Strategic Planning for Urban Transportation: A Dynamic Performance Management Approach (System Dynamics for Performance Management & Governance, 3) 3030368823, 9783030368821

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System Dynamics for Performance Management & Governance 3

Guido Noto

Strategic Planning for Urban Transportation A Dynamic Performance Management Approach

System Dynamics for Performance Management & Governance Volume 3

Series Editor Carmine Bianchi, CED4-System Dynamics Group, University of Palermo, Palermo, Italy

More information about this series at http://www.springer.com/series/13452

Scientific Committee for System Dynamics for Performance Management & Governance Luca Anselmi University of Pisa, Italy—Professor of Public Administration David Birdsell Baruch College/CUNY, USA—Dean, School of Public Affairs Elio Borgonovi Bocconi University, Milan, Italy—Professor of Economics and Management of Public Administration Tony Bovaird University of Birmingham, UK—Professor of Public Management and Policy Dario Cavenago Bicocca University, Milan, Italy—Professor of Public Management Lino Cinquini Scuola Superiore Sant’Anna, Pisa, Italy—Professor of Business Administration Paal I. Davidsen University of Bergen, Norway—Professor of System Dynamics, Chair of the System Dynamics Group John Hallighan University of Canberra, Australia—Emeritus Professor of Public Administration and Governance David Lane Henley Business School, UK—Professor of Informatics Manuel London State University of New York at Stony Brook, USA— Distinguished Professor of Management Luciano Marchi University of Pisa, Italy—Professor of Planning & Control Systems Marco Meneguzzo Università della Svizzera Italiana, Lugano—Switzerland; University Tor Vergata, Rome, Italy—Professor of Public Management Riccardo Mussari University of Siena, Italy—Professor of Public Management Guy Peters University of Pittsburgh, USA—Maurice Falk Professor, Department of Political Science Angelo Riccaboni University of Siena, Italy—Professor of Planning & Control Systems William C. Rivenbark University of North Carolina at Chapel Hill, USA—MPA Program Director, School of Government Etienne Rouwette Nijmegen School of Management, The Netherlands—Associate Professor of Research Methodology and System Dynamics Salvatore Rotella Riverside College, California, USA—Chancellor Emeritus and Professor of Political Science Khalid Saeed Worcester Polytechnic Institute, USA—Professor of System Dynamics Markus Schwaninger University of St Gallen, Switzerland—Professor of Management Carlo Sorci University of Palermo, Italy—Professor of Business Management Jürgen Strohhecker Frankfurt School of Finance & Management, Germany— Professor for Business Administration, Operations and Cost Management Jarmo Vakkuri University of Tampere, Finland—Professor of Local Government Accounting & Finance Wouter Van Dooren University of Antwerp, Belgium—Associate Professor of Public Management David Wheat University of Bergen, Norway—Professor in System Dynamics

Guido Noto

Strategic Planning for Urban Transportation A Dynamic Performance Management Approach

Guido Noto University of Messina Messina, Italy

ISSN 2367-0940     ISSN 2367-0959 (electronic) System Dynamics for Performance Management & Governance ISBN 978-3-030-36882-1    ISBN 978-3-030-36883-8 (eBook) https://doi.org/10.1007/978-3-030-36883-8 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To my mother

Abbiamo tutti dentro un mondo di cose: ciascuno un suo mondo di cose! E come possiamo intenderci, signore, se nelle parole ch’io dico metto il senso e il valore delle cose come sono dentro di me; mentre chi le ascolta, inevitabilmente le assume col senso e col valore che hanno per sé, del mondo com’egli l’ha dentro? Luigi Pirandello, Sei personaggi in cerca d’autore

Preface

This book brings together several investigations conducted during my Ph.D. studies, as well as my subsequent experience with public management and urban transportation systems. Transportation, intended as the ability of people and goods to move from one place to another, is a core element in addressing the good performance of cities and in pursuing sustainability. This book focuses on the transportation of people. The significance of transportation in cities is related to direct, indirect, and induced effects on the economy, as well as on the development of urban areas. As such, in Western countries, urban transportation services and infrastructure are usually conceived as public issues and therefore managed, or at least regulated, by the public administration. The importance of public intervention in urban transportation is enhanced by the uncontrolled urban growth, which in the last fifty years has characterized cities all over the world. In fact, if, on the one hand, population is growing at an exponential rate, on the other, more and more people are moving from rural areas to urban ones. This is determining rapid growth in demand for transportation systems and represents a serious challenge for policy makers at the urban level (e.g., mayors, city councils, and public managers) who are called to cope with it. Planning and managing urban transportation systems requires the ability to deal with dynamic complexity. The latter is determined by the presence of a massive number of travelers with different needs, a thick infrastructure network, different travel modes, multiple decision makers, and the time delays occurring from the decisions made and their effects. The complexity of urban transportation, as well as the complexity of the public sector in general, can be framed through three main features, namely pluralism, institutional fragmentation, and scientific uncertainty. Pluralism concerns the different needs and expectations of the community in relation to specific problems. In transportation systems, for instance, different target populations may demand different mobility services. Similarly, different businesses may perceive the adequacy of infrastructure in different ways (and so forth). Institutional fragmentation characterizes public service provision when more than one institution is involved. This could be the case of a metropolitan area in ix

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which public services are run by several providers operating under the standard requirements of different public authorities. This may lead to pressures and constraints arising from different institutional levels, with diverse—and sometimes conflicting—goals and service expectations. These pressures and constraints are mainly due to ownership relationships and the asymmetric exchange of resources and contracts. Scientific uncertainty concerns fragmentation and gaps in reliable knowledge. This permeates urban systems in numerous aspects, among which we may identify environmental impacts and citizens’ behavior. Today, through the help of new and sophisticated tools, we may develop a good understanding of how cities work. However, we would never be able to predict their future—with specific reference to the long term—due to the social dynamic complexity that characterizes them. These characteristics determine what existing literature defines as “wicked” problems. The term “wicked” does not mean “evil,” but rather refers to issues that are difficult to define and manage because they often lead to counterintuitive behaviors in terms of time (trade-offs between the short and long term) and space (trade-­ offs between different institutions) when actions are taken to resolve them. As such, wicked problems undermine policy makers’ abilities to plan, manage, and control public services toward the creation of value for the reference community. The concept of “public value” was introduced by Moore in 1995 and can be defined as a multidimensional construct created by government through services, the regulation of laws, and other actions. Creating public value implies the delivery of public services aligned with community expectations, and hence the achievement of “outcomes” and the generation of trust and fairness. Consequently, in an environment characterized by pluralism, fragmentation, and uncertainty, the process of value creation requires collaborative forms of organizing and managing the public sector that also include engagement and the collaboration of stakeholders and the public at large. Traditionally, urban transportation has been the exclusive domain of transportation engineers, transportation economists, and urban planners. Their studies, although relevant to policy makers and scientifically sound, do not necessarily consider the presence of the highly fragmented institutional structure that characterizes the subject. Thus, the solutions provided may encounter barriers to implementation and/or produce unexpected results owing both to the interaction of a network of organizations participating in service delivery (which may have conflicting interests and expectations) and to the unpredictable behavior of users (e.g., their willingness to adopt new infrastructure). In order to deal with the multiplicity of stakeholders who characterize the urban transportation sector, as well as to avoid unexpected performance results, it is important to include in one’s analysis their interests, expectations, and power to influence the overall system. To do so, the adoption of a strategic management perspective is suggested here. The aim of this work is to discuss the contribution of strategic and performance management to the planning and management of urban transportation systems. In particular, this book suggests the adoption of a dynamic performance management

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(DPM) approach to support urban transportation decision makers at both the managerial and political level. DPM allows one to embrace a dynamic and systemic perspective and, as a result, to frame the contributions of different stakeholders in terms of outcome-based performance at an inter-institutional level. Chapter 1 provides the reader with an in-depth exploration of the literature on strategic planning and urban transportation planning, outlining a number of critical issues and challenges in current planning practices. In particular, the chapter focuses on the evolution of urban transportation planning in Western countries and discusses the main challenges and opportunities. Chapter 2 proposes and discusses the opportunity to adopt DPM as a supporting method for strategic urban transportation planning. Furthermore, special attention is given to the identification of performance indicators for urban transportation. Chapter 3 focuses on stakeholder analysis as a key phase of every strategic urban transportation planning process. This chapter is mainly addressed at overcoming the issues related to the institutional fragmentation mentioned above. Chapter 4 regards the strategic planning phases of diagnosis, design, and implementation. In particular, a general model of transportation in cities is provided and analyzed with regard to its main components. This book would not have been possible without the strong support, both scientific and human, of many people. First of all, I would like to thank Carmine Bianchi from the University of Palermo for his guidance throughout this project and for being a wise mentor of my academic pathway. His research has significantly contributed to the conceptual framework on strategic and performance management developed in this book. I am deeply thankful to Federico Cosenz from the University of Palermo for his friendship, mentoring, and insightful suggestions. The research we conducted together represented a key moment of my personal and professional growth. I am indebted to Sabina Nuti and Milena Vainieri from the Scuola Superiore Sant’Anna for their trust and support during my postdoctoral research experience and for being outstanding examples in terms of motivation and proactivity. I wish to thank Pablo Bereciartua from the University of Buenos Aires for his invaluable support during my research experience in Argentina, which represented a pivotal moment in the development of this book. I am also indebted to Enzo Bivona from the University of Palermo who trasmitted to me passion and guidance with regards the topic explored in this book. I am deeply thankful to the colleagues from the University of Messina for having welcomed me with enthusiasm and for providing me with the opportunity to work together. I would like to thank my former colleagues from the MeS Laboratory of the Scuola Superiore Sant’Anna, with whom I could share ideas, teaching and research experiences. I especially wish to thank Elisa, Giuseppe, and Tommaso. The book reviewers also deserve special thanks. Special thanks go to my whole family. Messina, Italy  Guido Noto

List of Abbreviations

CABA CLD DPM GMB ICT LUTI NPM PA PEST PM RBV RMN SD SFD SWOT

Ciudad Autónoma de Buenos Aires Causal loop diagram Dynamic performance management Group model building Information and communications technology Land use–transport interaction New public management Public administration Political, economic, social, and technological Performance management Resource-based view Región Metropolitana Norte System dynamics Stock and flow diagram Strengths, weaknesses, opportunities, and threats

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Contents

1 An Introduction to Urban Transportation Strategic Planning��������������    1 2 A Dynamic Performance Management Approach to Support Urban Transportation Planning ��������������������������������������������������������������   29 3 Governance and Stakeholder Management in the Urban Transportation Sector��������������������������������������������������������������������������������   61 4 Modelling Urban Transportation System Through Dynamic Performance Management��������������������������������������������������������   93 Index������������������������������������������������������������������������������������������������������������������  123

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Chapter 1

An Introduction to Urban Transportation Strategic Planning

Urban areas’ economic and social health depends to a large extent on the performance of their transportation systems (Meyer and Miller 2001). An urban transportation system is one of several interrelated subsystems (e.g., energy supply, waste management, public safety, water supply, etc.) that characterize urban areas and that primarily rely on public sector governance and performance. Urban transportation, by definition, deals with transportation in cities. Transportation can be categorized along two main dimensions: people and goods. This work is specifically focused on the transportation of people in urban areas. The key challenge that modern urban transportation system faces is related to the need to address sustainable development (Bertolini et al. 2008). Sustainable development can be seen as the ability to seek desired outcomes without compromising the ability of others to achieve their own in space and time (United Nations 1987). Pursuing sustainable development is a complex task that requires clear and well-­ defined management processes and practices to deal with the key characteristics of modern social systems, namely institutional fragmentation, pluralism, and uncertainty (Head and Alford 2015). As such, in recent decades, urban transportation planning has evolved from a traditional approach, which mainly conceived transportation plans as prescriptive and regulatory tools, to a process aimed at supporting policy makers in addressing sustainable policies in a flexible and comprehensive way. This shift has mainly implied the need to combine traditional urban transportation planning approaches with strategic and performance management counterparts, which emerged in the public management literature during the 1980s (Bryson 1988; Poister 2010). The result of this combination has been the establishment of what is here termed “urban transportation strategic planning.” Compared with traditional planning methods, which rely on the “predict and provide” approach, strategic planning necessitates consideration of the institutional characteristics of the environment being analyzed, i.e., the urban transportation system (Bertolini et al. 2008). Indeed, traditional urban transportation approaches are

