traffic safety, improving the convenience of road traffic and consummating the early warning and control of road traffic safety risks.

Road Traffic Safety in China: Challenges Typical Traffic Violations Persist Due to some road users’ poor safety awareness and driving habits, they are typically involved in such traffic violations as speeding, overloading, DUI, and running a red light. These violations are the most prominent safety hazards in road traffic safety. i.

Speeding

Speeding is the most prominent and serious problem challenging China’s road safety. Speeding may cause extremely high risks to road traffic safety such as through rear-end collisions and rollover accidents. Moreover, faster speed corresponds to a shorter response time, worse emergency judgment, and more serious consequences. Currently, the speeding of passenger vehicles has become the main cause of road traffic accidents, especially with regard to group death and injury accidents. By 2014, more than 30% of the fatal and serious traffic accidents that killed more than 10 people each in China were caused by the speeding of passenger vehicles (National Bureau of Statistics, PRC 2014b). Speeding was the number one traffic violation investigated in 2016. In the statistics of motor vehicle traffic violations in 36 major cities across China in the same year, road traffic accidents caused by speeding accounted for 2.1% of all road traffic accidents, and the deaths caused by speeding accounted for 2.64% of the total deaths, so speeding ranked sixth among all traffic violations (Center for Research on Road Traffic Safety, Ministry of Public Security 2018).

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Overloading

As demand in China’s transportation sector grows, freight vehicles are increasingly overloaded to save money. Overloading may directly affect the service life of vehicles, change safety technical performance, damage key parts, reduce braking performance, and increase road traffic safety hazards. China implements varying levels of road protection, and road safety assurance capabilities are low in some areas. In particular, protective fences and protective piers are insufficient on road sections near cliffs and water, where it is extremely dangerous to drive overloaded vehicles. In 2013, 20% of the road traffic accidents that killed more than five people each involved overloaded trucks; 70% of the fatal and serious accidents that resulted in more than ten deaths each involved overloaded trucks (Traffic Administration, Ministry of Public Security, PRC 2014). iii.

DUI

DUI in China shows a trend of increase before a decline. Before the criminalization of drunk driving in 2011, DUI happened frequently and caused a high incidence of malignant accidents. From 2008 to 2010, 2,500 people were killed on average by traffic accidents caused by DUI in China. After relevant authorities increased the punishment for drunk driving in 2011, by 2014, road traffic accidents caused by drunk driving had decreased by 25% year-on-year and the resulting deaths had decreased by 39.3% (National Bureau of Statistics, PRC 2014b). Despite the increasing penalties, DUI has not been totally eliminated. In the first half of 2019 alone, China dealt with 901,000 cases of DUI, of which 177,000 were drunk driving; 1,525 traffic accidents caused by DUI and drunk driving resulted in 1,674 deaths; and DUI caused 7,512 non-fatal traffic accidents, a year-on-year increase of 28.2%. Therefore, the situation remains grim.4 Given the vehicle type, DUI is prominent in passenger cars and motorcycles, and is increasing in vehicles in relation to online car-hailing and designated driving. Given the occurrence time, DUI more frequently occurs during the night and early morning hours. Given the regional distribution, DUI cases investigated on urban roads are 54% more frequent, and DUI is still serious in rural areas.5 iv.

Running a red light

Running a red light violates the road traffic rules and the rules for allocation of right of way and increases traffic conflicts at intersections and the probability of road traffic accidents. It may lead to road traffic disorder and threaten life safety. Although China’s Road Safety Law explicitly prohibits the running of red lights, compliance by road users is not satisfactory. Pedestrians and non-motor vehicles often run red lights, as evidenced by a typical act known as “Chinese-style road crossing.”6 Between 2013 4 China

News: A total of 901,000 cases of DUI are investigated nationwide in 2019H1, July 23, 2019. See http://www.chinanews.com/gn/2019/07-23/8904725.shtml. 5 China News: A total of 901,000 cases of DUI are investigated nationwide in 2019H1, July 23, 2019. See http://www.chinanews.com/gn/2019/07-23/8904725.shtml. 6 “Chinese-style road crossing” is a joke made by Internet users about the phenomenon of some Chinese people running red lights in a group. In other words, “when you get enough people together,

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and 2014, running red lights in China resulted in 2,951 road traffic accidents, causing 600 deaths and 3,675 injuries (National Bureau of Statistics, PRC 2014b).

New Forms of Business and New Technologies Present New Requirements for Traffic Safety Management Amid the rapid social and economic development in China, industries such as car sharing and on-demand delivery have sprung up and greatly changed people’s lives. Technological progress has brought about new ways of driving such as intelligent driving and autonomous driving. These new forms and technologies are continuously influencing the development of road traffic safety and bringing new challenges to traffic management. i.

On-demand delivery and express delivery

Since the “Internet Plus” Action Plan was launched, the space of the Internet economy has expanded rapidly. Its integration with the traditional catering industry has given birth to on-demand delivery based on online to offline (O2O) processes, which has changed people’s lives and eating patterns. This has been accompanied by a rapid increase in motorcyclists and electric bicycle riders on the Chinese roads. More than four million people have registered on Eleme, Meituan, and Baidu, which are known as three major on-demand delivery platforms, while other crowdsourcing and logistics platforms employ more than three million part-time delivery men and women. Therefore, there are more than seven million “delivery men and women.”7 A total of 117 road traffic accidents involving express delivery and on-demand delivery occurred in Shanghai in 2017, resulting in nine deaths and 134 injuries, according to data released by the traffic police department of Shanghai Public Security. Traffic violations mainly included speeding, running red lights, and riding on the wrong side of the road.8 On-demand delivery and express delivery industries are subject to the following hidden dangers in relation to road traffic safety. First, on-demand delivery and express delivery mainly employ motorcycles and electric bicycles, and motorcyclists and electric bicycle riders are vulnerable road users who are exposed to higher risks of potential traffic injuries. Second, given the nature of delivery work, on-demand delivery and express delivery men and women usually travel in the morning, afternoon, and evening rush hours and make frequent trips to and from the congested you can go. It has nothing to do with the traffic lights.” On October 14, 2012, this phenomenon became widely known after the CCTV Live News program broadcast “Chinese-style road crossing: 600 people run red lights in an hour at an intersection.” 7 Xinhua News: How does the on-demand delivery economy affect our life? December 14, 2017. See http://www.xinhuanet.com/fortune/2017-12/14/c_1122113096.htm. 8 Shanghai Observer: Shanghai Traffic Police releases 117 traffic accidents involving express delivery and on-demand delivery in 2017: Nine people died, and Eleme has the highest incidence of traffic accidents, February 9, 2018. See https://www.shobserver.com/news/detail?id=79593.

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roads in downtown areas, resulting in a high risk of traffic accidents. Third, time is of the essence in both on-demand delivery and express delivery. Delivery men and women often commit violations such as speeding, running red lights, and riding while fatigued. Currently, Chinese cities have varying regulations and standards for motorcycles and electric bicycles on urban roads as well as different controls on modified vehicles on the roads. The traffic safety management requirements and systems for on-demand delivery and express delivery need further improvement. ii.

Travel sharing and autonomous driving

Thanks to the popular concept of travel sharing, bicycle sharing and car sharing are rapidly emerging in China. These new travel modes facilitate the use of vehicles but also pose new challenges to traffic safety management. China’s car-sharing industry is still in its infancy and lacks specific laws at the national level. Local governments now implement different requirements and standards. The legal regulatory environment for car sharing needs to be improved. In the field of road traffic safety, there are no sound and clear management rules and handling bases for car-sharing–related traffic accidents in terms of the responsible subject, extent of discretion, and compensation standard. Speeding up the construction of a management method and management system for new ways of using roads is a priority with the development of car sharing. The continuous development of big data and artificial intelligence enables autonomous driving to penetrate people’s lives along with the enormous “blue sea” behind it. Shanghai has piloted autonomous taxis that are equipped with human security officers, while Guangzhou launched road testing of intelligently networked autonomous cars in 2020.9 China now lacks legislation on autonomous driving. At present, only two trial documents have been released by the Beijing Municipal Commission of Transport together with the Beijing Traffic Management Bureau and the Beijing Municipal Bureau of Economy and Information Technology.10 Future rules for autonomous driving should consider the rationality of autonomous vehicles, explore whether they can become road users and road traffic “drivers”, clarify the regulatory authorities and responsibilities, and stipulate how to assume responsibility for accidents.

9 Guangzhou

Municipal Transportation Bureau, Guangzhou Municipal Industry and Information Technology Bureau, and Guangzhou Municipal Public Security Bureau: Notice on the Issuance of Guidance on the Road Testing of Intelligently Networked Autonomous Cars (Sui Jiao Yun Gui [2020] No. 4), January 14, 2020. 10 Guidelines of Beijing Municipality on Accelerating Road Testing of Autonomous Vehicles (Trial), and Implementation Rules of Beijing Municipality for Road Testing Management of Autonomous Vehicles (Trial).

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References Center for Research on Road Traffic Safety, Ministry of Public Security (2018) Research report of Metropolis’ road traffic development in China (IV), China Architecture & Building Press Forward business information Co., Ltd., Shenzhen (2020) Report of market prospective and investment strategy planning on China Intelligent Transportation Industry (2020–2025) National Bureau of Statistics, PRC (2014a) China Statistical Year Book 2014, China Statistics Press National Bureau of Statistics, PRC (2014b) Seven types of traffic violations are serious, and there is a long way to go for safe and civilized travel. China Police Daily-Traffic Safety Weekly, 2014-12-02(03) National Bureau of Statistics, PRC (2018) China Statistical Year Book 2018, China Statistics Press National Bureau of Statistics, PRC (2019) China Statistical Year Book 2019, China Statistics Press National Bureau of Statistics, PRC (2020) Statistical Communique of the People’s Republic of China on the 2019 National Economic and Social Development, China Statistics Press State Council, PRC (2006) National medium-and-long-term program for scientific and technological development (2006–2020) Traffic Administration, Ministry of Public Security, PRC (2011) Annual report on road traffic accidents of the People’s Republic of China 2010 Traffic Administration, Ministry of Public Security, PRC (2014) Annual report on road traffic accidents of the People’s Republic of China 2013

Chapter 2

Typical Characteristics of Road Traffic Safety Management in Guangdong, Hong Kong and Macao Guangnan Zhang, Ying Tan, Qiaoting Zhong, and Yinchang Fu

Abstract There is a large amount of cross-border transportation in Guangdong, Hong Kong and Macao regions. Statistics show that cross-boundary traffic in Macao was 4,927,368 vehicles in 2018, while Hong Kong’s total number of cross-boundary vehicles reached 15,870,839 (roundtrip) in 2019. After the Hong Kong-ZhuhaiMacao Bridge was completed and opened to traffic, land routes became a new option for transportation between Hong Kong and Macao, reducing the reliance on ferry terminals. In the context of “one country, two systems, and three customs territories,” the traffic safety laws, driving rules, and road traffic management standards and systems vary among Guangdong, Hong Kong, and Macao. With the continuous advancement of the construction of the Guangdong-Hong Kong-Macao Greater Bay Area and the sustained development of the regional economy, the degree of interconnection among the three regions has continued to increase, and the process of removing obstacles in the flow of elements has continued to deepen. Road transport management cooperation between Guangdong, Hong Kong, and Macao has become increasingly important under this situation.

Comparison of the Legal System of Traffic Safety in the Guangdong, Hong Kong and Macao Regions Guangdong, Hong Kong and Macao regions are home to three separate legal systems. In terms of traffic safety legal systems, there are differences in the legislative bodies, G. Zhang · Q. Zhong (B) Center for Studies of Hong Kong, Macao and Pearl River Delta, Institute of Guangdong, Hong Kong and Macao Development Studies, Sun Yat-sen University, Guangzhou, China e-mail: [email protected] Y. Tan Guangdong University of Finance, Guangzhou, China Y. Fu Department of Chinese Language and Literature, University of Macau, Macau, China

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 G. Zhang and Q. Zhong (eds.), Road Safety in China, https://doi.org/10.1007/978-981-16-0701-1_2

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law enforcement agencies and legislative provisions of the traffic safety laws among Guangdong, Hong Kong and Macao. In terms of cross-border traffic, specific laws and regulations related to cross-border traffic safety have also been formulated for management.

The Social Institutions, Legal System, and Traffic Safety Laws and Regulations of Guangdong Province Guangdong Province is under the continental legal system of socialism with Chinese characteristics.1 The highest level of the legal basis for road traffic safety in Guangdong Province is the Law of the People’s Republic of China on Road Traffic Safety (hereinafter referred to as the Law of Road Traffic Safety). The legislative power (legislative body) rests with (is) the National People’s Congress, which is based on the Law of the People’s Republic of China on Road Traffic Safety, the People’s Republic of China Regulations on Implementation of The Law on Road Traffic Safety, and related laws and regulations. Combined with its realistic environment, the province formulated the Guangdong Province Regulations on Implementation of The Law on Road Traffic Safety (hereinafter referred to as Implementation of The Law on Road Traffic Safety), which came into effect on May 1, 2006. In addition to the National Law of Road Traffic Safety and Guangdong’s Implementation of The Law on Road Traffic Safety, the specific provisions are based on administrative regulations like road traffic safety management and administrative penalty regulations of the cities in the province. The legislative bodies are the local people’s congresses and their standing committees, and the enforcement powers (implementing subjects) rest with the public security organs.2

The Social Institutions, Legal System, and Traffic Safety Laws and Regulations of Hong Kong Since the handover of Hong Kong, the capitalist economic system in the region has been maintained under the principles and guidelines of “One Country, Two Systems.” The Constitution of the People’s Republic of China and The Basic Law of the Hong Kong Special Administrative Region together constitute the constitutional foundation 1 The

State Council Information Office, People’s Republic of China: The Socialist System of Laws with Chinese Characteristics, 2011-10-27. https://www.scio.gov.cn/zfbps/ndhf/2011/Document/ 1034943/1034943.htm. 2 “Law of Road Traffic Safety:” “The department of public security under the State Council shall be in charge of the administrative work for road traffic safety nationwide. The traffic control department of the public security organs under the local people’s governments at or above the county level shall be in charge of the administrative work for road traffic safety within their respective administrative areas.”