© Springer Nature Switzerland AG 2020 G. Noto, Strategic Planning for Urban Transportation, System Dynamics for Performance Management & Governance 3, https://doi.org/10.1007/978-3-030-36883-8_1

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top-down, implying that a high level of governance (e.g., the city council) is in charge of analyzing the system (predicting) and defining policies and actions ­(providing), without involving key stakeholders who contribute to the overall performance of the urban transportation system. This has often led to implementation problems owing to the lack of legitimacy and feasibility of the proposed actions (Meyer and Miller 2001). In contrast, strategic management approaches are, according to Poister (2010), oriented at the following: (a) defining policies and actions through a process aimed at assessing the environmental features that characterize the analyzed context by engaging with the various governance levels and key stakeholders; (b) measuring and monitoring the performance results achieved through the actions implemented for a twofold objective, i.e., designing corrective actions if required and for accountability purposes. To the best of the author’s knowledge, few existing contributions have explored the practical implications, challenges, and opportunities related to the adoption of such a strategic approach (see, for instance, Meyer and Miller 2001; Bertolini et al. 2008). This book thus seeks to advance knowledge in this direction. The current chapter explores the process of urban transportation strategic planning, with the aim of answering three key questions based on extant literature on the topic. These questions are: –– What is urban transportation planning and how has it evolved during the last four decades? –– Who are the players involved and what is the role of public sector organizations in this process? –– How might a strategic planning process for urban transportation be developed? This chapter is organized into three sections aimed at addressing each of these questions.

1.1  W  hat Is Urban Transportation Planning? Evolution, Opportunities, and Challenges The purpose of urban transportation planning is to provide the information necessary to support policy makers in designing and implementing urban transportation strategies to achieve desired outcomes (Banister 1994; Meyer and Miller 2001). Urban transportation systems are part of the wider urban system, which comprises other subsystems such as water supply, telecommunications, and energy. A transportation system consists of different components (Meyer and Miller 2001). The first component is constituted of the users who represent the key drivers of travel behavior. The second major component is the transportation mode. The characteristics of each mode, together with users’ adoption rate, have a significant impact on the transportation system’s performance. A third component is represented by the infrastructure that provides the modal networks, facilities, and services necessary for mobility in the urban area. Connected to infrastructure are the intermodal connections (e.g., place where movement occurs between modes) that

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guarantee an effective multimodal transportation system. Another key component is represented by the wide set of stakeholders who affect and are affected by transportation in cities. Transportation planning requires three main coordinated actions (Meyer and Miller 2001): managing transportation system supply, managing transportation demand, and managing land use. Managing transportation system supply refers to actions regarding infrastructure, facilities, or services to improve system performance. However, this activity is related via a feedback loop (i.e., a double link) to transportation demand (i.e., adding supply to accommodate demand may foster demand itself). Therefore, actions to manage demand (so as to influence the intensity, timing, and distribution of transportation) are required. These actions that directly affect the habits and behaviors of travelers (both real and potential) seek to reduce or redistribute the transportation demand peak, thereby avoiding congestion during rush hours. In recent decades, studies on transportation planning have focused on the transportation system in a broad way, considering not only transportation per se, but also land use as a key driver of the performance of urban areas’ transportation systems. In the long run, the spatial distribution of land use can profoundly influence urban transportation, and land-use distribution may in turn be influenced by the accessibility of transportation (Meyer and Miller 2001; Waddell 2002). These three coordinated actions should be implemented within a complex and constantly evolving environment. According to Meyer (2000) and Meyer and Miller (2001), four key challenges with which urban transportation planning should deal with can be identified: (a) changes in society, i.e., changes in population behavior determined by changes in the demographic, market, and technological characteristics of society; (b) evolution in planning policy mandates; (c) changes in the technology of planning that day by day allow us to better understand and communicate complex urban phenomena that affect transportation; and (d) changes in the institutional framework characterizing the urban transportation sector. Each of these challenges is explored in detail here in order to define their characteristics and to discuss opportunities pertaining to a shift in the planning paradigm toward strategic and performance management.

1.1.1  Changes in Society Travel needs and citizens’ behavior have a strong influence on urban transportation performance. They are contingent on the interaction of many different factors (such as age, income, and household structure) that are beyond the direct control of the agencies in charge of regulating and managing transportation in cities. Ongoing urbanization is probably the main phenomenon affecting transportation in cities all over the world. In recent decades, while the world population has been growing exponentially, more people have moved from rural to urban areas (United Nations 2014) causing a rapid urbanization trend. Data demonstrate that in 1950 about 30% of the world’s population was living in urban areas, while in 2007 this

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figure was as high as 50%. Moreover, the United Nations (2014) forecasts that by 2050 about 70% of the world’s population will be living in cities. The implications of these phenomena are clear. On the one hand, the creation of urbanization economies may support both economic development and service delivery in a less scattered area. On the other hand, the consequences of urban issues (such as uncontrolled urban growth, segregation, and so forth) risk negatively impacting the quality of life of a rising number of people. Figure 1.1 shows historical urbanization trends and those forecast in the world’s major areas. This world dynamic represents a key global trend that is affecting transportation demand in cities. However, this phenomenon should be linked at the local level with other relevant social factors, such as household number and composition, rising

Fig. 1.1  Urban and rural population as the proportion of total population, by major areas, 1950– 2050 (United Nations 2014)

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unemployment, aging population, the dissemination of information and communication technologies (ICT), and land use. All of these factors may determine changes in travel needs through changes in travel behaviors. For example, job growth and households’ growing incomes in many countries after World War II (see, for instance, the USA, many European countries, Canada, and New Zealand) created market demand for personal transportation, which was mainly satisfied by private modes (e.g., cars). Another example related to societal changes is the percentage of households with Internet connection, which in many countries has increased very rapidly; for instance, in Italy the proportion raised from 34.5% in 2005 to 69.2% in 2016. During the same period, many public institutions implemented e-government practices to allow citizens to conduct their activities with public administration (PA) through the web. Moreover, western countries have seen increased usage of e-commerce platforms, which enable people to do their shopping online. All of these trends combined have had the general effect of reducing travel demand related to specific needs (e.g., shopping). At the same time, other transportation needs and flows have emerged for the same reasons (e.g., the shipment of products bought online). The emergence of new technology (like artificial intelligence, robotics, etc.) may additionally alter the modes and provision of some transportation services (e.g., driverless vehicles). Travelers’ behavior is also necessarily connected to land-use characteristics, such as where businesses, art/cultural and leisure centers, and other major infrastructure (e.g., stadiums) are located. For example, the demand for new infrastructure—such as investment in a new center of business development or a new stadium in a specific part of a city—has immediate and direct effects on the travel behaviors of the groups of people affected by the decision (e.g., tourists, sports fans, etc.). In recent years, many cities have experienced such investments, and the shape of urban areas has significantly changed. Even though such developments are well known in advance, urban transportation planning is required to examine and make explicit the expected behavioral and attitudinal changes. In order to manage such phenomena and to avoid their negative consequences, public administrations are required to understand the social environment so as to take action to support the economic, social, and environmental sustainability of transportation in cities.

1.1.2  E  volution in Planning Policy Mandates: The Path Toward Sustainable Transportation The evolution of transportation planning is closely related to the evolution of society’s concerns and issues. In western countries, the early 1960s were characterized by the need to improve road network extensiveness, hence the planning process focused on where to build

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new highways to accommodate a growing demand for automobile use (Meyer 2000; Meyer and Miller 2001). In the late 1960s–1970s, policy agendas witnessed an increasing societal awareness of the environmental consequences of transportation, and thus urban transport system planners became more concerned with the impacts of transport facilities on air, land, and water. This shift was also accompanied by a more inclusive approach to planning with respect to stakeholders and other community groups. The importance of economic growth and growing connectivity to global markets stimulated new planning emphasis in the 1980s and 1990s on freight movement and intermodal connections. Today, the potentially varying impact of transport accessibility and mobility on different groups in society has led to a planning emphasis on sustainability and sustainable development. Due to the rising challenges that urban transportation systems should negotiate (i.e., changes in travel needs and behavior), many authors have identified the importance of adopting a sustainability perspective when managing and planning the functioning of these systems (Meyer 2000; Bertolini 2007; Bertolini et al. 2008). The concept of sustainable development emerged in the 1980s with progressive awareness of systematic relationships between human society and the natural environment. The concept places greater attention on human impacts and the fact that our future is partly contingent on our current decisions (Litman 2016). Sustainability has been defined as the ability to meet the needs of the present generation without compromising the ability of future generations to meet their own needs (United Nations 1987). Due to the relationship that relates transportation to the other activities and dimensions of urban development, such a notion should consider both time (e.g., future generations) and space (i.e., the ability of other subsystems to achieve their needs). Adoption of the sustainable development paradigm in planning practice offers guidance to ensure that individual decisions balance economic, social, and environmental objectives, taking into account indirect, distant, and long-term impacts (Litman 2016). Adopting such a perspective requires cities to generate better outcomes, such as providing employment opportunities, expanding necessary infrastructure, ensuring equal access to services, and preserving natural assets within the city and surrounding areas (United Nations 2014). Growing attention to sustainability in transportation planning practices has resulted in what literature and practice call “sustainable urban transportation.” This has become the key objective among policy makers dealing with transportation in cities. The main definitions of sustainable urban transportation provided are displayed in chronological order in the table below (Table 1.1). These definitions agree on the two main features characterizing sustainable urban transportation: a focus on improving quality of life through satisfying people’s mobility needs, and an orientation toward future generations. The key ­concepts that occur frequently in these definitions are “access to mobility,” “resource use,” and “ecosystem protection.” In order to provide a deeper explanation of what sustainable urban transportation means in practice, these concepts are explored in detail.

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Table 1.1  Sustainable urban transportation definitions Source Organization for Economic Co-operation and Development (OECD) (1998) May et al. (2001); Black et al. (2002)

Center for Sustainable Transportation (CST) (2005)

Foro de Transporte Sostenible de América Latina (2011)

Song et al. (2013)

Definition Sustainable urban transportation does not endanger public health or ecosystems and meets needs for access consistent with (a) use of renewable resources at below their rates of regeneration, and (b) use of non-renewable resources at below the rates of development of renewable substitutes A sustainable urban transport and land use system: Provides access to goods and services in an efficient way for all inhabitants of the urban area; protects the environment, cultural heritage, and ecosystems for the present generation; and does not endanger the opportunities of future generations to reach at least the same welfare level as those living now, including the welfare they derive from their natural environment and cultural heritage Sustainable urban transportation allows the basic needs of individuals and societies to be met safely and in a manner consistent with human and ecosystem health. Is affordable, operates efficiently, offers choices of various transportation modes, and supports a booming economy. Limits emissions and waste so that plants are able to absorb them, minimizes consumption of non-renewable resources, limits consumption of renewable resources to the sustainable level, reuses and recycles its components, and minimizes noise pollution and use of land Sustainable urban transportation regards the provision of services and infrastructure for the mobility of goods and people, needed for economic and social development and for improving quality of life and competitiveness. These services and transport infrastructure provide safe, reliable, economical, efficient, equitable, and affordable mobility while mitigating the negative impacts on health and the local and global environment, in the short, medium, and long term without compromising the development of future generations Sustainable transportation systems imply balancing current and future economic development, social qualities, and environmental preservation

Regarding “access to mobility,” a sustainable urban transportation system should guarantee that every person is equally able to decide where and when to travel. This has two main implications. The first is related to a geographical dimension: a transportation network1 sufficiently widespread to cover all different urban areas taking into account the needs and density of travelers. The second is related to an equity dimension: each citizen should be able to afford to travel in the city, so as to accomplish his or her duties and needs (e.g., as a worker, a student, a family member, etc.). Solutions to accessibility problems may vary and depend on the social, geographical, and social characteristics of each urban area. When dealing with ­sustainable urban transportation, solutions directed to specific issues must also take into account how the other interrelated factors of the mobility system would react to changes. For example, widening the transportation network may incentivize people to move to a 1  A transportation network is a kind of graph made up of nodes (e.g., rail stations, bus stops) and links (routes) that are always interconnected (Black 1995)

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particular urban area, thereby creating new demand for transportation. Likewise, reducing prices (e.g., bus tickets) for a specific group of citizens may increase travel demand. The second aspect emphasized by sustainable urban transportation definitions is the responsible use of resources. The transportation of people and goods accounts for about 25% of the world’s total energy consumption. In 2012, over 100 quadrillions Btu were consumed by the transportation sector all over the world, and 150 per year are expected to be consumed by 2040.2 Considering that about 96% of these come from petroleum and other liquid fuels (i.e., non-renewable resources), addressing energy use in the transportation sector is clearly urgent. A sustainable use of resources means avoiding the use of non-renewable resources while using renewable resources below or at the rates of their production. Reading the above data, we can assert that today’s world is still far from reaching the goal of sustainable use of resources in the transportation sector. According to the World Bank (2012), this can be pursued in three ways: • Reducing travel demand. This can be primarily achieved through the effective and extensive activity of urban spatial planning. In fact, providing urban areas with basic services and facilities allows citizens to run their daily activities (working, shopping, enjoying leisure) without the need to travel around the city. ICT is also playing an important role in this regard. Through the use of such technologies, it is now possible to perform some activities online that in the past had to be undertaken physically (e.g., e-government, e-commerce, e-banking, etc.). • Optimizing use of vehicles. Considering how each motorized trip has an impact on emissions and resource consumption, the more people each trip carries, the less global impact transportation would have. Therefore, stimulating mass transportation (e.g., bus, train, metro) and social innovation (e.g., ride-sharing, car-­ pooling) would represent a further step toward the sustainable use of resources. • Adopting new technologies that use energy from renewable resources and reduce energy consumption. Converting the urban fleet into more efficient vehicles (e.g., electric cars) represents another example of progress that policy makers may pursue with regard to energy consumption. None of these measures alone is capable of resolving the resource consumption problem. A significant impact may be reached only by implementing them all simultaneously. The last key concept highlighted in the sustainable urban transportation definitions is “ecosystem protection.” This is an expression that embraces numerous aspects, ranging from the emission of harmful substances to the use of land, and from the defense of natural and cultural heritage to noise. In the last 40 years, the environment has constituted one of the main concerns of policy makers and the public alike. The transportation sector is a significant source of pollution, especially of the air. In the majority of cities around the world, motor

 Data from U.S. Energy Information Administration (2016) available at https://www.eia.gov/

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vehicles cause most of the air pollution, or at least contribute significantly (Black 1995). Transportation produces about 23% of global CO2 emissions from fuel combustion (Ribeiro et al. 2007). Furthermore, rapid urbanization in developing countries has rendered transportation the fastest-growing consumer of fossil fuels and the fastest-growing source of CO2 emissions (Ribeiro et al. 2007). As in the previous aspect (resource utilization), the key activities required to tackle these externalities are related to a reduction in travel demand, the optimization of vehicle use, and the adoption of new technologies (World Bank 2012). The features characterizing sustainable urban transportation call for the adoption of a systemic view when tackling urban transportation-related problems, with the aim of balancing the trade-off between the objective of guaranteeing access to mobility in terms of effectiveness and equity, and the need to manage resources in a sustainable way. This will be explored in depth in the following chapters of the book.

1.1.3  Changes in the Technology of Planning Of considerable significance in the evolution of urban transportation planning was the revolutionary use of the computer, not only in society in general but specifically with application to planning activities. The ability of transportation planners to analyze and evaluate transportation systems is highly dependent on the tools and methods available to collect data, model transportation system performance, analyze results, and communicate this information to those making decisions, as well as to the wider community for accountability purposes. The rapid evolution in computer processing capabilities has had as significant an impact on transport planning as it has had on other aspects of society (Chang and Meyers 1999). More powerful computers have allowed transportation planners and modelers to process larger quantities of data and utilize more complex mathematical relationships that describe the dynamic behavior of urban travel flows and the interaction between the transport system and land use (Southworth 1995). Since the 1960s, transportation modelers have started to develop new generation models capable of gathering the multiple-feedback processes existing in urban transportation systems, with specific reference to those relating mobility issues with economic and spatial issues, e.g., IRPUD (Wegener 1998), DELTA (Simmonds 1999), UrbanSim (Waddell 2002), MARS (Pfaffenbichler 2008; Pfaffenbichler et  al. 2010) and other agent-based models. However, many of these encountered implementation problems (Wegener 1994; Saujot et  al. 2016). Therefore, many researchers and modelers (Vonk et  al. 2005; Brömmelstroet and Bertolini 2008; Waddell 2011) highlighted the need to better connect with end users and ­stakeholders in general (e.g., public agencies, transport authorities, private companies). To respond to this limit, the effective use of these sophisticated models should be enhanced through their integration within a strategic planning process. Another relevant change of technology that is having a wide impact on transportation planning and modeling pertains to the availability of so-called big data and

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the ability to process them. Compared with traditional data sets, big data typically include masses of unstructured data that require more real-time analysis (Chen et al. 2014). Today, planners and decision makers have access to a growing amount of information stored in data sets from different sources of differing levels of quality. These data could be generated by third parties (e.g., users of a web app such as Google Maps, Twitter, or Instagram), or by a voluntary collection process (e.g., applying sensors to monitor traffic). The availability of new data and sources of data characterizes all public and non-public organizations operating in modern times. Big data analysis increases policy makers’ access to detailed and on-time information about the functioning of the urban transportation system. However, there are risks related to decision-making processes. In fact, the availability of a huge amount of data from different sources may disorient decision makers and planners when developing analysis aimed at designing policies if not properly structured and framed according to a strategic and performance-­based view. The risk is that of producing data-driven actions that are incoherent with urban system strategies. With respect to the utilization of new technologies, it is therefore essential to tackle two key potential issues. First is to develop methods of checking the quality of data, both in terms of completeness and values. Contemporary data sets are often broad and derived from different sources, which cannot always be controlled through regulation in terms of quality prescription and completeness. For instance, data coming directly from users (e.g., car drivers) may poorly represent population dynamics as they cut out all of those who did not contribute to the data set for different reasons (use of a different mode of transportation, unwillingness to participate, unavailability of the tools to participate, culture, etc.). Nevertheless, such data could be particularly relevant for addressing some issues at various levels of governance. Second is to develop a performance management framework that includes all information related to key stakeholders’ expectations and that may effectively support urban transportation planning.

1.1.4  C  hange in the Institutional Framework Characterizing the Urban Transportation Sector Urban transportation systems are characterized by a highly fragmented governance structure (Van de Velde 1999; Tornberg and Odhage 2018) and operate in a complex environment defined by a large number of travelers, a thick infrastructure network, and different travel modes (Song et  al. 2013), such as cars, buses, trams, metro, bicycles, and walking. Traditionally, urban transportation planning was deemed an exclusive domain of city councils and was mainly focused on providing top-down solutions to deal with

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travel demand, transport supply, infrastructure efficiency, and so on. Until the end of the twentieth century, few studies considered the wider institutional framework characterizing the transportation sector (Meyer and Miller 2001). However, in today’s world, consideration of this aspect is salient in order to deal with the legitimization of the planning process and to guarantee the successful implementation of the actions designed. The units of analysis of urban transportation planning processes are the urban and metropolitan areas being considered. The decisions that are made at this level of analysis cannot be linked to a single player, but are made at different governance levels and involve a wide set of stakeholders. The main actors who are expected to intervene in order to govern the opportunities and challenges discussed in the previous sections are public organizations and, in particular at the urban level of municipalities (or city councils), so-called promoters (Cavenago and Margheri 2006). However, planning processes should also involve all key stakeholders and governance levels that belong to the fragmented governance structure that characterizes urban transportation systems. These belong to both the public and private sectors. The fragmentation of the public sector is related to the various institutional reforms that transformed the organizational structure of public administrations (PAs) from hierarchical to market-oriented (Hood 1991; Borgonovi 2005) and, in recent years, to collaborative governance (Moore 1995; Kettl 2002; Bovaird 2005; O’Flynn 2007; Turrini et al. 2010). The private sector has gained the legitimacy to participate in planning processes, both due to the reforms mentioned above—which often implied the privatization of public services—and because of the rising need to empower the community through engagement and accountability practices (Borgonovi and Mussari 2011). Given the relevance of these matters for the aim of this book, the evolution of the public sector and the institutional framework characterizing the transportation sector are analyzed in depth in the following section.

1.2  W  ho Takes Care of Urban Transportation Planning? The Institutional Fragmentation Characterizing the Context in Which Planning Processes Are Designed and Implemented 1.2.1  Institutional Fragmentation and Public Sector Reforms As mentioned in the previous section, the unit of analysis of urban transportation planning processes is the urban area. This does not correspond to any single institution because it comprises a wide set of stakeholders operating at various levels. Therefore, it is clear that urban transportation planning processes are designed and

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implemented in an inter-institutional context (Bianchi 2012, 2016; Noto and Bianchi 2015). The interests and expectations of the set of stakeholders involved in urban transportation systems are heterogeneous. In this sense, local public organizations are probably the ones whose interests are perceived as being closer to those of the urban area, and at the same time those with the stronger levers to implement policies and actions. Due to that, these organizations are those who are expected to take the lead in planning processes. This section is aimed at describing the institutional context in which local public organizations currently operate, retracing the public sector reforms of the last few decades. PA reforms have always been designed to respond to the challenges and shortcomings of the institutional settings that were previously in place (Bryson et  al. 2014). Until the 1980s, western countries’ PAs adopted hierarchical forms of organization and control so as to respond to a particular set of conditions, including industrialization, urbanization, the rise of the modern corporation, faith in science, belief in progress, and a concern with major market failures (Bryson et al. 2014). These forms were shaped in terms of the typical bureaucratic administration model, conceptualized by Weber in the first decades of the 1900s. In order to avoid a political or individual influence across PAs, bureaucracy models were primarily concerned with input monitoring and process compliance, rather than with achieving results. This limited the administrations’ ability to develop decision-making processes that were performance-oriented. Another feature of the traditional bureaucratic approach was represented by the tendency to foster specialization, rendering every department a cultural fortress and thereby creating “vertical silos”(Christensen and Laegreid 2007; Head and Alford 2015). In the early 1980s, the New Public Management (NPM) reform was implemented so as to respond to a concern with government failures and a belief in the efficiency of markets (Hood 1991; Bryson et  al. 2014). Indeed, the reform sought to solve shortcomings with regard to efficiency and effectiveness, which are intrinsic to the traditional bureaucratic approach described above (Hood 1991; Borgonovi 2005). NPM was characterized by four reinforcing trends (Hood 1991; O’Flynn 2007): slowing down or reversing government growth; privatization and quasi-­privatization; automation in the production and provision of public services; and an international agenda focused on public policy and management issues. These have been articulated in greater depth by Hood (1991), who identified seven main doctrinal pillars around which NPM reforms were designed: • Hands-on professional management: the idea is to attribute more power and responsibilities within the organization and to top officials. According to NPM, these figures are managers, who are thus required to make decisions (a role which had previously been entirely in politicians’ hands) and are responsible for the results achieved. • Explicit standards and measures of performance: a culture of (quantitative) measurement is transferred to PAs. Public organizations are obligated to implement

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•

•

•

• •

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a performance management system in which the end results and performance indicators are clearly identified. Greater emphasis on output controls: in order to improve efficiency and effectiveness, resource allocation is linked to measured and reported performance. The idea is to gain (both human and capital) resource flexibility, breaking up centralized bureaucracy-wide resource management. Shift to disaggregation of units: this implies the breaking up of PAs into “manageable” units with the aim to attribute individual responsibilities throughout organizations. This allows public organizations to adopt and put in place management control practices. Shift to greater competition: creating markets (either open or regulated) for public services in which the services are devolved to the company (whether private or public) demonstrating higher levels of efficiency or in which people are enabled to choose the provider (either private or public) that best meets their expectations. Emphasis on the private-sector style of management: introducing greater flexibility for hiring and rewards (e.g., bonuses). Stress on greater discipline and parsimony in resource use: spending review by cutting direct cost and raising labor discipline, with the principle of “doing more with less.”