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of the Hong Kong Special Administrative Region. According to the Basic Law, Hong Kong is granted independent legislative power and the power of final adjudication3 ; thus, the legal system is based on common law. In Hong Kong, the legal basis for road safety includes various ordinances and subsidiary legislations such as Road Traffic Ordinance, Road Traffic (Driving-offense Points) Ordinance, Fixed Penalty (Traffic Contraventions) Ordinance, Road Traffic (Validation of Collection of Fees) Ordinance, Traffic Accident Victims (Assistance Fund) Ordinance. The enforcement power rests with the Traffic Branch Headquarters of the Hong Kong Police Force, as well as the five regional traffic districts. Responsibilities like promotion, publicity, and preventive education lie with the Hong Kong Road Safety Patrol.

The Social Institutions, Legal System, and Traffic Safety Laws and Regulations of Macao Since the handover of Macao, the capitalist economic system in the region has been maintained under the principles and guidelines of “One Country, Two Systems.” A high degree of autonomy has been implemented based on The Constitution of the People’s Republic of China and The Basic Law of the Macao Special Administrative Region, where the legal system is based on the continental legal system. The Road Traffic Law implemented in 2007 replaced the Road Code implemented in 1993 and is the prevailing law for road traffic safety in Macao. In January 2019, Macao began to plan to make amendments to the Road Traffic Law and conducted public consultations to ensure the involvement of society at large. Entities with authorities over road traffic include the Superior Council of Traffic, Land, Public Works, and Transport Bureau, Public Security Police Force, Civil and Municipal Affairs Bureau, and Customs Service.4

Relevant Laws and Regulations for Cross-Boundary Traffic in the Guangdong, Hong Kong and Macao Regions The laws and regulations related to vehicles traveling between the administrative regions of Guangdong, Hong Kong, and Macao carry obvious administrative regionalities. Guangdong Province follows national regulations and manages cross-boundary vehicles based on the Provisions on the Administration of Motor Vehicles and Drivers of Temporary Entry issued by the Ministry of Public Security. Specifically, for crossboundary vehicles traveling to and from Hong Kong and Macao, the management is based on the following: Temporary Entry Registration for Motor Vehicles from 3 See 4 See

details in Article 8 of the Basic Law of the Hong Kong Special Administrative Region. details in Article 5 of the Road Traffic Law of the Macao Special Administrative Region.

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Hong Kong, Macao, and Taiwan regions issued by the Guangdong Provincial Public Security Department; Administrative Measures for the Direct Travel of Transport Vehicles directly connecting to Hong Kong/Macao issued by the People’s Government of Guangdong Province; and Measures of the Customs of the People’s Republic of China for the Administration of Enterprises of Highway Freight between the Inland and Hong Kong/Macao and Their Vehicles and Drivers issued by the General Administration of Customs of the People’s Republic of China (Mei and Gao 2017). Macao’s regulations on cross-boundary vehicles are based on the Regulation of Cross-Border Road Passenger Transportation and Administrative Regulation No. 3/2006 Control and Inspection of Vehicles Entering and Leaving the Land Borders of the Macao Special Administrative Region.5 These regulations stipulate that any vehicles that cross into Macao’s area should apply for a permit or RFID and enter under the supervision of the Customs Service. To cope with the opening of the Hong Kong-Zhuhai-Macao Bridge, Administrative Regulation No. 14/2018 Amendment of Administrative Regulation No. 3/2006 ‘Control and Inspection of Vehicles Entering and Leaving the Land Borders of the Macao Special Administrative Region’ came into effect in August 2018. The Macao Customs Service adjusted its services on issuing permits for vehicles, and the targets include vehicles traveling between the Macao Special Administrative Region and the other regions of the People’s Republic of China.6 Hong Kong’s management regarding cross-border road traffic safety is extremely strict. All vehicles running on the road in Hong Kong and all road users must strictly abide by Hong Kong’s local road traffic safety laws, as well as the Road Users’ Code and the Safe Motoring Guides issued by the Transport Department.

Comparison of Traffic Safety Management in the Guangdong, Hong Kong and Macao Regions The typical characteristics of traffic safety management in Guangdong, Hong Kong and Macao regions are mainly reflected in the obvious differences in vehicle types managements, driving licenses, scoring systems and fine management.

5 Administrative

Regulation No. 3/2006 of Macao Special Administrative Region: Regulation of Cross-Border Road Passenger Transportation, https://bo.io.gov.mo/bo/i/2006/08/regadm03_cn.asp. 6 Administrative Regulation No. 14/2018 of Macao Special Administrative Region: Amendment of Administrative Regulation No. 3/2006 “Control and Inspection of Vehicles Entering and Leaving the Land Borders of the Macao Special Administrative Region,” https://bo.io.gov.mo/bo/i/2018/31/ regadm14_cn.asp.

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Management of Vehicle Types Traffic safety management in Guangdong, Hong Kong, and Macao regions show the biggest differences regarding the management of motorcycles.7 Driving regulations and the use of helmets are detailed in separate sections and provisions in Macao’s Road Traffic Law.8 The law also explicitly stipulates the regulations regarding vehicle examinations at particular year intervals according to the type of the motorcycle.9 In most of the city in the Pearl River Delta region within the Guangdong Province, the driving of motorcycles is banned or limited. The laws and regulations on road safety management of cities such as Guangzhou, Shenzhen, Dongguan, Foshan, Zhuhai, Zhongshan, Shantou, and Huizhou explicitly stipulate that the registration of motorcycle licenses is banned or limited (in the main zones of the cities). The number of motorcycle license applications out of the main city zones is also strictly limited.10 Some cities in the Pearl River Delta region within the Guangdong Province continuously expand the restricted areas for motorcycles, except for the use of special vehicles such as police motorcycles. However, both the Road Traffic Ordinance and Safe Motoring Guides of Hong Kong do not consider motorcycles to be specific items or produce separate chapters for the regulations of motorcycles.

Management of Driving Licenses Guangdong, Hong Kong, and Macao practice different regulations in training requirements, examination standards, issuing institutions, effective license period for firsttime applicants, and renewal/replacement. The three regions do not automatically recognize each other’s driving licenses. In training requirements, examination standards, and issuing institutions, the training, examination, and application of motor vehicle driving licenses in Guangdong Province are implemented according to Order No. 123 of the Ministry of

7 Note: “Electric bike” is a common name for motorcycles in Hong Kong and Macao. According to

Article 2, Part 1 of the Hong Kong’s “Road Traffic Ordinance,” motorcycle (electric bike) refers to a two-wheeled motor vehicle with or without a sidecar. Therefore, there is an essential difference between “electric bicycles” in Hong Kong and electric vehicles or electric bicycles in Mainland China. 8 See details in Section 13 of the “Road Traffic Law” of the Macao Special Administrative Region: Special Regulations on heavy motorcycles, light motorcycles, and bicycles. 9 See details in Article 75 of the Road Traffic Law of the Macao Special Administrative Region: Examination. 10 For details, see “Road Traffic Management Regulations of Guangdong Province,” “Road Traffic Management Regulations of Shenzhen Special economic zone,” “Road Traffic Management Regulations of Zhuhai Special economic zone,” and “Road Traffic Management Regulations of Shantou Special economic zone.”

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Public Security Provisions on the Application for and Use of Driving Licenses.11 The licenses are issued by the local public security traffic control departments. In Hong Kong, the relevant regulations on driving licenses are administered under Article 8, Part 2 of the Road Traffic Ordinance,12 while the licenses are issued by the Transportation Department of Hong Kong. Driving licenses in Macao are issued by the Transport Bureau of Macao under Article 81 of the Road Traffic Law. Regarding license application, renewal, and mutual recognition, taking vehicle driving licenses as an example, the validity period of the first light vehicle driving license application was 6 years in Guangdong Province, 10 years in Hong Kong, and 15 years in Macao. Guangdong, Hong Kong, and Macao do not automatically recognize each other’s driving licenses yet. Residents of Hong Kong and Macao who hold a local motor vehicle driving license must first meet requirements and take and pass a unified examination before they can apply for a Mainland Chinese motor vehicle driving license for the same type of vehicle. Residents of Guangdong Province with a driving license in Mainland China must obtain permission to stay in Hong Kong and Macao before they can register for driving classes and training courses in these areas, for the examinations and applications for Hong Kong/Macao driving licenses afterward.

Scoring System and Penalty Management Guangdong Province and Hong Kong currently implement a scoring system, while Macao does not. Note that Macao’s public consultation since January 2019 included the discussion on the introduction of a “point-deduction system.” Based on the results of the consultation, the authorities of Macao decided to consider the scoring system at the legislation level.13 Penalties for traffic violations in Guangdong Province belong to the Country according to the Law of Road Traffic Safety.14 Road Traffic Ordinance defines penalties for traffic violations as “debts owed to the government” in Hong Kong. Macao’s Road Traffic Law stipulates that penalties for offenses shall be the income of the 11 The latest amendment to this provision entered into force in 2016 as Order No. 139 of the Ministry of Public Security “Decisions of Amendments on the Provisions on the Application for and Use of Driving Licenses by the Ministry of Public Security.” 12 See details in Article 8, Part 2 of the “Road Traffic Ordinance” of Hong Kong Special Administrative Region: Regulation of driving licenses. 13 See details in “Amendment to Law No. 3/2007 ‘Road Traffic Law’ and its supplementary regulations” and the summary report of “Amendment to Law No. 3/2007 ‘Road Traffic Law’ and its supplementary regulations.” 14 Article 82 of the “Law of Road Traffic Safety”: When the traffic control department of the public security organ imposes fines as an administrative punishment, it shall, in accordance with the provisions of relevant laws and administrative regulations, separate the decision on fines from the collection of fines; and the fines collected and unlawful gains confiscated according to law shall be turned over to the State Treasury in full.

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Macao Special Administrative Region, while the penalties for offenses related to vehicle examination, driving courses, and driving test shall belong to the Civil and Municipal Affairs Bureau.15 It is also stipulated that if the offender pays the penalty within a specified time limit, the amount can be reduced to two-thirds of the total penalty.16

Reference Mei F, Gao F (2017) Research on the collaborative environmental management of motor vehicles from the perspective of regional integration: a case study of motor vehicles across administrative regions in Guangdong, Hong Kong and Macao. Modern Manag Sci 3:94–96

15 See details in Article 142 of the “Road Traffic Law” of the Macao Special Administrative Region: Attribution of fines. 16 See details in Article 137 of the “Road Traffic Law” of the Macao Special Administrative Region: Voluntary payment.

Part I

Traffic Safety Facts in China

Chapter 3

Economic Development and Road Traffic Safety in China: A Status Quo Analysis Yinchang Fu, Qiaoting Zhong, and Qingxuan Yang

Abstract Economic development is an important factor affecting road traffic. The rapid development of China’s economy has driven vehicle population growth and increased investment in road infrastructure. As a result, the number of civilian vehicles, road length, and drivers in China have all soared. In the overall context of economic development, road traffic safety, vehicle population, and road length growth in China are characterized by significant regional differences. Keywords Economic growth · Vehicle population · Road length · Regional differences

Economic Growth and Changes in Vehicle Population The sustained and rapid development of China’s economy has greatly improved urban residents’ living standard. With rising disposable income, consumer spending keeps increasing, and ownership of civilian vehicles is increasing yearly (see Fig. 3.1). According to the National Bureau of Statistics, PRC (2019), by 2019, China had 220 million small passenger vehicles, an increase of 19.26 million or 9.37% compared with the end of 2018; the country also had 26.94 million goods vehicles, accounting for 10.71% of the total vehicle population.1 It is worth noting that from 2005 to 2019, China’s vehicle population and per capita gross domestic product (GDP) exhibited a trend of common growth.

1 The data are from the China Statistical Yearbook 2019 compiled by the National Bureau of Statistics, PRC. Unless otherwise specified, the data used in this paper are from the China Statistical Yearbook 2019.

Y. Fu Department of Chinese Language and Literature, University of Macau, Macau, China Q. Zhong (B) · Q. Yang Center for Studies of Hong Kong, Macao and Pearl River Delta, Institute of Guangdong, Hong Kong and Macao Development Studies, Sun Yat-Sen University, Guangzhou, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 G. Zhang and Q. Zhong (eds.), Road Safety in China, https://doi.org/10.1007/978-981-16-0701-1_3

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Fig. 3.1 China’s per capita GDP and vehicle population by year

In terms of the number of newly registered vehicles, in China in 2019, 80% were civilian vehicles, and the biggest increase was recorded in the number of newly registered goods vehicles. By the first half of 2019, 1.75 million new goods vehicles were registered, an increase of 30,000 compared with the same period in 2018, and the number of new registrations reached a record high. In terms of vehicle types, China had 207 million private cars in 2019, with an average annual increase of 19.66 million vehicles over the past five years; the increase in new-energy vehicles exceeded one million for two consecutive years, showing a rapid growth trend. By 2019, China had 3.81 million new-energy vehicles, accounting for 1.4609% of the total vehicle population, an increase of 1.2 million or 46.05% compared with the end of 2018. Specifically, the total number of pure electric vehicles was 3.1 million, accounting for 81.19% of the total number of new-energy vehicles.

Economic Growth and Changes in Road Length Economic development has led to increasing investment in road infrastructure and growth in the length of highways in China (see Fig. 3.2). In 2019, China’s total highway length increased by 1.5 times, from 3.3452 million kilometers in 2005 to 5.0125 million kilometers. To be specific, the length of grade highways increased from 1.5918 million kilometers in 2005 to 4.4659 million kilometers by the end of 2018, while the length of expressways increased from 41,000 km in 2005 to 149,600 km in 2019.