The practical application of NPM suffered from a range of weaknesses attributable to implementation issues and fundamental tensions (O’Flynn 2007). Although NPM succeeded at superseding the bureaucratic model in various ways, competitive government models tended to create isolation and competition among programs and departments within the public sector, thereby reinforcing the “silo” structure already in place (Christensen and Laegreid 2007; Head and Alford 2015), and in some cases even leading to destructive behavior (O’Flynn and Alford 2005; O’Flynn 2007). NPM fostered public agencies’ fragmentation and isolation, which had already been initiated through the traditional public administration paradigm (Head and Alford 2015). The main issues characterizing the implementation of NPM reforms can be summarized as follows (see Pollit 2003): contradiction and tension between different policies, reducing their effectiveness; duplication and contradiction of action programs, negatively affecting the use of resources; lack of synergy among stakeholders; and fragmented services provided to citizens. NPM was also unable to respond to the new material conditions and challenges of recent years (Bryson et al. 2014) related to increased environmental complexity, including changes in terms of demand for transportation services related to migration dynamics. In this new context, Moore (1995) identified the need to shift away from the competitive paradigm, with its focus on results and efficiency, toward the achievement of a broader goal, namely “public value” creation. Public value is a multidimensional construct created by government through services, law regulation, and other actions (Kelly et al. 2002), primarily resulting from government performance (Moore 1995; Bryson et al. 2014). Public value creation is

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determined by the delivery of services, the achievement of outcomes, and the generation of trust (O’Flynn and Alford 2005; O’Flynn 2007). The outcome focus characterizing the emerging paradigm has been observed by several theorists (Moore 1995; Stoker 2006; O’Flynn 2007; Bryson et  al. 2014; Cuganesan et al. 2014). Outcome achievement in the public context does not depend exclusively on the action of single organizations, but rather requires more collaborative effort (Broussine 2003; Stoker 2006). As such, the public value management paradigm is based on the proposition that a wide range of stakeholders are legitimized and should be included and involved in the PA activity (Stoker 2006; O’Flynn 2007). Recent literature (Borgonovi and Mussari 2011; Cuganesan et al. 2014) has highlighted a renewed focus on networks in which different yet interrelated agencies combine in joint policies in the creation of public value. For instance, in many urban areas, new institutional arrangements to support the “meta-governance” of public services are emerging (Sørensen and Torfing 2009). These are aimed at coordinating different organizations toward the achievement of shared outcomes. This trend should be supported by the development of new accountability and performance management tools, with a shift from a narrow organizational perspective toward a more systemic perspective (Moore 2013; Cuganesan et al. 2014). Four key propositions characterize public value management (Stoker 2006; O’Flynn 2007): (a) public interventions are defined by the search for public value (in contrast to a market failure justification); (b) a wide range of stakeholders have legitimacy and should be included and involved in government activity, for example municipalities, public or private companies providing transportation services, infrastructure owners, firms, communities, and third sector organizations; (c) the adoption of an open-minded relational approach to procurement (which calls for pragmatism), such as adapting procurement practices to the institutional context, the service and the needs of the reference community; and (d) public service delivery is characterized by an adaptable, learning-based approach. The new paradigm requires public organizations to adopt a new or revised set of managerial tools to pursue the above key propositions. Among these, we mainly see the need for a new approach to strategy formulation (Bryson 2018) and the need to develop new performance management systems (Moore 2013; Cuganesan et al. 2014).

1.2.2  The Governance of Urban Transportation The local public transportation sector has not escaped the waves of reforms that have characterized public management in recent decades. We can identify two phases in the evolution of local public transportation, coherent with the public management trends already explored. According to the NPM reform trend, the first phase was characterized by a common path of changes oriented toward the growing usage of some form of competition (van de Velde 1999). This determined the emergence of new service organizational

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settlements that were output-oriented and more independent from political control (Della Porta and Gitto 2013). The reform implied the replacement of traditional mechanisms of transportation regulation that were mainly focused on the social function of the service by a market orientation and a performance management approach (Della Porta and Gitto 2013). Competition and privatization have been implemented with the aims of reducing costs and pursuing efficiency. NPM reforms established an output-oriented culture that revolutionized service provision. However, these new regulations have often failed to support transportation systems in pursuing outcome results such as traffic congestion problems or pollution (Goodwin 1997; Della Porta and Gitto 2013). The public transportation arrangements that emerged in this period were framed by van de Velde (1999) into two main categories: authority initiative and market initiative. This distinction refers to the organization of the supply of public transportation services and pertains to the legal framework in which these services are provided. In authority-initiated regimes, transport authorities have the legal monopoly of service. This means that autonomous market entry is legally impossible. In market-initiated regimes, the supply of transport services is based upon the principle of autonomous market entry resulting from a market process that may consider a certain degree of regulation at the start. The different governance structures of urban transportation are shown in Fig. 1.2.

Fig. 1.2  Organizational forms in public transport (van de Velde 1999)

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In authority initiatives, authorities play a key role in determining service delivery. In these arrangements, services are provided through: • “Conceding” to private companies: authorities select one or more private companies that are responsible for setting up and operating the public transport service; • Public ownership arrangements: the authority owns vehicles and infrastructure, and directly runs the service by itself or through a publicly owned company. Alternatively, the authority can make the assets available to a private company in charge of managing the whole service (van de Velde 1999). In such initiatives, the authority assumes the role of the entrepreneur, leaving limited room to the initiative of the selected service provider (whether public or private). In market initiatives, services are delivered through autonomous market processes. We may find both “open entry regimes” that rely on the hypothesis of perfect competition, and “authorization regimes.” In these cases, private companies need to apply for authorization in order to enter the market and provide the transportation service. These regimes shift the burden of continuous service improvement to the market. However, regulations and protection against competition may generate situations in which firms are no longer disciplined by market forces. In market initiatives, public authorities can no longer be conceptualized as entrepreneurs, but they may play three different roles (van de Velde 1999): • Watchdog (regulatory authority): controlling predatory behaviors by autonomous companies, the safety of operations, coordination of supply, etc. • Subsidizer: granting fare rebates to specific target groups of users and/or subsidizing transport companies. The aim of these subsidies is the redistribution of wealth so as to reach a different market equilibrium from what would otherwise prevail. • Supplier: in this case, the authority goes back to its role of entrepreneur by complementing the system service with additional services that are deemed to be socially desirable (i.e., providing routes that are not profitable but that may help in overcoming the segregation of some districts). Categorization into authority and market initiatives does not preclude combinations between them. In both kinds of arrangements, we find a delicate trade-off between pressures on the financial output objectives of service providers and the general interest in improving socio-environmental outcomes. Each institutional arrangement from NPM application to transportation is characterized by the presence of multi-level governance and, therefore, multiple levels of decision-making. Alongside conflicting interests between stakeholders, this adds complexity to the provision of transportation in cities (Della Porta and Gitto 2013). Consequently, the transformation of urban transportation services from an input to a result-oriented perspective cannot be solved by merely focusing on the companies that provide the service (Stanley and Smith 2013). Rather, the whole system should be considered through an inter-institutional perspective (Noto and Bianchi 2015).

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Consistent with the evolution of PA in the other public sectors, these emerging criticalities have provided room for the second phase of transformation, concerning a shift from outputs to outcomes (Della Porta and Gitto 2013) as well as a value creation orientation (Bryson et al. 2014). Although many national and local governments are going in this direction, it is not yet possible to identify an unambiguous reform process with clear key characteristics. This is also due to differences in the stage of NPM implementation across countries and the different organizational forms adopted in transport systems around the world (Noto 2016). Nevertheless, many authors have highlighted a common path toward coordination and networking efforts between the stakeholders involved in the service provision processes (Pucher and Kurth 1995; Hull 2005; Sager 2005; Stanley and Smith 2013; Della Porta and Gitto 2013; Tornberg and Odhage 2018). Post-NPM reforms (i.e., public value management) are indeed culturally oriented toward collaborative governance.3 They focus on cultivating a strong sense of values, team building, the involvement of participating organizations, trust, value-based management, collaboration, and improving the training and self-development of public servants (Ling 2002; Christensen and Laegreid 2007). According to the literature on post-NPM, urban transportation planning processes should thus foster collaboration between the “promoter” (e.g., the city council), public agencies, the private companies delivering transportation services, and other key stakeholders that have a role in determining the overall performance of the transportation system. In order to pursue this goal, it is vital to introduce new management concepts and tools. Many international experiences have adopted a strategic management perspective and tools such as stakeholder analysis as well as outcome-based performance management. The following section is devoted to the exploration of how planning activities combine with strategic management.

1.3  H  ow to Deal with the Current Challenges of Urban Transportation Planning: A Strategic Approach to Embracing Public Value Management The above analysis of urban transportation planning and the key actors involved leads us to the need to answer the third question advanced by this chapter, i.e., how urban transportation strategic planning should be developed. The dynamic and social complexity that characterizes urban systems is not exclusively derived from the institutional fragmentation explored in the previous

3  Collaborative governance can be broadly defined as “the processes and structures of public decision making and management that engage people constructively across the boundaries of public agencies, levels of government, and/or the public, private and civic spheres in order to carry out a public purpose that could not otherwise be accomplished” (Emerson et al. 2012, p. 2)

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section, but additionally from the different expectations of the community (i.e., pluralism), as well as from the interplay of several other factors, such as those related to the technical, economic, social, and environmental dimensions that determine scientific uncertainty. These characteristics (i.e., institutional complexity, pluralism, and scientific uncertainty) describe what the literature defines as “wicked” problems (Head and Alford 2015). Such problems characterize planning experiences in every complex social system (Rittel and Webber 1973), including in transportation (Bertolini 2017; Tornberg and Odhage 2018). Wicked problems are the opposite of tame counterparts that can be easily defined and formulated because they are characterized by a low level of complexity and are capable of standard or routine solutions (Head and Alford 2015). The term “wicked” does not mean “evil,” but instead refers to issues that are hard to define and manage due to the complexity of the environment they affect (Australian Public Service Commission 2007; Head and Alford 2015). These issues often lead to counterintuitive behaviors in terms of time (trade-off between short and long term) and space (trade-off between different institutions or functional areas within an organization) when actions are taken to resolve them (see Box 1.1 for further explanations of wicked problems).

Box 1.1 Ten Characteristics of Wicked Problems In order to better frame “wicked” problems, Rittel and Webber (1973) outlined ten characteristics that are reported and synthesized as follows: • There is no definitive formulation: In complex contexts, there are no criteria for framing problems because they exist in open systems. The process of formulating a wicked problem is the problem itself. This happens because the information we need to understand wicked problems depends on the way in which we intend to solve them, as well as by our biases in dealing with them. • They have no stopping rule: the problem of solving a wicked problem is equivalent to the process of understanding it. According to the first proposition, in open systems, such as the ones in which these issues exist, there are no ends to the causal chains that link the steps achieved in the process of understanding them. • Solutions are not true or false, but good or bad: the process of evaluating the solutions provided to tackle wicked problems is influenced by the interests, the value set, and the ideological predilections of who is judging. Social facts cannot be classified as true or false in absolute terms, hence their assessment may be better expressed as “good or bad,” “better or worse,” “satisfactory,” or “good enough.” • It is not possible to test a solution: if we implement a solution to a wicked problem, it will generate a wave of consequences that will not allow us to isolate the solution itself. For example, in complex systems, short-term

1.3  How to Deal with the Current Challenges of Urban Transportation Planning…

•

•

•

•

•

•

solutions may appear to work, but only later can we measure their real impact. There is no opportunity to learn by trial and error: when dealing with wicked problems, every implemented solution is consequential and irreversible. If we build a new road to solve a congestion problem, and spend an appropriate amount of money, we cannot easily disinvest if its performance proves to be unsatisfactory, and its impacts will affect the lives of the people involved for several years. Therefore, in a context in which the actions taken are irreversible and the half-lives of the consequences are long, every trial counts. They do not have an enumerable set of potential solutions. Indeed, generally in the pursuit of a wicked problem, a host of potential solutions arise, and another host is never considered. When problems are poorly defined, the set of potential solutions relies on realistic judgment and on the amount of credibility and feasibility of the actions proposed. Every problem is essentially unique: there are no classes of wicked problems. Although some wicked problems may seem similar, we can never be sure that the particularities of a problem do not override its commonalities with other problems that have already been tackled. Every problem can be considered a symptom of another problem: problems consist of the gap between “what is” and “what should be.” In order to solve problems, we should first understand the cause of the discrepancy. Removing the cause identified poses another problem of which the original plan was the symptom. The new problem can, in turn, be considered the symptom of still another “higher-level” problem, and so on. This is what happens in wicked contexts. Trying to cure symptoms may complicate the resolution of the root problem because “every trial counts” (see proposition 5). An example can be given by fragmented governances when symptoms are experienced by single players that tackle them at their own level without exploring the real problem behind them. The existence of a discrepancy representing a wicked problem can be explained in numerous ways. The choice of explanation determines the nature of the problem’s resolution: in dealing with wicked issues we cannot rely on a linear model of reasoning and test a hypothesis due to their uniqueness (see proposition 7) and the lack of opportunity available for rigorous experimentation (see proposition 5). People tend to choose explanations arbitrarily, i.e., those that are plausible and fit their intentions best. Decision makers have no right to be wrong, in the sense that there is no tolerance for experiments that fail. Decision makers are made responsible for the consequences of the actions they generate. Moreover, their proposed solutions are confounded by a further set of dilemmas raised by the growing pluralism of the public.

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In a context characterized by wickedness, traditional urban transportation planning practices based on a “predict-and-provide” approach are no longer applicable (Bertolini et  al. 2008; Marsden and Reardon 2017; Tornberg and Odhage 2018). This approach, which consists of the provision of services/infrastructure for future predicted transportation needs, is indeed characterized by the following assumptions: well-defined problems and predictability of socio-environmental behaviors to specific actions (e.g., demand growth rate, technology adoption rate). Today, these assumptions are no longer valid and, in order to deal with wicked problems, planning approaches should embrace a strategic perspective (Meyer and Miller 2001; Head and Alford 2015). Bertolini et al. (2008) have identified four key features that urban transportation planning should address in order to embrace the strategic perspective explored in the previous section. First, an orientation toward sustainability intended as a dynamic and systemic perspective to approach problem solving. This amounts to developing transportation policies that are supportive of social and economic development, while at the same time acknowledging the limits to growth imposed by finite natural resources (Bertolini et al. 2008). Second, a paradigmatic change is required to include in the planning process new goals (i.e., public value creation) and tools such as stakeholder engagement, performance measurement, and management practices in a systematic way. Urban transportation planning should not only provide the information that is most relevant to policy makers (e.g., costs, short-term impacts, benefits), but also information that gives them a more complete understanding of the implications of their decisions at the broader level (e.g., foregone opportunities, long-term outcomes, equity issues) (Meyer and Miller 2001). Third, integration, collaborative practices, and exchange with other professionals and sectors are required (Bertolini 2017; Tornberg and Odhage 2018). The institutional fragmentation that characterizes the public sector requires collaborative practices to pursue common outcomes that cannot be attributable to single organizations and sectors. Fourth, an effort to improve communication practice should be made. In order to involve private and public stakeholders and to maintain their engagement in the planning process, planning aims, activities, and operations should be effectively communicated. Communication and participation are transversal to every phase of the planning process in order to facilitate wide and fair participation (information, feedback, and shared decision-making) with local stakeholders. One of the most important challenges is to maintain a good level of participation right until the implementation of the action through continuous stakeholder stimulation. Strategic planning is thus aimed at both supporting an understanding of the driving forces of an urban area’s performance, and fostering consensus building among stakeholders (Albrechts 2004; Bianchi and Tomaselli 2015). The first urban strategic plans were developed and applied in the 1980s (Healey 1997) with the aim to promote the use of a systemic governance approach for cities and districts.

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Since then, strategic planning has been applied to various sub-sectors of public administration, such as healthcare, education, and transportation. Strategic planning in cities and regions is an activity that has occurred as a result of several factors enhancing urban environmental complexity, i.e., increased competition among cities (investment and tourism attractions), declining public financial resources, and increased attention to environmental issues (Bryson and Roering 1988; Begg 1999; Trivellato and Cavenago 2014). The literature identifies some procedural phases to approaching strategic planning (Bryson 1988; Meyer and Miller 2001; Cavenago and Margheri 2006; Young 2006). These can be summarized in four key steps: vision development; diagnosis; design; and implementation. According to the policy cycle theory (Jann and Wegrich 2007), these phases are then supported by a monitoring activity and an evaluation through which decision makers receive feedback to adjust the various previous phases (see the blue arrows at the bottom of Fig. 1.3). Moreover, a transversal activity that should accompany each phase in order to negotiate pluralism and institutional fragmentation is stakeholder engagement. The following sub-sections explore each phase in detail.

1.3.1  Vision The first step in every strategic planning process is represented by the development and clarification of a vision for the urban area. A vision can be defined as “an image of the future” (Senge 1990; Collins and Lazier 1992), reflecting interaction between the desired states of prosperity, environmental quality, and community quality of life (Meyer and Miller 2001). When dealing with a city or a region, the vision, which is often developed at the political level, should be agreed and shared with stakeholders and the community (Healey 1997). Therefore, this step in the planning process should provide numerous opportunities for public input (Meyer and Miller 2001). According to Bryson (1988), in this phase the organization/entity identifies its stakeholders, along with the stakeholders’ criteria for judging its performance.

Fig. 1.3  The strategic planning process (adapted from Cavenago and Margheri 2006)

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Once a stakeholder analysis is completed, the organization/entity can develop a vision statement that takes key stakeholder interests into account. There are two ways in which the vision is usually defined. A first option concerns a preliminary engagement of local stakeholders and the involvement of the public at large. Then, through a facilitation process, a common vision of the local area is agreed upon and shared. The risk to adopting such a method is potential resistance from key stakeholders, who may decide against participating in the vision’s definition or intentionally slow down the process. A second option is related to the adoption of the vision of the political representatives elected through receiving the majority of electoral preferences. This alternative, although quick and easily managed, may be perceived as poorly legitimated by specific community groups. Furthermore, this choice may bind the strategic planning process to the current administrative mandate and to political swings.

1.3.2  Diagnosis The second step of strategic planning processes is related to the diagnosis or “environmental assessment.” Here planners are called upon to identify opportunities and threats coming from political, economic, social, and technological trends, as well as the strengths and weaknesses attributable to resources, processes, and current outputs. The success of this phase is strictly related to the effectiveness of the prior stakeholder analysis. In fact, in a highly fragmented institutional structure operating in an environment characterized by pluralism, it is particularly challenging to identify resources, processes, and drivers of performance activated in a geographic area such as a city. Traditionally, the most frequently adopted management tools to run the diagnosis phase are SWOT (strengths, weaknesses, opportunities, and treats) analysis and PEST (political, economic, social, and technological) analysis. SWOT analysis, developed by the Harvard Business School, aims to identify the factors that may affect the organization’s future in order to develop a proper strategy to align the external environment with the internal situation. In particular, SWOT focuses on the strengths and weaknesses of the local area, including an analysis of external threats and opportunities. PEST analysis is based on environmental variables. Its aim is to identify what in the environment might affect the decision-making process and strategies developed. The typical variables considered are politics, economy, society, and technology. This tool is more useful for managing external environmental factors. SWOT and PEST analyses can be used at the same time to provide different inputs for strategy building. Pickton and Wright (1998) and Ho (2014) have offered in-depth analyses of these tools and their contributions to strategic management. Although useful, SWOT and PEST analyses may only provide static representations of the urban system environment. Consequently, the diagnosis phase can be supported by dynamics tools such as system thinking and system dynamics, which enable us to focus on the dynamic relationship between the main elements that

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characterize the urban system (Forrester 1969; Senge 1990; Sterman 2000). An in-­ depth explanation of how these tools can be adopted in the strategic management process is provided in the second chapter of this book.

1.3.3  Design The third step of strategic planning processes is termed the “design phase” or “strategy formulation.” A strategy is a pattern of purposes, policies, programs, actions, decisions and/or resource allocations. […] An effective strategy must meet several criteria. It must be technically workable, politically acceptable to key stakeholders, and must accord with the organization’s philosophy and core values. It must also be ethical, moral and legal (Bryson 1988, p 0.77).

Strategy development begins with the identification of practical alternatives to resolving the strategic issues affecting the urban area considered. Planners should then identify the barriers to achieving those alternatives in order to ensure that the strategies deal with implementation difficulties directly rather than haphazardly (Bryson 1988). Owing to limited resources and the need to set priorities, the selection of some alternatives over others may determine an atmosphere of conflict. As such, decision-­ making processes are often characterized by bargaining, incremental adjustment to the existing situation, and a search for consensus (Meyer and Miller 2001). To deal with this issue, decision-making processes should be transparent, clearly communicated, and shared with stakeholders and the community. Once alternatives (along with barriers to their realization) have been identified, strategy formulation requires the design and development of process and activities to achieve the expected outcomes. At the technical level, strategies are sought to define objectives and goals consistent with the urban vision. These objectives are then translated into processes and activities for the achievement of desired end results, i.e., outcomes. These outcomes depend on the achievement of intermediate results (measurable through performance indicators) or performance drivers, resulting from the deployment of the resources owned by the urban area (Bianchi 2010, 2012, 2016; Bianchi and Tomaselli 2015). Thus, when formulating strategies, it is important that for accountability and implementation purposes a set of performance indicators based on resource consumption and accumulation processes is defined consistently with the objectives fixed (Noto and Noto 2019).

1.3.4  Implementation, Monitoring, and Evaluation The final step of the strategic planning process is about implementation. This phase is the realization of what was thought of and designed in the previous phases.