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Fig. 3.2 Annual growth of the length of highways in China

Regional Characteristics of Vehicle Population and Road Length Growth The development of the regional economy is an important factor affecting residents’ consumption level. The most economically developed provinces in China rank at the top in terms of vehicle population (see Table 3.1). In 2019, China had 340 million vehicles; Guangdong, Shandong, Henan, and Jiangsu had more than 20 million vehicles, respectively; and Guangdong, Jiangsu, and Shandong were the top three provinces in terms of GDP, while Henan ranked fifth. In addition to the above four provinces, another ten provinces also had more than ten million vehicles, respectively, in 2019. Given the regional distribution, these provinces are concentrated in the eastern and central regions of China.2 Given the urban distribution, 66 cities had more than one million vehicles, respectively, in 2019 and are mainly distributed in the eastern and central provinces; 11 of them had more than three million vehicles, respectively, and are all located in the eastern and central regions3 ; both Beijing and Chengdu had more than five million vehicles. Regional economic development has steadily popularized new means of transportation, and the number of new-energy vehicles and shared vehicles continues to rise. With economic development, population expansion, balanced development, and increasing demand for environmental protection, economically developed regions have introduced license plate restrictions and encouraged the development of the new-energy automobile industry. In 2018, the top three provinces in terms of newenergy vehicle sales (sales volume and share of total sales) were Guangdong (88,820 2 They

were Hebei, Zhejiang, Yunnan, Hunan, Anhui, Guangxi, Hubei, Fujian, and Liaoning. were Beijing, Chengdu, Chongqing, Suzhou, Shanghai, Zhengzhou, Shenzhen, Xi’an, Wuhan, Dongguan, and Tianjin.

3 They

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Table 3.1 Vehicle population and regional economic development in 2019 Province

Per capita GDP (CNY)

National ranking in per capita GDP in 2019

Number of motor vehicles in 2019 (10,000 vehicles)

National ranking in the number of motor vehicles in 2019

Percentage of total motor vehicles nationwide (%)

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50,501.1

1

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9.05

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vehicles, 25.13%), Zhejiang (48,381 vehicles, 13.69%), and Shandong (44,173 vehicles, 12.50%), which were among the top five provinces in terms of economic development. Car sharing is a new mode of transportation spawned by social development, with time-sharing leasing as its biggest feature. It is popular in first- and second-tier cities. In 2019, second-tier cities accounted for 45.2% of the car sharing market, while first-tier cities accounted for 21.5% (Intelligence Research Group 2020). Regional economic development also determines the amount of local investment in road infrastructure construction, thus affecting the road construction situation in various regions. Economically developed regions see a rapid increase in road length because of a good foundation of road traffic, rapid development, and sufficient investment. By 2018, Guangdong, as the largest economic province, had maintained the first place in the country in terms of expressway length since 2014; the total highway length of Guangdong was 217,700 km. The gap between other provinces and Guangdong was widening. Moreover, China offers more assistance and support to less developed regions in terms of investment, especially in infrastructure construction. For example, Yunnan, Guizhou, and Sichuan have been increasing investment in transportation construction in recent years. In 2018, Yunnan invested CNY 219.6 billion in transportation infrastructure. In 2019, Guizhou completed an investment of CNY 120 billion, ensuring that its total investment exceeded 100 billion yuan for six years in a row.4

Regional Differences in Road Traffic Accidents In recent years, the total number of traffic accidents and the total number of casualties in China have been declining. However, road traffic safety varies greatly across provinces due to different levels of economic development. Data show that road traffic 4 Transport

Conference of Yunnan Province in 2019: Work Plan 2019 of the Department of Transport of Yunnan Province, see https://www.ynjtt.com/Item/249250.aspx; Department of Transport of Guizhou Province: Poverty Alleviation Conference 2019 held by the Department of Transport of Guizhou Province, see https://zizhan.mot.gov.cn/st/guizhou/jiaotongxinwen/201901/t20190 111_3156250.html.

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accidents occur more frequently in some regions. The provinces with the highest traffic accident incidence and the highest number of deaths and the provinces with the lowest traffic accident incidence and the lowest number of deaths have significant regional characteristics. Specifically, Guangdong, Jiangsu, and Zhejiang had the highest number of road traffic accidents and deaths in 2016, with 18,337 accidents and 9,071 deaths, accounting for 35.8% and 28.8% of the national total, respectively. All three provinces are concentrated in the southeastern coastal region. Qinghai, Ningxia, and Tibet had the lowest number of road traffic accidents and deaths, with 753 accidents and 418 deaths, accounting for 1.5% and 1.3% of the national total, respectively. These three provinces are concentrated in the northwestern inland region. The traffic accident incidence of the three eastern provinces was 24 times higher than that of the three western provinces; the number of people killed in these traffic accidents in the three eastern provinces was 22 times higher than that in the three western provinces (Traffic Administration, Ministry of Public Security, PRC 2017). The increase in the number of vehicles and road length has enabled closer and more frequent regional exchanges, brought more convenience to people’s life, and made great contributions to the economic development of all regions. Furthermore, it has led to the rapid expansion of road users and drivers, especially novice drivers. By 2019, China had 435 million motor vehicle drivers, of whom 397 million or 91.26% were civilian vehicle drivers. The huge vehicle population and the increasing road length have negatively impacted China’s road traffic safety, resulting in more traffic accidents and casualties, and threatening the safety of people’s lives and property. Figure 3.3 shows road traffic safety conditions in the top three (Guangdong, Shandong, and Jiangsu) and the bottom three (Qinghai, Hainan, and Gansu) provinces in terms of vehicle population in 2018, reflecting a correlation between vehicle population and regional road traffic safety. 30000

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Fig. 3.3 Road traffic safety in the top three and the bottom three provinces in terms of vehicle population in 2018

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References Intelligence Research Group (2020) Survey on the development status of China’s car-sharing industry and development strategy consulting report from 2020 to 2026 National Bureau of Statistics, PRC (2019) China statistical yearbook 2019. China Statistics Press Traffic Administration, Ministry of Public Security, PRC (2017) Statistical annual report on road traffic accidents in the People’s Republic of China (2016)

Chapter 4

Road Traffic Safety and Differences in Regional Economic Development: Evidence from Random Effects Model and Shapley Value Decomposition Yanyan Li and Guangnan Zhang Abstract To investigate the relationship between differences in regional economic development and road traffic safety, this paper used data on traffic accidents in China for the 2007–2010 period in conjunction with a random effects (RE) model and Shapley value decomposition method to analyze the mechanisms by which gross domestic product (GDP) per capita, population, and vehicle- and road-related factors affect the number of traffic accidents, number of injured persons, and number of fatalities and examined the relative contributions of these factors and their dynamic trends. This paper discovered that GDP per capita and road mileage have a positive influence on the reduction in traffic accidents, while vehicle and population factors tend to increase the probability of traffic accidents; in Eastern China, GDP per capita had a negative influence on the number of persons killed or injured in traffic accidents; in Western China, GDP per capita had no significant influence on the reduction in deaths and injuries resulting from accidents. The Shapley value decomposition results revealed that vehicle- and road-related factors are the chief factors affecting number of traffic accidents and number of persons injured and that GDP per capita has a major effect on the number of deaths in road accidents; notably, this effect increases with time. Accordingly, in economically well-developed Eastern China, the establishment of an intangible infrastructure consisting of laws and regulations, medical services, and driver’s education, etc. has been very effective in improving road traffic safety. In the case of less-developed Western China, extensive infrastructure development and improved motor vehicle safety have the largest positive impact on traffic safety. Keywords Economic development · Traffic accident · Shapley value decomposition · Regional differences

Y. Li Center for Studies of Hong Kong, Macao and Pearl River Delta, Sun Yat-sen University, Guangzhou, China G. Zhang (B) Center for Studies of Hong Kong, Macao and Pearl River Delta, Institute of Guangdong, Hong Kong and Macao Development Studies, Sun Yat-sen University, Guangzhou, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 G. Zhang and Q. Zhong (eds.), Road Safety in China, https://doi.org/10.1007/978-981-16-0701-1_4

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Introduction Traffic accidents cause vast economic, personal, and social losses, and the caused losses are especially severe in developing nations. According to a World Health Organization (WHO) (2013a) report, owing to factors such as a common absence of adequate laws and policies, road traffic facilities unproportional to the growing number of vehicles, poor public transportation safety and a lack of orderliness, losses attributable to traffic accidents in developing countries are significantly higher than those in developed countries. According to statistics, the death rate per 100,000 persons due to traffic accidents is 20.1% in middle-income countries, 18.3% in lowincome countries, and only 8.7% in high-income countries. Middle-income countries contain only 52% of all registered vehicles worldwide, but they account for 80% of the world’s fatalities due to traffic accidents (WHO 2013a). With economic development and a growing number of motor vehicles, China has become the country with the greatest number of traffic fatalities. According to statistics from the Ministry of Public Security of the People’s Republic of China (PRC), the 204,196 traffic accidents in China in 2012 caused 59,997 deaths, 224,327 injuries, and direct economic losses exceeding RMB 1.1 billion (Traffic Management Bureau, Ministry of Public Security, PRC 2013). This implies that one traffic accident takes place in China every 3 min and that traffic accidents cause approximately 160 deaths per day. Addressing this situation, China’s State Council has proposed the explicit goal of reducing the traffic accident death rate by 36% during the period of the Twelfth Five-Year Plan and has formally implemented the United Nations’ Decade of Action for Road Safety 2011–2020 in China. To reduce losses from traffic accidents, a steadily increasing body of research has addressed the factors and mechanisms affecting traffic safety and has proposed policy recommendations. Macro-level research has found that the level of economic development could affect medical care standards, motor vehicle safety features, road infrastructure, and traffic laws and policies, as well as the effectiveness of their implementation, which could in turn influence traffic safety. In addition, differences in culture and cost of living among countries or regions at different stages of economic development could also affect traffic safety. However, due to the available data, most current researches have focused on developed countries, while devoting insufficient attention to low- and middle-income countries, and there has been a particular lack of research on traffic accidents in China. Because China is a large country with vast differences in regional economic development, factors affecting traffic safety and their effects may also differ from region to region. This paper therefore provides an analysis of the factors affecting road traffic safety in regions of China with different economic development levels to offer reference information that can help these regions reduce their traffic accidents, fatalities, and injuries, and that can assist other developing nations understand and prevent traffic accidents.

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Literature Review Studies have discovered that a linear inflection point, piecewise linear, or inverted Ushaped relationship may exist between per capita income level and traffic deaths and injuries. The number of traffic accidents does not increase steadily with economic development but tends to decrease after reaching an inflection point; previous researches have suggested that the inflection point for an increase in traffic accidents is at a gross domestic product (GDP) per capita of USD 9,600 (Kopits and Cropper 2005), USD 1,500–8,000 (Bishai et al. 2006), or USD 11,454 (Anbarci et al. 2009). After performing regression analysis of data from 44 countries, Paulozzi et al. (2007) found that the death rate due to traffic accidents tends to peak in low- and middleincome countries when per capita income reaches USD 2,000 and the motor vehicle ownership rate reaches 100 per 1,000 persons, but the overall traffic accident rate decreases when per capita income reaches USD 24,000. In their analysis of panel data for 88 countries during the 1963–1999 period, Kopits and Cropper (2005) found that a piecewise linear relationship existed between the traffic accident death rate and real GDP per capita. In addition, an inverted U-shaped relationship may exist between economic development level and number of traffic accidents (Kopits and Cropper 2005; Anbarci et al. 2006; Bishai et al. 2006). However, the fact that early studies assumed that all countries’ traffic accident growth trajectories and inflection points were identical may have resulted in deviations in their findings. As a consequence, some scholars have sought to improve their research through the use of fixed effect (FE) negative binomial regression with panel data (Law et al. 2009) or semiparametric local linear models with the consideration of potential nonlinear relationships (Iwata 2010), resulting in an inverted U-shaped relationship. Furthermore, common cyclical behavior may exist in both the real economy and traffic accidents. García-Ferrer et al. (2007) discovered that traffic accidents and real economic activity shared common cycles, possibly because of the long-term existence of common constituent variables for both, where those factors that do not follow cyclical laws have leading or lagging relationships with economic cycles. In China, Zhou et al. (2006) found that Smeed’s Law is applicable to China. The number of traffic accidents in China is currently still rising, and China’s road traffic safety could remain severe for the next few years. In their analysis of the characteristics of traffic accidents in China, Liu et al. (2006) found that rapid economic development, increasing passenger and cargo transport volume, and high traffic density have caused the number of traffic accidents, the traffic accident fatality rate, and the traffic accident death rate per 10,000 vehicles to increase in economically developed regions. Wang (2009) suggested that while the number of traffic accidents in China has transitioned from rapidly increasing to steadily decreasing, the number of traffic accidents in Western China may still be increasing and noted that although the overall number of traffic accidents in economically developed regions is high, the traffic accident death rate is relatively low. Economic development level is closely related with a country’s traffic safety. On the one hand, low-income countries typically neglect traffic safety investments