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Given that the actions taken do not necessarily solve the problems for which they were designed, and owing to potential implementation challenges, monitoring and evaluation are required to correct any distortion that may occur during implementation (Meyer and Miller 2001). Thus, the steps required by the programming and budgeting of the transportation projects implemented need to be continually verified and monitored throughout this process. The purpose is to be consistent with the design and with the development idea formulated during the diagnosis phase. In this phase, it is important to adopt a logic of flexibility that will allow the adaptation of the plan in a changing context. Monitoring is key to evaluating strategic plan processes both at the operational level (i.e., is the plan being implemented as designed?) and the strategic level (i.e., is the strategy implemented producing the expected results?) The monitoring process at the strategic level (i.e., how far we are from the achievement of strategic outcomes) can be put in place through the use of the performance indicators identified and adopting the instrumental view of performance previously identified, i.e., indicators that represent the drivers of urban transportation performance. Performance indicators should focus on the information of greatest concern to policy makers, reflecting the ultimate outcomes of transportation system performance. Thus, they represent a critical way of providing feedback to the decisionmaking process on the results of previous decisions. Performance information is crucial not only to assessing the consequences of a decision but also to better defining the problem itself (Meyer and Miller 2001). Due to their importance, the next chapter will be devoted to the process of defining performance indicators in urban transport planning experiences. In order to be effective and consistent with the aim of fostering sustainable development policies, the processes and phases analyzed here should be supported by tools that can cope with the complexity of urban transportation systems. In particular, tools that allow planners and decision makers to adopt a systemic and outcome-­ based performance view are required.

1.4  Conclusions This chapter has provided an overview of the key aspects of urban transportation strategic planning. It has outlined the “what,” “who,” and “how” of urban transportation planning, discussing the opportunities and challenges that characterize these processes. The key message that emerges from the literature and experiences considered here is related to the need to adopt a strategic management perspective in the planning and management of urban transportation systems. The strategic management perspective mentioned here is defined by the need to deal with the environmental complexity that characterizes transportation in cities. This complexity can be framed according to the three main characteristics that according to Head and Alford (2015) define “wicked” problems, namely pluralism, institutional fragmentation, and scientific uncertainty.

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Strategic management in urban transportation system should be developed around two main pillars. The first is related to the systemic view of the urban area. This implies consideration of the different interests and expectations of stakeholders and the community, as well as identification of the main causal relationships that determine the performance of the urban transportation system. The second pillar is related to the need to adopt a performance management approach to support policy makers in defining, monitoring, and assessing the objectives and outcomes that the urban transportation system should address consistently with the systemic view adopted. In order to better explore the concepts outlined in this first chapter, the following chapters are designed to focus on the adoption of a systemic and performance-­ oriented approach in transportation (Chap. 2), the stakeholder analysis and governance mechanisms of urban systems (Chap. 3), and the technical features of dynamic performance management, applied to urban transportation systems (Chap. 4).

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Chapter 2

A Dynamic Performance Management Approach to Support Urban Transportation Planning

The public management literature on strategic planning suggests that future challenges in this field of practice are related to the switch toward adopting a holistic and strategic management perspective (Poister and Streib 1999; Poister 2010; Poister et al. 2010). Strategic management combines strategic planning processes with ongoing performance management (PM) in order to ensure that strategies are effectively implemented (Poister 2010). PM can be defined as the cyclic process of identification of objectives, definition of actions and performance indicators, implementation, and monitoring in progress and ex-post the results achieved (Amigoni 1978; Otley 1999; Bouckaert and Halligan 2008; Noto and Bianchi 2015). The application of PM in strategic planning in the public sector has two main aims. On the one hand, PM guarantees public organizations’ accountability in terms of the use of resources and the results achieved (Osborne et  al. 1995; Heinrich 2002). On the other hand, it enables strategies to be effectively linked with performance results (Poister 2010). In fact, while strategic plans usually identify performance measures that are monitored and may feed meaningful information into strategy review, they are often not systematically linked to goal structures and measurement systems at the operational level, i.e., the level at which performance improvement is most likely to be generated (Poister 2010). Without such linkages, strategic planning is much less effective in driving decisions and actions. A barrier to this linkage is represented by the different levels at which strategic planning and PM are usually adopted in the public sector. While strategic management focuses on taking actions to drive public policies that are defined and conducted in a networked environment (Bryson et al. 2006), PM is largely concerned with managing ongoing programs and operations at a single institutional level (Brignall and Modell 2000; Dekker 2016; Nuti et al. 2018). This may limit the ability of policy makers to assess the performance of the wider system of stakeholders taking part in the public value creation process. Another limitation of traditional PM

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systems to feedback at the strategic level is related to their static perspective in terms of time. Indeed, PM systems are usually poorly designed to support policy makers in addressing trade-offs between short-term and long-term decisions (Cosenz and Noto 2016). With the aim of overcoming both limits (i.e., the static perspective in terms of space and time), several authors suggest integrating traditional PM schemas with system dynamics (SD)1 modeling (Morecroft 1997; Warren 2005; Bianchi 2012, 2016; Cosenz and Noto 2016). One of the resulting approaches is known as dynamic performance management (DPM) (Bianchi 2016). This chapter is mainly devoted to exploring and discussing this approach with specific reference to the transportation sector. To this end, in the following sections we focus on PM and SD with specific reference to their application in the public and urban transportation sectors. Subsequently, the DPM approach is discussed in order to highlight the opportunities and challenges related to its adoption in supporting urban transportation planning activities.

2.1  P  erformance Management as an Approach to Support Strategy Development and Accountability in Urban Strategic Planning Processes The advantages of PM with reference to the public sector are particularly important when considering the accountability requirement of linking the activities undertaken by different actors and stakeholders with ultimate performance (Marcon and Russo 2014). The importance of accountability in the public sector is mainly related to the need to be transparent regarding the use of public resources. In fact, public services are often totally or at least partially covered by public funds collected via general taxation. Consequently, public agencies are obligated to display information about how public resources have been used and whether they created or destroyed value for the community. Measuring performance is a practice that had already been developed in the private sector to support decision-making. In the public sector it was introduced through the NPM reform, and assumed a dual role: (a) as a tool to support decision-making at different governance levels, such as in the private organization; (b) as a means to communicate public administrations’ results to the community and to other “external” stakeholders (e.g., investors). In order to foster the adoption of performance-measuring practices, many countries make the production of performance-related documentation for public administration (PA) compulsory. Although this enables a wide diffusion of such practices, in many cases public organizations produce documentation not to support decision-­ making or to foster accountability, but rather to respond to a legal obligation (Bianchi 2004, 2016). 1  SD is a scientific approach based on simulation that was developed to deal with complex systems (Forrester 1961; Sterman 2000).

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Nevertheless, performance measurement and management systems are crucial in managing public organizations to achieve strategic goals (Poister et al. 2013). For example, in a study conducted with 88 cases of transit agencies in the USA, Poister et al. (2013) discovered that an extensive use of performance management practices contributed to increased effectiveness in small- and medium-sized public transportation systems. This can be explained by the fact that PM practices help both the establishment of clear goals and the generation of useful information that enable decision makers to manage programs more effectively (Poister et al. 2013). PM may be defined and framed according to different perspectives. Bianchi (2010, 2012, 2016) has identified three complementary views of PM: objective, instrumental, and subjective. The objective view takes into account the relationship between an organization— or a network of organizations in the case of the public sector—and its environment. Once the product/service has been defined, it is possible to design the processes and activities necessary to deliver it. For example, if we see infrastructure maintenance as a service, the objective view then aims to identify the processes required to perform this task and its related activities. The instrumental view of performance conceives PM frameworks according to three main building blocks: • Strategic resources: physical or information resources held by the system that can be employed in order to achieve some desired result. These usually represent the inputs of the system analyzed. • Performance drivers: usually represented as performance indicators, these are intermediate results that measure the state of a system by comparing it with a target value or a benchmark. They typically refer to output measures (e.g., the number of trips performed over a benchmark or target). • End results: the results that the organization/system attains, thanks to the management cycle (production process). These may comprise either outputs or outcomes depending on who takes advantage of their achievement (e.g., an improvement in quality of life may be considered an outcome because it produces benefits for the reference community, while an increase in revenues coming from service delivery represents an output for the organization running the service). The subjective view provides a synthesis of the previous two because it is related to both the activities to be undertaken and the associated objectives and performance targets to achieve. This view focuses on the importance of strategic goals/ objective settings, and allows us to consider the potential distortions that may arise with improper goal settings (i.e., unfocused goals, confusion between means and ends, etc.) that risk affecting the overall system performance management. This risk is enhanced in the case of complex systems such as cities where different stakeholders pursue different and sometimes conflicting goals. When dealing with urban transportation strategic planning, the instrumental and subjective views are particularly relevant, whereas the objective is more critical when focusing on the operational level. The subjective view allows linking the system objectives (which are developed consistently with the urban vision) to the end

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results and the performance drivers, whereas the instrumental view focuses on the resources that need to be activated in the territory to achieve those end results (Fig. 2.1). The combination of both views is therefore particularly relevant in strategic planning processes because it allows one to design a set of performance indicators to monitor the desired outcomes and to activate the relevant strategic resources (Noto and Noto 2019) consistently with the objectives identified by the defined strategy (Fig. 2.2).

Fig. 2.1  Three views for designing a performance management system (adapted from Bianchi 2010)

Fig. 2.2  The combination of a subjective view and an instrumental view of performance (adapted by Bianchi 2010, 2016)

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The multi-level governance and the plurality of interests implicit in the urban transportation sector may represent an obstacle for the implementation of an effective PM framework. In complex systems characterized by a fragmented governance structure, it is indeed likely that strategic resources and end results are under the control of different actors. This generates additional elements of complexity that PM systems must negotiate. PM systems usually take the form of a set of performance indicators. Consistent with the NPM reform, traditional PM frameworks adopt an intra-organizational perspective because they were designed to assess and monitor performance at the organizational level (Head and Alford 2015). They usually focus on financial measures and are mainly used on an annual basis in order to monitor the use of resources and to attribute organizational responsibilities (Otley 1994). Such an approach poses limits to the implementation of a PM system capable of supporting public value creation, toward which the public sector all over the world is moving (Nuti et al. 2018). According to strategic and performance management studies, is it essential that PM systems (in their role of information sharing and communication tools) are designed consistently with the aims and strategies of organizations or systems of organizations (Teece 1990; Pettigrew 1992; Marr 2006; Van Thiel and Leeuw 2002; Nuti et al. 2018). Therefore, in an institutionally fragmented context, traditional PM systems focused on performance in individual institutions cannot be considered consistent with the overall system’s aim and may thus generate counterintuitive results or unintended consequences. Counterintuitive results are also related to the other limits that may characterize PM systems. These limits have been explored by several authors (Sloper et al. 1999; Linard and Dvorsky 2001; Bianchi 2012; Bisbe and Malagueño 2012). Bisbe and Malagueño (2012) have demonstrated the lack of perspective capturing the dynamic complexity of managerial decision-making. In fact, PM techniques tend to ignore a number of relevant factors that influence both the planning and measuring of organizational performance, and therefore constrain decision makers’ strategic learning processes (Sloper et al. 1999; Linard and Dvorsky 2001). Such factors are primarily associated with delays, non-linearity, intangibles, and to the unintended consequences to human perceptions and behaviors caused by a superficial or mechanistic approach in setting performance targets. Thus, traditional PM approaches can be described as “static” both in terms of space—they tend to excessively stress some subsystems (e.g., transportation, economic development, land use) or single organizations while ignoring others—and time, as they do not allow one to properly assess both the determinants and consequences of performance with reference to the trade-offs existing between short- and long-term effects (Bianchi et al. 2015; Bianchi 2016). The limits described above—i.e., a lack of alignment between strategy and PM systems, and a lack of dynamic perspective—often result in so-called performance traps or performance paradoxes (Meyer and Gupta 1994; Smith 1995; Van Thiel and Leeuw 2002; Bevan and Hood 2006). Performance traps are related to narrow views and uses of measurement, which may lead to unintended consequences