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while increasing investment in safety, such as improving medical care standards, vehicle safety features, and road infrastructure, which can all enhance traffic safety. On the other hand, due to uneven levels of economic development within individual countries, differences could exist in laws, policies and the effectiveness of their implementation, which could in turn affect traffic safety. In addition, the differences existing in the culture and cost of living in countries and areas at different stages of economic development could further affect motorist behavior. Safety investment typically keeps pace with economic development, and lowincome countries commonly neglect investing in traffic safety. One study of public investment in traffic safety revealed that the traffic safety investment rate in Pakistan and Uganda was approximately USD 0.07–0.09 per person (Bishai et al. 2003). The rate of return on investment in traffic safety in low-income countries is actually very considerable, and the calculated rate of return on traffic safety investment in lowincome countries is close to that of health investments in many developing countries (Jacobs 1989; Norton et al. 2006). Increasing investment in safety facilities, such as improving medical standards and vehicle safety features, can reduce traffic injuries, and investment in healthcare can boost standards of medical assistance and reduce the accident death rate. Research has found that low medical investment, low levels of medical assistance provided to persons injured in traffic accidents, and a lack of timely medical assistance are likely to be the main reasons for the relatively high traffic accident death rate in lowincome countries (Bishai et al. 2006). For example, in the city of Kumasi in Ghana, 51% of persons severely injured in accidents die on scene, while the corresponding percentages in Monterrey, Mexico, and Seattle, America are 40 and 21%, respectively (Mock et al. 1998). However, Bishai et al. (2006) also suggested that improvements in emergency trauma care and passenger protection measures can reduce the accident death rate; however, this may not reduce the number of traffic accidents or traffic accident injuries. In addition, the increased use and upgrade of motor vehicle safety facilities, for example, seat belts (Loeb 1995), air bags (Kneuper and Yandle 1994; Levitt and Porter 2001), helmets (Jones and Bayer 2007; French et al. 2009) and motor vehicle safety inspections (Merrell et al. 1999), etc., has an extremely important effect on the prevention of accidents. Great differences also exist in the laws and policies and the effectiveness of their enforcement in countries with different levels of economic development. The current literature unanimously affirms the effect of traffic-related laws and policies on the number of traffic accidents and traffic-related casualties; however, conclusions are controversial. Numerous studies have concluded that seat belt-related laws (Dee 1998; Cohen and Einav 2003; O’Malley and Wagenaar 2004; Houston and Richardson 2005, 2006; Carpenter and Stehr 2008) and helmet-related laws (Sass and Zimmerman 2000; Houston and Richardson 2008; Dee 2009; French et al. 2009) have a positive impact on traffic safety and have also found that stricter traffic laws and regulations could increase the marginal cost of accidents to motorists and thereby reduce the number of accidents and the severity of injuries resulting from accidents (Dee 1998; Cohen and Einav 2003). Notably, conclusions concerning the effect of alcohol-related laws and regulations are considerably controversial. Most studies

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assert that alcohol-related laws help reduce motor vehicle accidents and the resulting deaths and injuries. For example, researchers have found that stricter blood alcohol concentration (BAC) laws (Dee 2001; Shults et al. 2001; Eisenberg 2003), zerotolerance laws (Shults et al. 2001; Carpenter 2004), withdrawal of administrative license (Grabowski and Morrisey 2001; Freeman 2007), minimum drinking age laws (O’Malley and Wagenaar 1991), and tax increases (Ruhm 1996; Chaloupka et al. 2002) could all help to reduce the motor vehicle accident death rate. However, other studies have found that BAC laws do not have a significant effect on the motor vehicle accident death rate, with some even reporting no effect (Eisenberg 2003; Freeman 2007). In addition, research has found that other laws and policies, such as minimum wage laws (Adams et al. 2012) and mandatory driver education (French et al. 2009), also influence traffic accidents and injuries. Moreover, apart from the existence of laws and regulations per se, the expectation of penalty and the degree of law enforcement also affect traffic safety, although the duration of their impact is limited. Research has found that the expectation of penalty (Williams et al. 1995; Benson et al. 1999; Makowsky and Stratmann 2011) and harsher penalties (McCarthy 1999; Young and Likens 2000) could both result in reduced traffic infractions and a lower traffic accident death rate (Young and Likens 2000). Furthermore, traffic tickets also have a positive effect on the behavior of high-risk motorists (Makowsky and Stratmann 2011). However, research by Redelmeier et al. (2003) found that although increased ticketing for traffic infractions has a short-term impact on traffic accidents, the effect disappears after 3 months. Differences also exist in the culture and cost of living in countries or areas with different levels of economic development, and these differences affect motorist behavior. Owing to economic pressure, taxi drivers in less-developed countries and areas tend to engage in risky driving to increase their income. However, increased income levels can also increase the value of time, which could reduce the true cost of fines, and differences in cultures and attitudes, life expectancies, and discount rates in countries and areas at different levels of economic development could also affect motorist behavior (Fosgerau 2005; Grimm and Treibich 2010). Differences in economic development level not only affect human behavior but also affect the types of vehicles on the road, which could in turn affect traffic safety. In countries with a low level of economic development, roads are filled with large numbers of motorcycles and bicycles, while in countries with a higher level of development, vehicles on roads mostly consist of sedans and busses. Researchers found that different types of vehicles have different accident rates and different causative factors of accidents and that motorcycles are especially prone to accidents (Smith 1999; Scuffham 2003), which may be attributable to the fact that motorcycle riders are especially likely to exceed speed limits, ride without a license, and fail to wear safety helmets (Peek-Asa and Kraus 1996; Shankar 2003; Bledsoe and Li 2005). Because light trucks are difficult to operate, often have poor brakes and have a high center of gravity, they are hard to drive and prone to rolling over (Anderson 2008). As a result of these factors, light trucks tend to be more hazardous than other vehicles when a traffic accident occurs (Joksch 1998; Toy and Hammitt 2003; White 2004). Furthermore, the level of public transportation development could also have

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an effect on traffic accidents. The WHO’s Global status report on road safety 2013: supporting a decade of action noted that because public transportation is subject to close supervision in most high-income countries, it tends to be safer than private vehicles (WHO 2013b). However, because supervision and safety cannot keep up with the fast growth in public transportation in numerous low- and middle-income countries experiencing rapid economic development, the rate of traffic accidents involving public transportation vehicles has also risen. Moreover, the mixed use of large and small vehicles may also lead to high rates of death and injury in traffic accidents. When trucks and sedans must share the road with pedestrians, bicyclists, and motorcyclists, the latter are especially prone to injuries (Smith 1999; Kobusingye et al. 2001; Scuffham 2003). Other studies have examined the effect of economic development on traffic accidents from the perspectives of corruption (Pellegrini and Gerlagh 2004; Anbarci et al. 2006), population characteristics (Bishai et al. 2006), insurance (Cohen and Dehejia 2003), financial deficit (Makowsky and Stratmann 2011), and casinos (Cotti and Walker 2010). Regarding research methods, much of the existing literature use panel data models to study factors that affect traffic accidents and the relevant mechanisms (Ruhm 1996; Dee 1999; Morrisey and Grabowski 2005; Freeman 2007). Bishai et al. (2006) employed ordinary least squares (OLS), fixed effect (FE), and random-effect models in conjunction with data from 41 countries to investigate the effect of economic development on the numbers of traffic accidents and casualties. Apart from these models, the difference-in-differences (DID) method has also often been used in research on the effect of policies on traffic accidents (Jackson and Owens 2011). Time series models (García-Ferrer et al. 2007), weighted least squares (WLS) estimation (Cotti and Walker 2010), Granger causality tests, and dynamic harmonic regression (DHR) models (García-Ferrer et al. 2007) are also commonly used. However, the existing literature has typically dealt poorly with the relationship between the level of law enforcement and traffic accidents, which may lead to problems, such as estimation deviation and simultaneous measurement of underestimation of law enforcement effect (Elvik 2002; Blais and Dupont 2005). Early studies chiefly uniformly use either micro or macro data for analyses. To overcome the deficiencies arising when only a single type of data is used, more recent studies have typically combined micro and macro data in analyses. For example, Gayer (2004) employed micro-level data concerning fatal accidents in conjunction with interstate cross-sectional data to deal with the problem of hidden variables, while a study by Anderson (2008) combined state-level panel data with micro-level police accident data.

4 Road Traffic Safety and Differences in Regional Economic …

33

Model Settings and Explanation of the Variables This paper employed a random effects (RE) model and Shapley value decomposition in conjunction with province-level traffic accident panel data in China during the 2007–2010 period to analyze the effect of GDP per capita, population, and vehicleand road-related factors on the number of traffic accidents, number of injuries, and number of fatalities and determined the relative contributions of these factors and their dynamic trends.

Panel Data Regression: Analysis of Factors Affecting Traffic Accidents, Deaths, and Injuries Previous research has found that economic growth is commonly accompanied by population growth, enhanced vehicle performance, and improved traffic infrastructure, which have major impacts on road traffic safety. In addition, a large body of research has shown that other factors closely related with economic development, such as medical care standards (Bishai et al. 2006), laws and regulations (Dee 1998; Cohen and Einav 2003), level of law enforcement (McCarthy 1999; Young and Likens 2000), and safety consciousness (Fosgerau 2005; Grimm and Treibich 2010) may also affect traffic safety. However, each of these indicators can only be used to gauge one aspect of socioeconomic development and may be difficult to measure or may not be available. Therefore, the use of GDP per capita as a general indicator of economic development (Kopits and Cropper 2005; Bishai et al. 2006) can provide a relatively comprehensive assessment of the effect of economic growth on traffic accidents. Population, vehicles, and road facilities are also important factors affecting road traffic safety. For example, the increased traffic congestion that accompanies growing population density could result in a high traffic accident rate, and the likelihood of casualties due to traffic accidents could also increase. Growth in the number of vehicles could have an effect similar to that of population growth, as shown in studies of high-income countries and areas where the number of vehicles on the road has a significant influence on traffic accidents (Bishai et al. 2006). Due to rapid changes in the number of vehicles that are occurring in low- and middle-income countries, the effect of the number of vehicles on traffic safety may differ from that in highincome countries, but there is a lack of research on these less-developed countries. In addition, road mileage is an indicator of road infrastructure development. On the one hand, increased road mileage may cause the number of accidents to increase. On the other hand, increased road mileage may be accompanied by improvements in road quality, which can reduce the number of accidents and their subsequent hazardous consequences. The most common models used with panel data are random effect model (RE) and fix effect model (FE); these two models differ in their assumptions concerning

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Y. Li and G. Zhang

individual effects. For FE model, the assumed individual effects are nonrandom variables, while for RE model, the assumed individual effects are random. When both models have consistent estimates, the RE model is more effective. Nevertheless, there is currently some debate in the literature concerning the use of RE and FE models. While the most widely used method for determining the applicability of RE and FE models is the Hausman test, this method is limited because the calculated statistics can be negative. However, as suggested by Schreiber (2008), a negative Hausman statistic indicates that the original hypothesis is not reasonable. Lian et al. (2014) used the Monte Carlo method to model the performance of the Hausman statistic under a small sample size and discovered that the power of the Hausman test was extremely low when the sample size was small (N = 30 × 30). As a consequence, this paper performed model selection on the basis of the data structure and the actual situation. According to Mundlak (1978), in most circumstances, to avoid a loss of degrees of freedom when performing estimations, when panel data consist of a large number of cross-sectional entities and have a short time span, individual effects should be seen as random. Because the data in this paper contained many cross-sectional entities and had a relatively short time span, the degrees of freedom in an FE model would be significantly reduced. From this perspective, the use of an RE model was the most suitable choice. Additionally, to determine whether the use of an RE model in this paper was reasonable, we used the Breusch-Pagan Lagrange Multiplier (LM) test; significant results obtained using this test indicated that individual effects existed. This paper therefore adopted an RE model. This paper employed the following model to analyze factors that affect traffic accidents, deaths, and injuries: ln(outcomeit ) =α0 + α1 ln(gdpit ) + α2 ln( popit ) + α3 ln(veh it ) + α4 ln(r oadit ) + μi + εit

(4.1)

where the subscripts i and t indicate province and time, respectively. Regarding the different outcomes of traffic accidents, differences may exist in the degrees of influence of the various relevant factors. In this paper, traffic accident outcomes were divided into three types, i.e., number of traffic accidents, number of persons injured in traffic accidents, and number of traffic fatalities, and the effect of economic development on each traffic accident outcome was investigated. In this paper, outcomeit indicates three types of accident outcomes, namely, the number of traffic accidents acdit , the number of persons injured in traffic accidents injit , and the number of traffic fatalities deathit ; gdpit is GDP per capita, popit is population, vehit is the number of vehicles, and roadit is the road mileage. In addition, u i is used to capture the various individual characteristics that do not change with time and cannot be measured, and εit is a random disturbance term, which is assumed to follow a normal distribution. The explanatory variables and explained variables selected in this paper underwent logarithmic transformation before use in regression analyses seeking to determine factors that influence the rate of change in the number of accidents and number of injuries.

4 Road Traffic Safety and Differences in Regional Economic …

35

α1, α2, α3, and α4 represent the effect of GDP per capita, population, number of vehicles, and road mileage, respectively, on the number of traffic accidents, number of persons injured, and number of fatalities. The effect of GDP per capita on traffic accident outcome may involve the indirect effects of vehicle- and road-related factors. This paper consequently adopted two interaction terms, i.e., GDP per capita and number of vehicles, GDP per capita and road mileage, to describe these effects. The model with the addition of interaction terms is as follows: ln(outcomeit ) =α0 + α1 ln(gdpit ) + α2 ln( popit ) + α3 ln(veh it ) + α4 ln(r oadit ) + δ1 ln(gdpit ) · ln(veh it ) + δ2 ln(gdpit ) · ln(r oad it ) + u i + εit

(4.2)

Furthermore, because a large gap in economic development exists between Eastern and Western China, the factors that affect the number of traffic accidents and number of traffic-related deaths and injuries, as well as the magnitude of their effect, may also differ. This paper consequently examined and compared the traffic safety and influencing factors in economically well-developed Eastern China with those in economically backward Western China.

Shapley Value Decomposition: Determining the Contributions of Influencing Factors To probe the trend characteristics of factors that cause traffic accidents, deaths, and injuries and quantify the effects of GDP per capita, population, and vehicle- and road-related factors on traffic accidents, deaths, and injuries, this paper used the Shapley value decomposition method (Shorrocks 1999; Wan 2004) to analyze the relative contributions of the various influencing factors. Commonly used factor decomposition methods can generally be classified into three types. The first consists of the Oaxaca decomposition method, which was proposed by Oaxaca (1973) and Blinder (1973) and subsequently developed further by Juhn et al. (1993) and Bourguignon et al. (2001). According to Chen et al. (2009), one shortcoming of the Oaxaca decomposition method is that it cannot explain differences in the contribution of specific variables to explained variables. The second type consists of semiparametric or nonparametric decomposition methods, which were established in Dinardo et al. (1996), and offer the advantage of not needing many structural hypotheses. However, methods of this type are limited because the decomposition results depend on the ranking of variables at the time of decomposition; in addition, the number of variables that can be analyzed is limited, and decomposition results have only a certain reference value (Chen et al. 2009). The third type consists of the decomposition method proposed by Shorrocks (1982) and Cowell and Jenkins (1995). The advantage of this method is that it can decompose the differences between

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Y. Li and G. Zhang

influencing factors into intragroup and intergroup differences, but it is also limited because the contribution of variables depends on the sequence of decomposition. Fields and Yoo (2000) and Morduch and Sicular (2002) subsequently developed this decomposition method further, although their research ignored constant and residual terms, and the decomposition method could only be applied to specified indicators (Chen et al. 2009). To avoid these problems, based on the Shapley natural decomposition principle (Shorrocks 1999) and the Before-After principle (Cancian and Reed 1998), Wan (2004) developed a more general method suitable for regression equations with different forms and different metrics to determine differences between variables. This method places few restrictions on the regression model and deals with the constant and residual terms in a more reasonable manner, reducing the possibility of model misspecification error and ensuring that decomposition results are more precise and the implications of the decomposition results are more explicit. In addition, this method can overcome the limitations of simple regression analysis and the solution of conventional indexes (Such as Gini coefficient and Thiel index), and obtain a theoretically possible influence factor for the specific contribution size and position ordering of explained variables (Wan 2004). The Shapley value decomposition method adopted in this paper is the regression equation-based Shapley value decomposition approach proposed by Wan (2004). Based on Eq. (4.1), while taking the availability of data into consideration, this paper used the logarithmic-mean deviation index (LMDI) to perform Shapley value decomposition. In accordance with Eq. (4.1), the decision functions for the three variables are as follows: acdit = exp(α0 ) · exp(α1lpgdpit + α2 lpopit + α3lveh it + α4 lr oadit ) · exp(εit ) (4.3) in jit = exp(β0 ) · exp(β1lpgdpit + β2 lpopit + β3lveh it + β4 lr oadit ) · exp(vit ) (4.4) death it = exp(γ0 ) · exp(γ1lpgdpit + γ2 lpopit + γ3lveh it + γ4 lr oadit ) · exp(ηit ) (4.5) When analyzing the relative contributions of the various factors, the constant terms exp(α0), exp(β0), and exp(γ0) can be eliminated because they cannot affect the results (Wan et al. 2007). The results can be obtained by calculating the difference between the initial value of the difference between the accident (casualties, deaths) and the difference between the accident (casualties, deaths) when the residual is 0 (Wan et al. 2007); therefore, the residuals can express the effects that cannot be explained by explanatory variables in the equations. After removing the influence of the residuals, the contributions of the various influencing factors can be obtained.

4 Road Traffic Safety and Differences in Regional Economic …

37

Explanation of Variables The data used in this paper were obtained from the China Road Traffic Accident Statistics Yearbook and the China Statistical Yearbook for the 2007–2011 period. The China Road Traffic Accident Statistics Yearbook is compiled by the Traffic Administration Bureau of the Ministry of Public Security of the People’s Republic of China and provides various indicators concerning road traffic accidents as determined by the Ministry of Public Security. In this paper, the number of traffic accidents, annual number of persons injured in traffic accidents, and annual number of fatalities in traffic accidents in China’s 31 provinces and autonomous regions (not including Hong Kong, Macao, and Taiwan) were obtained from the China Road Traffic Accident Statistics Yearbook; the GDP per capita, population, number of vehicles, and road mileage statistics were obtained from the China Statistical Yearbook; and this paper paired accident data with the economic data in each province. Because the sample size for the data for Hong Kong, Macao, and Taiwan was relatively small and their management systems differ from that of mainland China, this paper excluded the data from these three areas. The explanation of the main variables and their statistical description are shown in Table 4.1, and the expected influencing mechanisms of the variables are shown in Table 4.2.

Empirical Results Employing data from China’s 31 provinces and autonomous regions, this paper used a panel RE model based on Eqs. (4.1) and (4.2) to analyze the effect of economic development the number on traffic accidents, number of casualties, and number of fatalities. Because differences exist in the factors that influence traffic safety and their Table 4.1 Explanation of variables and their statistical description Variable

Description (unit)

Source

Mean

Min

Max

acd

Number of accidents

China Road Traffic Accident Statistics Yearbook

8136

600

46558

inj

Persons injured (persons)

China Road Traffic Accident Statistics Yearbook

9369

660

555566

death

Number of fatalities (persons)

China Road Traffic Accident Statistics Yearbook

2261

317

7994

gdp

GDP per capita (RMB)

China Statistical Yearbook

29937

6915

85213

pop

Population (1000 persons)

China Statistical Yearbook

42.67

2.87

105.05

veh

Number of vehicles (vehicles/1000 persons)

China Statistical Yearbook

6.12

0.13

21.59

road

Road mileage (km)

China Statistical Yearbook

12447

111163

283268

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Y. Li and G. Zhang

Table 4.2 Expected mechanisms affecting road traffic safety Differences in regional economic development

Areas with a high level of Areas with a low level of economic development economic development

GDP per capita Health care

Better and more immediate trauma care, thus reducing traffic fatalities

Vehicle types

Cannot obtain prompt, appropriate medical assistance, increasing accident-related fatalities

Many types of vehicles, Few types of vehicles and tending to cause collision relatively numerous accidents vehicle-pedestrian collisions, tending to cause traffic-related casualties

Laws and enforcement Relatively strict and comprehensive laws, facilitating reductions in accidents and casualties

Lax laws and supervision, low financial penalties for infractions, and violations of traffic laws by motorists, tending to cause accidents and casualties

Education

More universal driver education and better compliance with traffic laws, tending to reduce accidents

Less common driver education and poor compliance with traffic laws, tending to increase number of accidents

Attitudes

Greater degree of life safety consciousness and less risky driving, resulting in fewer accidents and casualties

Weaker life safety consciousness and lack of safe driving, resulting in relatively many accidents and casualties

Population

Increased population and high population density, increasing the probability of traffic accidents and casualties

Increased population, increasing the probability of accidents and casualties; however, relatively low population density, diluting the influence of population

Number of vehicles

Increased number of vehicles, increasing the probability of traffic accidents and casualties; however, better vehicle safety may weaken this effect

Increased number of vehicles, increasing the probability of traffic accidents and casualties; however, a relatively low vehicle density may keep this effect relatively weak (continued)

4 Road Traffic Safety and Differences in Regional Economic …

39

Table 4.2 (continued) Differences in regional economic development

Areas with a high level of Areas with a low level of economic development economic development

Road mileage

Increased road mileage, increasing the probability of accidents and casualties; however, improvements in road facilities could weaken the effect on accidents and casualties

Increased road mileage, increasing the probability of accidents and casualties; however, road infrastructure development in areas with a low level of economic development could reduce accidents and casualties

effects in areas with different levels of economic development, this paper performed regression analysis respectively for Eastern China and Western China to examine differences in factors that affect road traffic safety in areas with different levels of economic development. In addition, this paper also used Shapley value decomposition to assess the relative contributions of the various influencing factors. This paper used STATA 12.0 statistical software to perform empirical estimations.

Regression Results for Panel Data Using an RE Model Analysis of Factors that Affect the Number of Traffic Accidents As shown in Table 4.3, a negative correlation exists between GDP per capita and number of traffic accidents in China as a whole, in Eastern China, and in Western China; this effect is particularly strong in Eastern China. This result indicates that economic growth can reduce the incidence of traffic accidents; and this influence is most significant in Eastern China. The enormous effect of economic growth on road traffic safety can be attributed to infrastructure development and maintenance, improvements in laws and regulations, a shift in attitudes toward traffic safety, and improvements in medical care, etc. Bishai et al. (2006) found that the number of vehicles and road mileage were correlated with the number of accidents. In view of the possibility that the effect of GDP per capita on the number of accidents may be indirectly mediated by vehicle- and road-related factors, two interaction terms, GDP per capita and number of vehicles and GDP per capita and road mileage, were added in the regression equation. Regression results after considering the effect of nonlinear factors revealed that increased GDP per capita reduces the incidence of traffic accidents; the effect increases significantly with increased GDP per capita. This effect is greatest in Eastern China, followed by Western China and then China as a whole, indicating that when comparing areas with a high level of economic

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Y. Li and G. Zhang

Table 4.3 Regression results for number of traffic accidents using an RE model China as a whole(1)

China as a whole(2)

Eastern China(1)

Eastern China(2)

Western China(1)

Western China(2)

gdp

−0.598** (−5.07)

−1.936* (−1.90)

−0.887** (−4.09)

−5.932** (−2.87)

−0.428** (−2.31)

−2.666* (−1.94)

pop

0.711*** (3.53)

1.069*** (4.68)

−0.00406 (−0.01)

0.665 (1.28)

0.707** (2.41)

1.083*** (3.83)

veh

0.321* (1.89)

2.764*** (4.11)

0.380 (0.89)

9.012*** (3.89)

0.178 (0.85)

3.466*** (4.43)

r oad

−0.333** (−2.84)

−2.047** (−1.98)

0.259 (0.74)

−6.410** (−2.60)

−0.223 (−0.82)

−2.811** (−2.11)

gdp ∗ veh

−0.274** (−3.71)

−0.823** (−3.80)

−0.363** (−4.25)

gdp ∗ r oad

0.164* (1.70)

0.586*** (2.73)

0.244* (1.90)

*p

< 0.1, ** p < 0.05, *** p < 0.01

development to those with less economic development in Western China, economic growth has a greater effect on the reduction in the number of traffic accidents. A significant positive correlation exists in the number of vehicles and number of traffic accidents in China as a whole, in Eastern China, and in Western China; the effect is most significant in Eastern China. The increased GDP per capita weakens the negative effect of the increased number of vehicles on traffic safety. An increase in the number of vehicles increases traffic density and the variety of vehicle types on the road. These two factors may both increase the probability of traffic accidents. However, the effect of road mileage is opposite to that of number of vehicles. Road mileage displays a negative correlation with traffic accidents; this effect is most significant in the economically well-developed areas of Eastern China. A close relationship exists between road mileage and traffic infrastructure development, and increased road development is very commonly accompanied by improvements in road facilities, which could reduce the incidence of traffic accidents. The development of multiple lanes and sidewalks could separate motor vehicles from non-motor vehicles and pedestrians, and the separation of pedestrians and vehicles could effectively reduce contact between pedestrians and vehicles, which could lessen the likelihood of accidents. Although population has a significant positive correlation with increased number of traffic accidents in Western China, the effect of population on accidents in Eastern China is not significant, indicating that population increases in economically underdeveloped areas with a low population density could cause the probability of accidents to increase. Because Western China is vast and relatively unpopulated, population increases could directly result in an increased probability of accidents. Meanwhile, the infrastructure and traffic safety management standards in Western China are low. With population increases, the traffic demand increases, but the infrastructure and

4 Road Traffic Safety and Differences in Regional Economic …

41

traffic safety management level have not improved accordingly, leading to an increase in traffic accidents.

Analysis of Factors That Influence the Number of Traffic-Related Injuries As shown in Table 4.4, when there are no interaction terms, GDP per capita has a negative effect on traffic accident casualties in China as a whole, in Eastern China, and in Western China; the effect is greatest in Eastern China. After adding the interaction terms, a significant negative correlation still exists between GDP per capita and number of persons injured in traffic accidents in Eastern China; the magnitude of the effect is increased, but the effect of GDP per capita is no longer significant in China as a whole and in Western China. These results indicate that apart from population, vehicle- and road-related factors, increased GDP per capita in economically welldeveloped areas of Eastern China has resulted in reduced traffic deaths and injuries through the mediation of other factors. From one perspective, in areas with a high level of economic development, the upgrading of vehicles has been rapid, and safety features have improved steadily, resulting in a low probability of injury when an accident occurs. From another perspective, motorists and pedestrians in economically well-developed areas have greater traffic safety consciousness, traffic laws are better enforced in these areas, and the mandatory use of seatbelts and helmets has reduced the likelihood of traffic-related injuries. Similar to the number of traffic accidents, a significant positive correlation exists between number of vehicles and number of traffic-related deaths and injuries in China as a whole, in Eastern China, and in Western China; this effect is greater in Eastern China than in Western China and China as a whole. This result indicates that Table 4.4 Regression results for number of traffic-related injuries using an RE model China as a whole(1)

China as a whole(2)

Eastern China(1)

Eastern China(2)

Western China(1)

Western China(2)

gdp

−0.733*** (−5.93)

−1.636 (−1.62)

−1.130*** (−4.88)

−6.056*** (−2.80)

−0.387** (−2.01)

−1.280 (−0.93)

pop

0.475** (2.23)

0.895*** (3.77)

−0.662 (−1.14)

0.138 (0.24)

0.712** (2.36)

1.055*** (3.78)

veh

0.492*** (2.76)

3.481*** (5.24)

0.729 (1.59)

9.661*** (3.97)

0.212 (0.99)

3.657*** (4.69)

r oad

−0.296** (−2.33)

−1.708* (−1.66)

0.380 (1.00)

−6.304** (−2.44)

−0.250 (−0.91)

−1.675 (−1.26)

gdp ∗ veh

−0.335*** (−4.57)

−0.857*** (−3.76)

−0.377*** (−4.42)

gdp ∗ r oad

0.137 (1.42)

0.584*** (2.59)

0.127 (0.99)

*p

< 0.1, ** p < 0.05, *** p < 0.01

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Y. Li and G. Zhang

the steadily growing number of vehicles on the road has increased the probability of accidents, which in turn increases the likelihood of injuries resulting from accidents; this effect is especially significant in economically well-developed areas with high vehicle densities. Additionally, rising GDP per capita has weakened the effect of number of vehicles on number of accidents. Rising GDP per capita has caused residents’ discretionary incomes to increase, and additional income may cause people to consider switching to vehicles with better safety features. People in well-developed areas may also tend to use public transportation, which could also reduce the likelihood of traffic-related injuries. Although a negative correlation exists between road mileage and the number of accidental deaths and injuries in China as a whole and in Eastern China, this correlation is not significant in Western China. While population has a positive correlation with the number of traffic-related deaths and injuries in China as a whole and in Western China, the effect of population is not significant in Eastern China. This finding suggests that differences in factors that affect the number of persons killed or injured in traffic accidents between economically well-developed areas of Eastern China and economically underdeveloped areas of Western China exist. In densely populated Eastern China, the level of economic development is high, and road traffic facilities are relatively low. Although increases in population could result in an increased probability of accidents, vehicle and road safety features, such as airbags and seatbelts, have made injuries less likely in the event of an accident. In contrast, in areas with a low population density, population growth could result in an increased probability of accidents, but in the event of an accident, poor road conditions and slower vehicle speeds may result in injuries instead of fatalities.