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Table 2.1  Performance paradoxes (adapted by Wadmann et al. 2013) Term Gaming Cream-­ skimming Ossification Tunnel vision Goal fixation Sub-­ optimization Myopia

Description Altering of documentation practices rather than activities; eventually data manipulation Prioritizing of services who are likely to generate good performance measures, e.g., profitable routes If performance indicators are not updated in accordance with development Narrowing of managerial attention, e.g., downplaying experimental projects, human resources, or other non-measured areas Attention focused on obtaining good measures rather than improving underlying quality problems Attention focused on local performance results rather than overall system goals Attention focused on short-term rather than long-term goals, e.g., cost-shifting from long-term to short-term priorities

(Smith 1995; Van Thiel and Leeuw 2002; Bevan and Hood 2006; Wadmann et al. 2013; Marcon and Russo 2014, Nuti et al. 2018). Performance paradoxes have been explored in detail by the literature (see among others Smith 1995; Waddman et al. 2013). Table 2.1 provides a summary of the main paradoxes that may be derived from the adoption of static PM systems. For instance, using performance indicators aimed at increasing volumes of passengers in touristic areas may stimulate levels of visitation that cause visitor crowding, resource impacts, and other unintended consequences (Lawson et  al. 2017). Another example could be the case of an urban area in which public providers are exclusively evaluated based on their financial performance. In such cases, providers may exclusively focus on the provision of profitable routes where transportation demand is high. This may create equity and accessibility problems because transportation services in low-density urban areas are reduced or tariffs are significantly increased. As such, although the measured performance of single agencies seems to be satisfactory, the public value creation for the local area and the urban transportation system’s sustainability is compromised. Such performance distortions may be overcome by merging PM with a dynamic and systemic perspective such as SD modeling (Bianchi et al. 2010, 2015; Bianchi 2012; Noto and Bianchi 2015; Cosenz and Noto 2016). In fact, a systemic perspective may allow PM experts to switch from output performance measures to multi-­ dimensional outcome ones, rendering PM a useful approach to drive systems of organizations in both designing competitive strategies and measuring the results achieved in an institutionally fragmented environment. The next section seeks to provide a review of the PM systems and measures used in the urban transportation planning literature.

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2.1.1  P  erformance Measures to Evaluate Transportation Systems’ Performance Defining a set of measures to assess transportation systems’ performance is a complex task because, as mentioned in the previous section, performance can be assessed to address different purposes and to reach different users. Before entering into the description of the performance measures for urban transportation planning it is therefore crucial to consider the users of information produced by PM systems and how they might use this information. First, performance measures are intended to inform policy makers so that they can comprise the features of the transportation system, outlining its strengths and weaknesses. This information represents the basis to designing policies and setting objectives for the urban system. This implies that performance measures should be computed and monitored timely and with a high frequency (i.e., monthly or quarterly). Performance measures are also addressed to key stakeholders and to the broader community. PM systems are additionally used as tools to guarantee accountability and transparency when dealing with public resources (Osborne et al. 1995; Heinrich 2002). Governmental agencies, firms, media, and the public have the right to know how resources have been employed and the results they have produced. The multiplicity of stakeholders, interests, and purposes for which PM systems are implemented renders the selection of measures quite challenging. Nonetheless, it is important to remember that performance measurement should not be interpreted as an end (e.g., to comply with legal requirements), but as a means to support strategic planning and management (Bianchi 2004, 2016). Given the multifaceted nature of transportation systems, previous literature has identified a wide set of performance measures (Meyer and Miller 2001; Black et al. 2002; Marsden et al. 2006; Eboli and Mazzulla 2012; Shah et al. 2013; Cascajo and Monzon 2014; Litman 2016). In order to support urban transportation planning, performance measures may range from those directed toward system operation (e.g., average speed) to assessing the broader consequences of system performance (e.g., equity, accessibility) (Meyer and Miller 2001). PM systems should comprise both measures addressed at assessing output achievement (i.e., drivers of overall performance) and outcomes measures, or proxies of outcome measures (what have previously been called end results). Moreover, performance should be assessed across multiple domains (i.e., economic, social, environmental), dimensions (e.g. accessibility, efficiency), and modes (e.g., bus, train, automobile). For example, Litman (2016) has demonstrated that transportation performance indicators can be framed according to the three sustainable development domains (economic, social, environmental), plus a fourth section related to governance and planning. In Table 2.2, Litman (2016) links performance indicators to the objectives derived from the sustainability goals.

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Table 2.2  Key sustainable transport goals, objectives, and indicators (Litman 2016) Sustainability goals Economic Economic productivity

Economic development Energy efficiency

Affordability

Efficient transport operations

Social Equity/fairness

Safety, security, and health

Community development

Cultural heritage preservation

Objectives

Performance indicators

Per capita GDP Portion of budgets devoted to transport Per capita congestion delay. Efficient pricing (road, parking, insurance, fuel, etc.) Efficient prioritization of facilities Economic and business development Access to education and employment opportunities. Support for local industries Minimize energy costs, particularly Per capita transport energy petroleum imports consumption Per capita use of imported fuels Availability and quality of All residents can afford access to affordable modes (walking, basic (essential) services and cycling, ride-sharing, and public activities transport) Portion of low-income households that spend more than 20% of budget on transport Performance audit results. Efficient operations and asset Service delivery unit costs management maximizes cost compared with peers efficiency Service quality

Transport system efficiency. Transport system integration. Maximize accessibility Efficient pricing and incentives

Transport system diversity. Portion of destinations accessible by people with disabilities and low incomes Per capita traffic casualty (injury and death) rates Traveler assault (crime) rates. Human exposure to harmful pollutants Portion of travel by walking and cycling Helps create inclusive and attractive Land use mix communities Walkability and bikeability Quality of road and street environments Respect and protect cultural Preservation of cultural resources heritage. Support cultural activities and traditions. Responsiveness to traditional communities

Transport system accommodates all users, including those with disabilities, low incomes, and other constraints Minimize the risk of crashes and assaults, and support physical fitness

(continued)

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Table 2.2 (continued) Sustainability goals Environmental Climate stability

Objectives

Performance indicators

Reduce global warming emissions. Mitigate climate change impacts

Per capita emissions of greenhouse gases (CO2, CFCs, CH4, etc.) Prevent air pollution Reduce air pollution emissions Per capita emissions (PM, VOCs, Reduce harmful pollutant exposure NOx, CO, etc.) Air-quality standards and management plans Minimize noise Minimize traffic noise exposure Traffic noise levels Per capita fuel consumption. Protect water quality Minimize water pollution Minimize impervious surface area. Management of used oil, leaks, and hydrologic and stormwater functions Per capita impervious surface area Open space and Minimize transport facility land use. Per capita land devoted to transport facilities biodiversity protection Encourage compact development. Support for smart growth Preserve high-quality habitats development Policies to protect high-value farmlands and habitat Good governance and planning Clearly defined goals, objectives, Clearly defined planning process. Integrated, and indicators Integrated and comprehensive comprehensive, and analysis. Strong citizen engagement. Availability of planning inclusive planning information and documents. Lease-cost planning Portion of population engaged in planning decisions Range of objectives, impacts, and options considered Efficient and equitable funding allocation

The indicators identified by Litman (2016) are mixed and comprise performance indicators focused on inputs (e.g., portion of budgets devoted to transport), outputs (e.g., service delivery unit costs), and outcomes (e.g., service quality). These indicators are primarily aimed at informing policy makers of the performance of urban transportation according to a sustainable development perspective. However, few of these indicators represent performance drivers as defined in the previous section of this chapter. In fact, although these indicators may prove useful in providing a clear picture of a system’s performance, they do not clearly explain how the resources employed in the system produce the results achieved. These measures can be effectively used for social reporting purposes, i.e., to make results transparent and to disclose them to the system’s stakeholders. At the mode level, Cascajo and Monzon (2014) and Eboli and Mazzulla (2012) have provided an assessment and review of urban public transportation indicators

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by identifying some key dimensions that characterize every transportation service and system. These are: • Service availability: this variable is related to the path and coverage of the transportation system, the number of stops, and their relative distance and location. It mainly concerns the service’s extensiveness and frequency. • Service reliability: Turnquist and Blume (1980) define transit service reliability as the ability of the transit system to adhere to schedule or maintain regular headways and a consistent travel time. Frequency and timeliness may represent useful variables to monitor to assess this dimension (Cascajo and Monzon 2014). • Fares: the service aspect regarding fares includes characteristics of the monetary cost of the journey, as well as the availability of volume discounts (e.g., for monthly passes). • Comfort and cleanliness: these aspects characterize the experience of users in adopting public transportation modes. • Safety and security: the two terms respectively indicate the chances of being involved in an accident, and of becoming victim of a crime while making use of the service. • Information: passengers need to know how to use the transport service, where access is located, where to get off in the proximity of their destination, whether any transfers are required, and when transit services are scheduled to depart and arrive. Moreover, thanks to new technologies, passengers can be informed in real time in cases of dysfunction related to accidents, maintenance, and other events that may cause congestion or disservice. • Customer care: It includes the elements required to make the journey easier and more pleasant, such as drivers’ courtesy, helpfulness of ticket agents, personnel’s appearance, and elements linked to the ease of ticket purchase and paying fares, like the presence and condition of ticket issuing and validation machines, and the effectiveness of the ticket-selling network. • Environmental impacts: this is the service aspect regarding the impact of mobility systems on the environment, including effects in terms of emissions, noise, visual pollution, vibration, dust and dirt, odor, and waste, as well as the effects of vibrations on roads and estimation of natural resource consumption in terms of energy or space. In this respect, the vehicle’s age together with the type of vehicle (gasoline, methane, electric) used offers a reference to monitor environmental impacts. With specific regards to urban transportation planning and the dimensions to be assessed, Meyer and Miller (2001) have provided a sample set of performance indicators related to the potential goals emerging from the planning process. In particular, they selected a number of relevant dimensions affecting urban transportation performance and, for each, provided a number of examples of performance indicators. These are displayed in Table 2.3. This set of indicators is mainly focused on output measures. As such, it is specifically addressed at supporting decision makers at different levels to set goals, monitor results, and take corrective actions.