Analysis of Factors That Affect the Number of Traffic Fatalities As shown in Table 4.5, before the incorporation of interaction terms, GDP per capita has a significant negative effect on the number of traffic fatalities in China as a whole, in Eastern China, and in Western China; this effect is similar in all areas. After incorporating the interaction terms, GDP per capita has a negative effect on the number of traffic fatalities in Eastern China, but the effect of GDP per capita is not significant in China as a whole and in Western China. Research has indicated that medical care standards and first aid capacity are important factors causing differences in traffic accident fatality rates (Mock et al. 1998; Bishai et al. 2006). Although a high standard of medical care does not directly reduce the incidence of accidents, it could reduce the risk of death in a traffic accident. Compared with Western China, Eastern China possesses high standards of medical care and excellent medical facilities, which can reduce the probability of traffic-related deaths. Because of the limited economic development in Western China, the number of vehicles is growing rapidly, and road construction has developed rapidly; however, improvements in standards of medical care are lagging, and thus, medical care cannot contribute to reducing the traffic-related fatality rate.

4 Road Traffic Safety and Differences in Regional Economic …

43

Table 4.5 Regression results for number of deaths resulting from accidents using an RE model China as a whole(1)

China as a whole(2)

Eastern China(1)

Eastern China(2)

Western China(1)

Western China(2)

gdp

−0.406*** (−6.09)

−0.228 (−0.42)

−0.355*** (−2.85)

−2.448* (−1.92)

−0.332*** (−4.24)

−0.707 (−0.98)

pop

0.681*** (5.55)

0.776*** (5.43)

0.717** (2.36)

1.093*** (3.81)

0.594*** (4.38)

0.606*** (4.12)

veh

0.0930 (0.95)

0.660* (1.87)

−0.174 (−0.71)

3.997*** (2.81)

0.0206 (0.22)

0.144 (0.35)

r oad

−0.124 (−1.49)

−0.0875 (−0.16)

0.262 (1.30)

−2.660* (−1.77)

−0.0126 (−0.09)

−0.343 (−0.49)

gdp ∗ veh

−0.0656* (−1.67)

−0.401*** (−3.04)

−0.0136 (−0.30)

gdp ∗ r oad

−0.00296 (−0.06)

0.255* (1.93)

0.0343 (0.51)

*p

< 0.1, ** p < 0.05, *** p < 0.01

In Eastern China, the number of vehicles has a significant positive correlation with the number of traffic fatalities, while road mileage has a significant negative correlation with the number of traffic fatalities. In contrast, neither factor has a significant effect on the number of traffic fatalities in Western China. Owing to the region’s low vehicle density, even if there was the same number of vehicles in Western China as in Eastern China, Western China may still not experience severe traffic congestion. In Eastern China, however, because of its consistently high vehicle density, increasing the number of vehicles on the road could cause the vehicle density to increase further, which could in turn raise the probability of traffic accidents. Furthermore, in Western China, due to the restrictions imposed by road conditions and the traffic situation, vehicle speed tends to be relatively slow, and the probability of severe accident outcomes tends to be relatively low. Furthermore, while population is seen to have a significant positive effect on number of traffic fatalities, the effects of population and number of vehicles may gradually increase in the future with the further economic development.

Shapley Value Decomposition Results Based on the relative contributions of the variables to the number of traffic accidents shown in Table 4.6, the variables with the greatest contribution are vehicle- and road-related factors, accounting for approximately 70% of the total contribution. In contrast, roads and GDP per capita have relatively small contributions to the effects influencing the number of traffic accidents. As the number of vehicles and population density increase, the likelihood of contact between people and vehicles could also increase, which is the main factor causing an increase in the number of traffic accidents. From the perspective of recent trends, as the growth rate of

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Y. Li and G. Zhang

Table 4.6 Relative contributions of variables to their overall effects on the number of traffic accidents (%) 2007

2008

2009

2010

2011

gdp

10.23

11.54

pop

35.25

33.42

11.73

12.43

12.93

33.34

32.65

veh

35.85

32.25

36.53

36.24

35.72

36.19

r oad

17.11

18.34

18.28

18.33

18.36

China’s overall population slows, the overall effect of population is also gradually decreasing, with the contribution decreasing from 35.25% in 2007 to 32.35% in 2011. Although the contribution of GDP per capita is relatively low, it has increased steadily, climbing from 10.23% in 2007 to 12.93% in 2011. The contributions of vehicle- and road-related factors have remained quite stable. Table 4.7 shows the relative contribution of each variable to the number of casualties from traffic accidents. Similar to the contributions of factors influencing the number of traffic accidents, the number of vehicles and population are also the main factors that contribute to the effects on the number of persons injured in traffic accidents, while GDP per capita has less effect on traffic-related injuries than on the number of traffic accidents. It can be seen from recent trends that the effect of population on the number of traffic casualties has gradually decreased, falling from 34.45% in 2007 to 32.43% in 2011. In contrast, the effect of roads has increased from 17.82% in 2007 to 220.15% in 2011. Our findings indicate that improved vehicle safety and improvements in the development of road facilities have important significance for the prevention of traffic-related injuries. Finally, the contributions of number of vehicles and GDP per capita to the effect on traffic-related casualties have remained stable. Table 4.8 shows the relative contributions of the variables to traffic fatalities, and population remains the factor with the greatest contribution to the effect on the number of traffic fatalities. Notably, GDP per capita is second only to population regarding the contribution to the effect on the number of traffic fatalities, while the importance of number of vehicles is relatively low. The relative contribution of population to the number of traffic fatalities is similar to those for number of traffic accidents and number of traffic-related injuries, while the contribution of GDP per capita is significantly higher than those for number of traffic accidents Table 4.7 Relative contributions of variables to their overall effects on the number of traffic-related casualties (%) 2007

2008

2009

2010

2011

gdp

8.32

7.91

8.52

8.85

9.07

pop

34.45

35.32

34.66

34.14

32.43

veh

37.67

37.83

37.61

37.68

37.62

r oad

17.82

18.74

18.91

19.22

20.15

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Table 4.8 Relative contributions of variables to their overall effects on the number of traffic-related fatalities (%) 2007

2008

2009

2010

2011

gdp

25.28

25.94

pop

34.85

36.38

26.31

27.13

28.25

36.41

36.54

veh

20.19

36.54

20.59

20.73

20.32

20.18

r oad

18.75

16.63

16.12

15.78

14.34

and number of traffic injuries. In 2011, GDP per capita contributed 28.25% to the effect on the number of traffic fatalities, while its contributions to the effect on the number of traffic accidents and number of injuries were 12.93 and 9.07% in the same year, respectively. This indicates that the improved standards of medical care, legal reform, and high safety consciousness, etc. brought by economic growth make important contributions to reducing the number of traffic-related fatalities. Recent trends reveal that the relative contribution of GDP per capita has a significant and steady increase, while the contribution of roads has decreased significantly, falling from 18.75% in 2007 to 14.34% in 2011. The contributions of population and number of vehicles to the number of traffic fatalities have remained relatively stable. The foregoing analysis results reveal that population is the factor with the greatest contribution to the effect on traffic safety. This implies that controlling population growth, reducing traffic density, and diverging traffic flows in a rational manner could have important significance in reducing traffic accidents and accident-related deaths and injuries. Furthermore, the number of vehicles has a great effect on the incidence of traffic accidents and the number of traffic-related injuries, and GDP per capita has a substantial effect on traffic-related fatalities. Notably, the effect of economic development on traffic safety is long-term, and the contributions of the variables to the effect on traffic accident outcomes found in this paper cannot change very dramatically during a 5-year period. Observations and analysis should be performed over a longer period to more precisely gauge changes in the effects of these factors.

Conclusions and Policy Recommendations With China’s economic development, traffic safety is increasingly becoming a key means of preventing deaths, injuries, and property losses. To analyze the effect of economic growth on traffic safety, as well as the mechanisms of action, this paper gathered province-level traffic accident panel data in China for the 2007–2010 period, used an RE model and Shapley value decomposition to analyze the effect of GDP per capita, population, and vehicle- and road-related factors on the number of traffic accidents, number of injuries, and number of fatalities and determined the relative contributions of these factors and their dynamic trends. This paper made the following discoveries.

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First, from the perspective of China as a whole, increased GDP per capita has a significant effect on the reduction in traffic accidents but fails to bring about a significant reduction in deaths and injuries resulting from accidents. The number of vehicles has a significant positive correlation with a higher number of traffic accidents and traffic-related casualties, but a higher GDP per capita weakens the effect of number of vehicles. This phenomenon may be attributable to vehicle upgrades and road facility construction. Road mileage has a negative correlation with number of traffic accidents and number of traffic-related injuries, but higher GDP per capita also reduces the magnitude of this effect. Population has a significant positive correlation with number of traffic accidents and number of casualties. Second, a comparison of Eastern and Western China revealed that GDP per capita has a negative effect on the number of traffic accidents; this effect is greater in Eastern China than that in Western China. This result indicates that economic growth facilitates the prevention of traffic accidents, and the importance of economic growth to reducing the number of traffic accidents is greater in areas with a high level of economic development than in economically underdeveloped Western China. The number of vehicles has a significant positive correlation with the number of traffic accidents; this effect is most significant in Eastern China. However, growth in GDP per capita tends to weaken the negative effect of an increased number of vehicles on traffic safety. The effect of road mileage is the opposite of that of the number of vehicles, and it has a negative correlation with number of traffic accidents in both Eastern and Western China. Population has a significant positive correlation with the number of traffic accidents in Western China, but this effect is not significant in Eastern China. This result suggests that a higher population in economically underdeveloped, sparsely populated areas could increase the probability of accidents. Regarding the number of persons killed or injured in traffic accidents, GDP per capita has a negative correlation with traffic-related injuries in Eastern China, but this effect is not significant in Western China. The number of vehicles has a significant positive effect on traffic-related injuries; this effect is greater in Eastern China than that in Western China or China as a whole. Growth in GDP per capita tends to weaken the effect of number of vehicles on number of accidents; this effect may be attributable to vehicle upgrades and the construction of road facilities. In Eastern China, road mileage has a negative correlation with the number of traffic-related injuries, while population has a positive effect on the number of traffic-related injuries; however, neither factor has a significant effect in Western China. Regarding the number of traffic fatalities, the increase in GDP per capita in Eastern China has reduced the number of trafficrelated fatalities, but this effect is not significant in Western China, which may be attributable to East-West differences in medical care standards. Although high standards of medical care cannot directly reduce the incidence of accidents, it can reduce the risk of traffic fatalities. Despite the rapid development of vehicle number and road construction in the western China, the improvement speed of medical care standards has failed to keep pace with it. In Eastern China, the number of vehicles has a significant positive correlation with the number of traffic-related fatalities, and road mileage has a significant correlation with the number of traffic-related fatalities; however, neither of these factors has a significant effect on traffic-related fatalities

4 Road Traffic Safety and Differences in Regional Economic …

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in Western China. Finally, population has a significant positive effect on the number of traffic-related fatalities. Third, the results of Shapley value decomposition revealed that population makes the greatest contribution to the overall effect on traffic safety. Accordingly, controlling population growth, reducing traffic density, and diverging traffic flows in a rational manner could have important significance in reducing traffic accidents and accidentrelated deaths and injuries. Furthermore, the number of vehicles has a substantial effect on the incidence of traffic accidents and the number of traffic-related injuries, and GDP per capita has a notable effect on traffic-related fatalities. Recent trends indicate that the effects of GDP per capita and road mileage on the number of traffic accidents and number of traffic injuries have increased steadily, while the effect of population has decreased and the influence of number of vehicles has remained stable. However, for traffic-related fatalities, the effect of GDP per capita has increased significantly, while the effect of road mileage has decreased steadily and the effects of population and number of vehicles have remained stable. Empirical testing revealed that differences in traffic safety exist in areas of China with different levels of economic development, and safety protection measures needed in each region should therefore be differentiated. In economically welldeveloped areas, the economic development level is high, and road infrastructure development is relatively extensive; therefore, the main factor threatening traffic safety is traffic density. Consequently, the growth rate of the number of vehicles should be controlled, and people should be encouraged to use public transportation, which could reduce the probability of accidents. Additionally, the establishment of intangible infrastructure should be strengthened, such as the enactment of laws and regulations and the promotion of driver education, which should be more effective at improving road traffic safety. In economically underdeveloped Western China, as the population and number of vehicles grow rapidly in pace with increasing GDP, the development of safe road infrastructure, upgrades in vehicle safety features, and improvements in the mechanism of traffic accident trauma treatment should be effective means to enhance traffic safety in underdeveloped regions. Several aspects of this study can be improved further. First, research on traffic accidents has been conducted for many years overseas; however, this type of research has only recently gotten underway in China. Therefore, accident data collection methods are still crude, and it is difficult to pair accident data with other data (such as data concerning medical care received by accident victims), which has impeded holistic analyses. Second, differences may exist in the effects of various influencing factors in the case of various violations of traffic regulations; however, violations of regulations were not distinguished in the data used in this study. This paper focused solely on general factors that affect the number of accidents, accident-related injuries, and accident-related fatalities. Furthermore, economic growth has numerous impacts, and GDP per capita, examined in this paper, can only present some effects of economic growth. Owing to difficulties in measurement, this paper excluded other aspects, such as safety consciousness and level of enforcement of traffic laws, from the analysis; therefore, in-depth studies on such aspects are needed in the future.

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Acknowledgements The authors would like to thank Professor Shoufeng Huang from Xiamen University, China for his constructive suggestions and advice.

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Part II

Risk Factor Analysis: Traffic Accidents, Traffic Violations and Related Severities in Guangdong Province of China

Chapter 5

Traffic Violations in Guangdong Province of China: Speeding and Drunk Driving Guangnan Zhang, Kelvin K. W. Yau, and Xiangpu Gong

Abstract The number of speeding- and drunk driving-related injuries in China surged in the years immediately preceding 2004 and then began to decline. However, the percent decrease in the number of speeding and drunk driving incidents (decrease by 22%) is not proportional to the corresponding percent decrease in number of automobile accident-related injuries (decrease by 47%) from the year 2004 to 2010 (Traffic Management Bureau, Ministry of Public Security, Annual Statistical Reports on Road Traffic Accidents). Earlier studies have established traffic violations as one of the major risks threatening road safety. In this study, we examine in greater detail two important types of traffic violation events, speeding and drunk driving, and attempt to identify significant risk factors associated with these types of traffic violations. Risk factors in several different dimensions, including driver, vehicle, road and environmental factors, are considered. We analyze the speeding (N = 11,055) and drunk driving (N = 10,035) data for the period 2006–2010 in Guangdong Province, China. These data, obtained from the Guangdong Provincial Security Department, are extracted from the Traffic Management Sector-Specific Incident Case Data Report and are the only comprehensive and official source of traffic accident data in China. Significant risk factors associating with speeding and drunk driving are identified. We find that several factors are associated with a significantly higher probability of both speeding and drunk driving, particularly male drivers, private vehicles, the lack Reprinted from Accident Analysis & Prevention, 64, Zhang, G., Yau, K.W., Gong, X. Traffic violations in Guangdong Province of China: Speeding and drunk driving, 30–40, 2014, with permission from Elsevier. G. Zhang (B) Center for Studies of Hong Kong, Macao and Pearl River Delta, Institute of Guangdong, Hong Kong and Macao Development Studies, Sun Yat-sen University, Guangzhou, China e-mail: [email protected] K. K. W. Yau Department of Management Sciences, City University of Hong Kong, Hong Kong, China X. Gong Center for Studies of Hong Kong, Macao and Pearl River Delta, Sun Yat-sen University, Guangzhou, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 G. Zhang and Q. Zhong (eds.), Road Safety in China, https://doi.org/10.1007/978-981-16-0701-1_5

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of street lighting at night and poor visibility. The impact of other specific and unique risk factors for either speeding or drunk driving, such as hukou, road type/grades, commercial vehicles, compulsory third party insurance and vehicle safety status, also require particular attention. Legislative or regulatory measures targeting different vehicle types and/or driver groups with respect to the various driver, vehicle, road and environmental risk factors can subsequently be devised to reduce the speeding and drunk driving rates. As the country with the highest number of traffic accident fatalities in the world, applying these findings in workable legislation and enforcement to reduce speeding and drunk driving rates will save tens of thousands of lives. Keywords Traffic violations · Speeding · Drunk driving

Introduction Speeding- and drunk driving-related traffic fatalities and injuries have been a leading public health problem worldwide for some time. In most high-income countries, approximately 20% of driver fatalities are directly related to the driver’s high blood alcohol level at the time of the crash (Global Road Safety Partnership 2004a). In New Zealand, 31% of traffic fatalities and 17% of severe injuries in traffic accidents in the year 2002 were found to be related to speeding (Global Road Safety Partnership 2004b). In low-and middle-income countries, the situation is perhaps worse due to the lack of adequate infrastructure and to inadequate traffic management. Although very little data are available on the speeding- and drunk driving-related fatalities and injuries, studies have shown that in low- and middle-income countries, approximately 33–69% of drivers killed and 8–29% of drivers injured in traffic accidents had consumed alcoholic beverages prior to driving (Global Road Safety Partnership 2004a). In China (a middle-income country according to World Bank’s country classification by GNI per capita), the number of speeding- and drunk driving-related injuries surged in the years before 2004 and then began to decline. The Annual Statistical Report on Road Traffic Accidents (Traffic Management Bureau, Ministry of Public Security) showed that in 2000, a total of 40,873 speeding- and drunk driving-related injuries were recorded; this number reached its peak in the year 2004, when 73,499 victims were injured. Since then, the amount has fallen steadily; in 2010, the number of speeding- and drunk driving-related injuries was 57,219, and a 22% reduction overall has been observed since 2004. However, this decrease in number cannot obscure the fact that the magnitude of the decline in speeding and drunk driving injuries is less significant than that of the reduction of automobile accident-related injuries. In 2010, the overall number of automobile accident injuries was 254,075,

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which is nearly half of the 480,865 injuries (reduce by 47%) in 2004. The disproportionate decline between accident- related injuries and speeding- and drunk drivingrelated injuries implies that in China, the reduction of speeding and drunk driving incidents is more challenging compared with other causes of automobile accidents. For this reason, speeding and drunk driving have become issues of focus for legislative action. On February 25, 2011, the 8th Amendment to the Criminal Law was passed by the Standing Committee of the People’s Congress, stipulating that both car-racing on public roads and drunk driving are criminal offenses, whether these actions cause any accidents or not. Under the amended law, whoever drives a vehicle after drinking shall have his or her license suspended for at least 6 months and pay a minimum fine of CNY 1000 (≈USD 160). This amended law has had a positive impact on drunk driving rates in the short term. Within the four months of its implementation, the number of drunk driving incidents nationwide decreased by 45.4% compared to the number of incidents recorded in the same period last year. Whether this legislation continues to be an effective deterrent in the long term remains to be seen. As a public health issue, speeding and drunk driving have attracted the attention of professionals from various sectors. In 2004, a series of reports and manuals published by Global Road Safety Partnership as well as other organizations, ranging from World Health Organization to the World Bank, synthesized literature published up to 2004 on the topic of speeding and drunk driving. Apart from these reports and manuals, a variety of other studies thereafter have also contributed to the study of these issues from various perspectives. In general, demographic characteristics are considered to be an important factor affecting speeding and drunk driving. Among these characteristics, male drivers are usually regarded as a significant risk factor because they are generally more likely to speed and/or drive drunk than females (Hennessy et al. 2004; Jonah 1990, 1997; OECD 2006; Smart and Mann 2002; Wickens et al. 2008, 2011; Shinar and Compton 2004). This is perhaps because males are often overconfident and less likely to comply with traffic laws when driving, and therefore, they tend to be less cautious about the perils of dangerous driving behaviors (Shinar and Compton 2004). Likewise, youth is another risk factor that affects speeding and drunk driving. Young drivers are more likely to speed due to their underestimation of the potential risk of driving situations and overestimation of their level of skill (Castella and Perez 2004; Deery 1999; McKenna and Horswill 2006; Machin and Sankey 2008). This risk factor may also be attributed to the inexperience of young drivers as well as their higher level of excitement-seeking, lower levels of altruism, greater perceived likelihood of an accident, and lower aversion to risk taking (Machin and Sankey 2008). Moreover, young drivers are not only at higher risk of speeding, but studies have also demonstrated that youth is one of the predictors that profiles repeat drunk driving offenders (Ferguson et al. 1999). Few studies have focused on other demographic factors. However, demographic factors are worthy of study due to the light they may shed on how individuals’ social backgrounds may affect their driving behavior. Of this comparatively small body of literature, Kim et al. (2010), using a random telephone survey, found that among

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drivers convicted of drunk driving in Hong Kong, those with an occupation requiring social drinking are more likely to be involved in traffic accidents. Moreover, women with higher education levels are found to exhibit a higher likelihood of being involved in drunk driving than their less educated counterparts (Kim et al. 2008, 2010). In addition to focusing on factors that are associated with demography, several studies have investigated other factors related to vehicles and environment. Speeding has been identified as a significant factor associated with crashes for commercial vehicles, especially in low-income and middle-income countries (Global Road Safety Partnership 2004b). Compared to goods vehicle drivers, private vehicle drivers have the highest prevalence of a high blood alcohol concentration (BAC), with motorcyclists ranking second in São Paulo. In São Paulo, 6 p.m.–12 a.m. and 12 a.m.–6 a.m. were found to be the time periods with the greatest absolute number of accidents and BAC-positive cases; Saturdays and Sundays were the days with the highest prevalence of accidents and BAC-positive accidents (Ponce et al. 2011). Other research has focused on law enforcement. By investigating the effect of enforcement targeted at drunk driving in Greece, Yannis et al. (2007) concluded that enforcement can be more successful if better police practices and resource allocation strategies are adopted. Moreover, when public campaigns and enforcement targeted at speeding and drunk driving were implemented, they had a significant independent impact in reducing crashes, while their interactive impact was anti-complementary; in contrast, although the enforcement and campaigns had no independent impact, their interactive impact was significant in reducing serious crashes involving young male drivers (Tay 2005). The recent literature generally focuses on the risk factors surrounding drivers, vehicles and environment, whereas risk factors relating to roads are rarely discussed. Furthermore, in terms of data sources, few studies are based on data sets extracted from low- and middle-income countries where the infrastructure, modes of transport and demographic structure are different from those in high-income countries. As such, this study attempts to determine the significant risk factors associated with (i) speeding and (ii) drunk driving by analysing traffic accident data in China, with the aim of devising corresponding traffic regulations and legislative measures targeting different vehicle types and/or driver groups in terms of the various driver, vehicle and environment risk factors.

Materials and Methods Data A recent research study on large scale traffic accident data in China, focusing on traffic violations and accident severity, established the role of traffic violations as one of the major risks threatening road safety (Zhang et al. 2013). The current study further examines two important types of traffic violations, speeding and drunk driving, and

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attempts to identify significant risk factors associated with these traffic violations. We analyze the speeding and drunk driving data for the period 2006–2010 in Guangdong Province, China. These data, obtained from the Guangdong Provincial Security Department, are extracted from the Traffic Management Sector-Specific Incident Case Data Report and are the only comprehensive and official source of traffic accident data in China recorded by on-scene traffic police. Together with non-traffic violation accidents, the data contain 11,055 and 10,035 samples relevant to speeding and drunk driving incidents, respectively. Each sample includes demographic information, injury severity, vehicle characteristics, road conditions, the time of day of the accident and environmental conditions. Recent studies on road safety in China mainly rely on observational or survey data, to assess pedestrian safety (Liu et al. 2011; Zhuang and Wu 2011), helmet and seatbelt wearing usage (Routley et al. 2008; Huang et al. 2011), and drivers’ attitude and behavior on road safety (Zhang et al. 2006; Huang et al. 2008). Other studies have adopted regional traffic accident management data (Huang et al. 2008; Kong and Yang 2010), yet not on a large scale. However, research study in China using large scale official database targeting different traffic violation types is rarely found. Guangdong Province is located in the Southern part of Mainland China. Since the reform and opening-up policy in 1978, its GDP recorded an annual double-digit growth rate every year, which continuously ranked first among all provinces in the country. As of 2010, the resident population was 104.30 million, the only province in China having a total resident population of over 100 million. Accident analysis using Guangdong Province data is representative. First, due to Guangdong’s rapid economic development and consequently the vehicle numbers growth, traffic accident incidences are the highest among all 31 provinces in China. In 2010, the recorded traffic accidents, deaths and injuries were 13.84%, 9.51% and 14.37% (relative to nationwide total) respectively. Second, Guangdong is having the highest percent (30%) of floating population (over 36 million) in China. With such a population mix in the province, risk factors are more representative when compared with the data in other provinces. This is particularly important when assessing the effect of risk factors relating to floating population characteristic such as hukou, occupation, overload and insurance. The generalization of the research findings to other provinces in China and even to countries outside China becomes more probable. Third, Guangdong continues to take up the role of national reform “experimental field” (The National Development and Reform Commission, The Outline of the Plan for the Reform and Development of the Pearl River Delta (2008–2020), December 2008). Therefore, current study on Guangdong’s road safety and its corresponding policy recommendations will form the basis for the development of nationwide traffic management system. So, results arising from which are exemplary. To determine the risk factors associated with speeding or drunk driving, the dependent variables are set as follows: for the presence of speeding or drunk driving, ‘1’ = yes and ‘0’ = no. As the outcome measure is either 0 or 1, a logistic regression model can be used to estimate the effect of the risk factors on the probability of (i) speeding or (ii) drunk driving. The risk factors that were considered are the following: driver, vehicle, road and environment.

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Risk Factors The risk factors under consideration in the current study have been described in a previous study derived from the same database (Zhang et al. 2013).

Driver Factor Drivers’ age and gender are considered to be potential risk factors. According to the typical driving habits in different age groups and level of driving skill, driver age is divided into five groups: ≤ 25, 26–35, 36–45, 46–55 and ≥ 56. Driving experience is categorized in six groups (measured in years of experience): ≤ 2, 3–5, 6–10, 11–15, 16–20 and ≥ 21. Although drivers’ education, income and social status are expected to be potential factors that are associated with traffic violations, this information is generally not recorded in the traffic accident database. Instead, related information indicated by the drivers’ hukou origin and occupation are available. Two groups of the hukou household registration system are examined: rural hukou and urban hukou. The rural and urban hukou classification broadly reflects the latent effect of education level, income and social status differences. Urban residents received state-allocated jobs and access to an array of social services while rural residents were expected to be more self-reliant. Such system minimizes the movement of people between rural and urban regions, particularly discouraging rural residents moving to cities. However, with the economic growth in China in recent years, huge number of workers moves from country side to cities to find job. Occupations are classified as military and police, general staffs, workers, civil servants, the self-employed, farmers, migrant workers, the unemployed and other occupations.

Vehicle Factor Three major vehicle types, namely, private vehicles, goods vehicles and motorcycles, are included in the data. In addition, indicator variables are generated according to the vehicle’s safety status, overload condition, whether the vehicle had any compulsory third party insurance and whether the vehicle was a commercial vehicle.

Road Factor Types of traffic lanes can be divided into vehicle lanes, shared lanes and other lanes in accordance with their functional classification. When considering types and grades, roads can be divided into expressways, ordinary highways and urban highways. Specifically, ordinary highways include the first, second, third, fourth and fifth-class highways; urban highways include urban expressways, urban ordinary highways and

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urban other highways (detailed road grades classification can be found: GA 17.1– 2003 road traffic accident site code Part 1 road type code). There exist big differences between the road/highway quality in China. Having such a detailed road/highway classification provides accurate measure of the quality of the road infrastructure and strong discrimination power on the analysis of speeding incidences.

Environment Factor There are eight environmental factors under consideration: street-light condition, weather conditions, visibility level, day of the week, whether it was a public holiday, the time of day, the season and the year of the accident. Street-light condition is classified into daylight, good street lighting at night and no street lighting at night. Weather conditions (good = 0, bad = 1) and visibility level (good = 0, bad = 1) are generated as indicator variables accordingly. Weekends are defined as 17:00 Friday to 23:59 Sunday, as it is anticipated that the occurrence of Friday traffic accidents after 17:00 is similar to those occurring on Saturday and Sunday (MacLeod et al. 2011). Public holidays refer to the holidays stipulated by the State Council of China (before 2007, public holidays in China included New Year, Chinese New Year, International Labor Day and National Day; from 2008 onwards, three holidays were added: the Qing Ming Festival, Dragon Boat Festival and Mid-Autumn Festival). Time of day has been classified into six groups: 00:00–06:59, 07:00–08:59, 09:00– 11:59, 12:00–16:59, 17:00–19:59, and 20:00–23:59. In accordance with the Chinese Bureau of Meteorology, seasons are defined as spring (March to May), summer (June to September), autumn (October to November) and winter (December to February).

Statistical Data Analysis Contingency tables are constructed to assess the association between risk factors and (i) speeding and (ii) drunk driving. Chi-square tests of independence are conducted with the level of significance at 5%. To identify the differences among the risk classes in each factor, corresponding (i) speeding and (ii) drunk-driving proportion by each risk class within a factor are computed. Such proportion represents the corresponding traffic violation proportion within a particular risk class, which provides insights in differentiating risk class effect on traffic violation incidences. To further estimate the effect of different predictor variables on the likelihood of the occurrence of (i) speeding and (ii) drunk driving, logistic regression analyses are conducted. We consider the use of multivariate stepwise logistic regressions to identify significant factors determining (i) speeding and (ii) drunk driving and to estimate the magnitude of adjusted odds ratios (ORs) for each significant factor while controlling for other confounding factors (Hosmer and Lemeshow 1989). The adjusted ORs of significant factors and their 95% confidence intervals (CIs) are computed using a stepwise logistic regression model in which all factors were initially

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included and from which insignificant factors were subsequently removed by the stepwise procedure. Entry and removal probabilities for the stepwise procedure are both set at 0.05. As a remark, results obtained from this study are based on the recorded data of speeding- or drunk driving-related accidents (that is, non-accident cases are not included in the data). Therefore, the results on speeding or drunk driving should be interpreted as the effect of risk factors conditional on relevant accident cases according to the recorded accident data.

Empirical Results and Discussion Risk Factors Affecting Speeding Table 5.1 presents the chi-square test of independence. The results indicate that both driver’s gender and age (in years) are important factors significantly associated with speeding. From Table 5.2, speeding drivers are more likely to be young (speeding proportion: ‘ ≤ 25’ = 14.27%, ‘26–35’ = 13.69%, ‘36–45’ = 13.44%, ‘46–55’ = 11.95%, ‘ ≥ 56 ’= 6.85%) and male (speeding proportion: ‘female’ = 6.95%, ‘male’ = 13.80%). Additionally, an increased risk of speeding is associated with rural hukou (speeding proportion: ‘urban hukou’ = 12.87%, ‘rural hukou’ = 14.62%), farmers and general staffs (speeding proportion: ‘farmers’ = 19.37%, ‘military and police’ = 15.79%, ‘general staffs’ = 17.83%, ‘workers’ = 10.36%, ‘civil servants’ = 14.88%, ‘self-employed’ = 14.27%, ‘migrant workers’ = 12.15%, ‘unemployed’ = 8.73%, ‘other occupations’ = 11.44%). For vehicle factors, vehicle type, safety status, the presence of compulsory third party insurance and commercial operation status are found to have significant relationships with speeding. Specifically, private vehicles (speeding proportion: ‘motor cycles’ = 6.58%, ‘private vehicles’ = 18.14%, ‘goods vehicles’ = 15.69%), unfit status (speeding proportion: ‘fit status’ = 12.97%, ‘unfit status’ = 23.61%), compulsory third party insurance (speeding proportion: ‘no’ = 10.59%, ‘yes’ = 13.69%) and commercial operation status (speeding proportion: ‘no’ = 11.56%, ‘yes’ = 17.60%) are factors that are all likely to positively affect speeding. The results indicate that second and third class highways significantly contribute to speeding (speeding proportion: ‘expressways’ = 5.78%, ‘first class highways’ = 14.01%, ‘second class highways’ = 22.63%, ‘third class highways’ = 20.23%, ‘fourth class highways’ = 18.15%, ‘fifth class highways’ = 14.71%, ‘urban expressways’ = 11.87%, ‘urban ordinary highways’ = 6.89%, ‘urban other highways’ = 9.55%). Among environmental factors, it is found that street-light condition, visibility level, public holidays and time of day exhibit a certain significance. In particular, the highest probability of speeding is found for no street-lighting at night (speeding pro-portion: ‘daylight’ = 13.40%, ‘good street-lighting at night’ = 9.96%, ‘no

5 Traffic Violations in Guangdong Province … Table 5.1 Chi-square test of independence

Factors

63 Speeding (N = 11,055)

Drunk-driving (N = 10,035)

χ2 (P-value), degree of freedom

χ2 (P-value), degree of freedom

Driver factors Driver’s gender

28.273* ( 1.2 g/l is the standard for drunk driving.46 Tables 10.2 and 10.3 show the penalties for drunk driving and drunk driving with an extremely high BAC in GD and in HK and Macao, respectively. In addition, the maximum speed limits for road traffic among GD, HK and Macao are different (Tables 10.4, 10.5 and 10.6). The speed limits in GD are the same as the national standards; the maximum and minimum speed limits on freeways are 60 and 120 km/h, respectively. Depending on road conditions and vehicle type, the speed limit on urban roads and roads is between 30–70 km/h.47 As a punishment for speeding, a DOP reduction and fines shall be imposed according to the excess of the speed limit.48 The maximum speed limits in HK are 50, 70 and 80 km/h. Driving in excess of these speed limits is considered speeding, and the driver could be fined HK $4000.49 According to vehicle type and road type, the maximum speed limits in 44 The General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China: Blood-Breath Alcohol Concentration and Examination for Drivers (GB195222004). 45 Article 1A of Section 39A of Part 5 of Road Traffic Ordinance, HK: The proportion of alcohol in a person’s breath, blood, or urine. 46 Article 90 of Road Traffic Law of the Macau Special Administrative Region. 47 Articles 35 and 36 of Regulations of the People’s Republic of China On Road Traffic Administration. 48 Articles 86 and 87 of Regulations of The People’s Republic of China On Road Traffic Administration. 49 Section 40 (Speed limit) and Section 41 (Driving in excess of the speed limit) of Part 5 of Road Traffic Ordinance, HK.

GD

Penalties for drunk driving with an extremely high BAC

Criminal punishment

1. Death caused by DUI: Whoever commits a crime that causes a traffic accident or drives dangerously in violation of Article 133 of the Criminal Law shall be sentenced to fixed-term imprisonment of no longer than three years or criminal detention 2. DUI and hit-and-run or other particularly bad circumstances: The violator shall be sentenced to fixed-term imprisonment of not less than three years but no longer than seven years; whoever causes death to another person due to a hit-and-run shall be sentenced to fixed-term imprisonment of no less than seven years (continued)

Drunk driving or drunk driving with an extremely high BAC resulting in a major traffic accident and constituting a crime: Criminal liability shall be investigated according to laws; the driver’s license shall be revoked, and a license cannot be reobtained for life

According to BAC: According to BAC: 80 mg/100 ml > BAC ≥ 20 mg/100 ml: a fine of 500 RMB, 1. 130 mg/100 ml ≥ BAC > 80 mg/100 ml: temporary 6 DOPs, and temporary suspension of the driver’s license suspension of the driver’s license for 5 months, a fine for longer than one month and less than three months of 1800 RMB, and detention for 8–10 days. 2. BAC > 130 mg/100 ml: temporary suspension of the driver’s license for 6 months, a fine of 1800 RMB, detention for 13–15 days, and 12 DOPs

Administrative punishment By vehicle type: By vehicle type: 1. Driving a motor vehicle after drinking: temporary 1. Driving a motor vehicle after drinking: the driver shall suspension of the driver’s license for longer than one be restrained by the public security organs until sober, month but less than three months, with a fine of the driver’s license shall be revoked, criminal 200–500 RMB and 6 DOPs responsibilities shall be investigated according to laws, 2. Driving a commercial vehicle after drinking: temporary and a driver’s license cannot be reobtained within suspension of the driver’s license for three months, 5 years 2. Driving a commercial vehicle after drinking: the driver with a fine of 500 RMB and 12 DOPs shall be restrained by the public security organs until sober, the driver’s license shall be revoked, criminal responsibilities shall be investigated according to laws, and a driver’s license cannot be reobtained within 10 years. After reobtaining a driver’s license, the driver shall not drive a commercial vehicle

Region Penalties for DUI

Table 10.2 Penalties for drunk driving and drunk driving with an extremely high BAC in Guangdong

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Penalties for drunk driving with an extremely high BAC

1. Administrative punishment: A person who is under security punishment and commits another crime within six months shall be given a heavier punishment 2. Criminal punishment: A heavier punishment shall be given to a person who commits another crime within five years of completing his or her sentence or being pardoned (except for crimes of negligence and those involving individuals younger than 18 years old)

Data source Data were obtained from public information

Recidivist punishment

Region Penalties for DUI

Table 10.2 (continued)

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Table 10.3 Penalties for drunk driving with an extremely high BAC in Hong Kong and Macao Region Punishment Macao Up to one-year imprisonment and driving ban for one to three years HK

Fines and imprisonment: Those who are indicted must pay a level 4 fine and are imprisoned for three years; for a first conviction, a level 3 fine is imposed, as well as six months imprisonment; for a second or subsequent conviction, a level 4 fine is imposed, as well as 12 months imprisonment Disqualification: For a first conviction, the person shall be disqualified from driving for at least 2 years; for a second or subsequent conviction, the person shall be disqualified from driving for at least 5 years If the court or magistrate has ordered the person to attend and complete a driving improvement course: For a first conviction, the person shall be disqualified from driving for at least 2 years or until the person has attended and completed the course at his/her own cost, whichever is later; for a second or subsequent conviction, the person shall be disqualified for at least 5 years or until the person has attended and completed the course at his/her own cost, whichever is later

Data source Data were obtained from public information

Macao are 80, 60 and 50 km/h.50 Exceeding the corresponding speed limit is defined as speeding, and fines and driving bans are imposed according to the type of vehicle, the speed and recidivism.51

Review of Cross-Border Road Traffic Safety Policies in Guangdong, Hong Kong, and Macao Cross-Border Road Traffic Safety Management in Guangdong, Hong Kong, and Macao (i)

Traffic safety management for the Hong Kong-Zhuhai-Macao Bridge

Drivers on the main section of the Hong Kong-Zhuhai-Macao Bridge (HZMB) drive on the right side of the road (mainland China). After arriving or leaving the ports of HK or Macao, drivers transition from left to right according to traffic signs. Vehicles must comply with local government requirements, and drivers from HK and Macao must comply with mainland regulations when crossing the bridge. For example, the maximum load limit for freight vehicles in HK is 44 tons, that in mainland China is 49 tons (the total weight of vehicle and cargo), and that in Macao is 38 tons. Drivers and 50 Section

4 of Road Traffic Law of the Macau Special Administrative Region: Speed and supplementary regulations. 51 Article 98 of Road Traffic Law of the Macau Special Administrative Region: Speeding.

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Table 10.4 Penalties and DOP reductions for speeding in Guangdong Road type

Excess of the speed limit (%)

Roads with a speed limit ≤50 km/h

10–20

50

20–50

100

50–70

300

Roads with a speed limit between 50 km/h and 80 km/h

>70

500

10–20

100

20–50

150

50–70

Roads with a speed limit >100 km/h

Driving qualification

500

>70 Roads with a speed limit between 80 km/h and 100 km/h

Fine (RMB)

1000

10–20

150

20–50

200

50–70

1000

>70

1500

10–50

200

50–70

1500

Revocation

>70

2000

Revocation

Road type

Excess of the speed limit (%)

Vehicle type and DOP

All roads

>50

Passenger vehicles, trucks, school buses, dangerous goods transport vehicles (≥ middle-sized vehicle)

Other motor vehicles

12

12

Freeways and urban expressways

20–50

12

6

Roads other than freeways and urban expressways

20–50

6

6

Freeways and urban expressways