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Table 2.3  Example performance measures for different goals (Meyer and Miller 2001) Dimension Accessibility

Mobility

Economic development

Quality of life

Performance indicator Average travel time from origin to destination Average trip length Accessibility index Mode split by region, facility, or route Percentage of employment sites within x miles of major highway Number of bridges with vertical clearance less than x feet Percentage of population within x minutes of y percent of employment sites Percentage of region’s mobility impaired who can reach specific activities by public transportation Origin–destination travel times Average speed or travel time Vehicle-miles traveled (VMT) by congestion level Lost time or delay due to congestion Level of service or volume/capacity ratios Vehicle-hours traveled per capita or VMT per capita Person-miles traveled (PMT) per vehicle mile traveled Percentage of transit on-time performance Frequency of transit service Mode split Transfer time between modes Customer perceptions of travel times Delay per ton-mile Person-miles traveled per capita or per worker Person-hours traveled Passenger trips per household Percentage of walking or using bike by trip type Economic costs of crashes Economic cost of lost time Percentage of wholesale/retail/commercial centers served with unrestricted (vehicle) weight roads Jobs created or supported (directly and indirectly) Percentage of region’s unemployed or low-income groups that cite transportation access as a principal barrier to seeking employment Lost time due to congestion Accidents per VMT or per PMT Tons of pollution generated Customer perception of safety and urban quality Average number of hours spent traveling Percentage of population exposed to noise above threshold (continued)

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Table 2.3 (continued) Dimension Environment and resource consumption

Performance indicator Overall mode split or by facility or route Tons of pollution Number of days in air-quality noncompliance Fuel consumption per VMT or per PMT Sprawl-difference between change in urban household density and suburban household density Number of accidents involving hazardous waste Safety Number of accidents per VMT, per year, per trip, per ton-mile, and per capita Number of high accident locations Response time to incidents Accident risk index Customer perception of safety Percentage of roadway pavements rated good or better Construction-related fatalities Accidents at major intermodal (e.g., railroad crossings) Pedestrian/bicycle accidents Operating efficiency (system Cost for transportation system services and organizational) Cost/benefit measures Average cost per lane-mile constructed Origin–destination travel times Average speed Percentage of projects rated good to excellent in quality Volume to capacity ratios Cost per ton-mile Mode split Customer satisfaction System preservation Percentage of VMT on roads with deficient ride quality Percentage of roads/bridges below standard condition Remaining service life Maintenance costs Roughness index for pavement Service miles between road calls for transit vehicles Vehicle age distribution

Although the measures suggested are often similar, it is apparent that they have been frequently framed into different categories and conceptual frameworks in the literature. These differences mainly depend on the purpose of the performance measurement, which in some cases could be oriented at assessing the transportation service’s performance (see for instance Eboli and Mazzulla 2012; Cascajo and Monzon 2014), sustainable development (e.g. Litman 2016), or the ability to pursue specific goals defined in the planning process (Meyer and Miller 2001).

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Thus, we can state that what really makes the difference in defining a set of performance indicators for strategic planning purposes is not the selection of the measures per se, but the process according to which these are defined (i.e., the response to the purposes for which they have been defined) and linked to policy action. When referring specifically to sustainable transportation, we can refer to the key domains identified by the literature (i.e., economic, social, and environmental) as well as to the six dimensions defined by Black et al. (2002) in their review, namely economic efficiency, livable streets and neighborhoods, protection of the environment, equity and social inclusion, safety, and contribution to economic growth. The set of performance indicators framed into these domains and dimensions may then be declined at the three performance management levels identified by the literature: strategic, tactical, and operational (van de Velde 1999; Bianchi 2004; Bouckaert and Halligan 2006). The strategic level is concerned with the formulation of those general goals that the organization aims to pursue. The main features of the service (e.g., who the main target groups are, which one is the area of supply, etc.) are established at this level. The tactical level focuses on a decision-making process directed at the acquisition of those means necessary for reaching the general aims fixed at the previous level, and on how to use these means most efficiently. The actual design of the service takes place at this level, that is, the definition of routes and frequency, fares, the image of the service, and so forth. At the operational level, the necessary steps are taken so as to ensure that the service is delivered efficiently by translating tactical aspects into day-to-day practice, for instance, the management of vehicles, drivers, infrastructure, and so on. A defined set of performance indicators does not imply the implementation of a performance management system. Rather, these indicators must be consistent with their objectives, computed on a regular basis, and be used to implement corrective actions when needed. In contrast to other public sectors (e.g., heritage management, education, etc.), transportation has a long tradition of performance measurement and, in particular, of measuring technical efficiency. This is due to the fact that its inputs, processes, and results are highly and easily measurable compared to other sectors. Given this “culture of measurement,” the risk that may occur is using all of the measures available even when they are not relevant or useful for strategic management purposes. This risk is enhanced by the rising number of information available for policy-­ making use (e.g., big data). In order to avoid this risk and to ensure that performance indicators are designed so as to effectively support decision-making processes, two main criteria are suggested here. The first is the adoption of an instrumental and dynamic view of performance, which implies assessing performance through the identification of measures representing performance drivers, i.e., the intermediate results of the system (see Sect. 2.3 for further explanation). Second is the cost–benefit analysis of the collection of data, i.e., whether it is convenient to collect data as much as the expected benefits of using that information are greater than the cost to do that.

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In order to follow the first suggested criterion, PM systems could be supported by the adoption of system dynamics (SD) modeling. The following section is aimed at exploring SD modeling and how it may support urban transportation planning activities.

2.2  System Dynamics and Urban Transportation System dynamics (SD) is a scientific approach developed during the 1950s, primarily by J. W. Forrester. It may be defined as a perspective and set of conceptual tools that enable us to understand the structure and dynamics of complex, non-linear, multi-loop feedback systems (Forrester 1961; Meadows 1980; Sterman 2000). SD was developed through bringing together concepts from several fields, such as control-engineering, cybernetics, and organizational theory (Meadows 1980; Vennix 1990). It was originally applied to industrial companies’ problems (e.g., inventory management, falling market share, instability of labor force, etc.). However, it has been gradually and successfully applied to a wider variety of social systems, such as climate change, engineering, environmental sciences, economics, and strategic management (Cosenz and Noto 2016). SD represents a powerful tool to analyze the dynamic tendencies of complex systems, i.e., what kind of behavioral patterns they generate over time. The main assumption of the SD paradigm is that these patterns arise from the causal structure of the system being analyzed. Causal structures are determined by physical or social constraints, goals, rewards, and pressures that make a system’s agents to behave in a certain way (Meadows 1980). The SD approach frames complex systems based on their causal structures and the dynamics that characterize them. As such, it fosters the adoption of an endogenous perspective. This means that SD analysts tend to shift the boundaries of their models in order to include all the structures that cause a certain behavior within the system independently from the institutional framework and governance structure that characterizes it. This perspective allows the modeler to build closed chains of causal relationships, also known as “feedback loops.” As a result, SD models are made up of several interlinked feedback loops. SD offers two kinds of representations: causal loop diagrams (CLD), and stock and flow diagrams (SFD). The first is qualitative and focuses on causal relationships between variables. The other is quantitative and aims to emphasize the physical structure of the system being analyzed. The figure below shows an example of a feedback loop through a CLD representation. The structure displayed in Fig. 2.3 makes clear the dynamics according to which the population of a certain city is directly related to the amount of taxes local authorities may collect. A greater amount of taxes generates resources that can be invested to improve public transportation services. An improved transportation makes the urban area more attractive, and this encourages people living somewhere else to

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Fig. 2.3  A causal loop diagram (CLD)

Fig. 2.4  Exponential growth behavior

move their businesses and residency into the city. The population will then grow due to this incoming migration flow. When every relationship in the loop is positive2 (therefore marked with the + sign) or the number of negative relationships is even, we have a so-called reinforcing loop. This kind of loop tends to produce exponential growth behaviors (see Fig. 2.4). On the other hand, “balancing” feedback loops are characterized by an odd number of negative relationships and tend to counteract any disturbance and move the system toward an equilibrium point (Meadows 1980). The inner loop in Fig. 2.5 is a balancing loop. Although an increase in the city population determines further taxes that can be collected, in the long run it also means more people to serve and, as a result, it may result in an inadequate public service in relation to the needs of the new population. This loop tends to counteract the exponential growth behavior shown previously. The graph shown by Fig. 2.6 presents the typical “exponential decay” behavior of balancing loops. 2  Positive (or direct) means that when one variable changes (e.g., increases), it determines a change in the same direction (e.g., increases) to the connected variable; negative (or indirect) means that when one variable changes (e.g., increases), it determines a change in the opposite direction (e.g., decreases) to the connected variable.

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Fig. 2.5  A balancing feedback loop

Fig. 2.6  Exponential decay behavior

While the concept of a causal loop diagram is very immediate and easy to understand, the stock and flow diagram requires a brief description of the elements included: • Stocks (square variables): accumulation of material/information resources at a given moment in time, e.g., the population stock; • Flows (arrows with valves): flows of materials/information to or from a stock over a period of time, e.g., the migration flow; • Auxiliary variables (round variables): elements that support the calculation, e.g., the function describing the effect of financial resources invested on the service quality. • Constant variables (diamond variables): input variables, e.g., the average tax rate. The CLD structure displayed in Fig. 2.5 has been re-built in SFD terms (Fig. 2.7). One should notice that the SFD requires a much more detailed structure in order to perform the simulation of the system’s behavior. The image also provides a legend describing the symbols that represent stocks, flows, and auxiliaries. The interaction between multiple loops determines the overall system’s behavior. The graph shown in Fig. 2.8 shows a simulation of the simple model described above.

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Fig. 2.7  A stock and flow diagram

Fig. 2.8  S-shaped pattern

As we may notice, the initial exponential growth behavior, driven by the reinforcing loop, is substituted by an exponential decay pattern after the tenth year. This generates an S-shaped behavior caused by the shift in loop dominance over time. What emerges from this simple system and its behavior is the importance of delays and nonlinearities. A non-linear relationship affects the “strength” of the

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feedback loop in which it is included, determining at a system level a switch in the pattern (from exponential growth to exponential decay). In the same way, time delays (e.g., the time required for public service quality to affect migration) can be crucial elements influencing the dynamic behavior of a system. In the previous example, the time that public service quality takes to affect migration determines the moment at which the behavior experiences a shifting loop dominance. One of the key features that makes SD particularly suitable for complex social systems is simulation. The value of simulation as a scientific method lies in three main features (Axelrod 1997): prediction (or forecasting), existence proof, and discovery (of new structures). However, in social sciences such as management, this value also lies in its educational and training features (Sterman 2014; Cosenz and Noto 2018). Sterman (2014) considers simulation a useful tool that supports us in discovering how complex systems work when real experimentation is too slow, too costly, unethical, or just plain impossible; that is, for most social issues, including urban transportation (e.g., it will be too costly to build a new highway to test whether the investment would create public value or not). The use of SD modeling empowers the two-step process, which according to Kim et  al. (2013) characterizes learning through simulation models, namely: (1) learning from causal maps, which implies building and exploring mental models3; (2) learning from the simulation model that follows, resulting in an enhancement of the mental models’ accuracy. The use of SD modeling thus helps decision makers theorize the potential impacts of scenarios that emerge from testing and challenging their mental models and strategic decisions (Cosenz and Noto 2018). The possibility of questioning how an urban transportation system works and whether solutions may imply a change in its governing values forms the basis on which to ground a “double-loop learning” process, as characterized by Argyris (2002). Double-loop learning involves the modification of goals or decision-making rules in light of the experiences that one can gain by simulating and testing alternative strategies. In this sense, SD differs from other simulation techniques that are frequently used in transportation studies, which mainly focus on behavior and parameter testing. SD instead focuses on the link between the behavior of the system and the complex system structure generating it. This allows one to challenge not only the single parameters, but the system structure itself. Figure 2.9 portrays the difference between single- and double-loop learning. In the urban transportation sector, for instance, SD modeling may provide insights related to the financial payback of potential investments, but also how to govern the travel demand by leveraging on a territory’s attractiveness (Noto 2017). Given the characteristics analyzed here, SD represents an adequate method to study governance issues in a wicked context, such as urban transportation (Noto and Bianchi 2015). This consideration arises from the fact that SD allows us to conduct a structural analysis of a system and to include in the boundaries of the analysis the characteristics of its fragmented institutional environment. 3  Mental models are defined as the assumptions, generalizations, and representations that influence how people understand the world and take action (Senge 1990).

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Fig. 2.9  Single- vs. double-loop learning (adapted from Argyris 2002)

The characteristics of SD mentioned above render it a valuable tool to support strategy making in urban transportation both for infrastructure (e.g., adding new facilities, dismissing one mode of transportation and investing in new others, etc.) and for transport management (re-organizing existing services both at the governance and the operational level). This will be further explored in the following sub-section. Furthermore, SD is also valuable for exploring urban transportation performance inasmuch as it may be framed through adopting the fundamentals of PM (Bianchi 2012, 2016).

2.2.1  A  Literature Review on System Dynamics and Urban Transportation Due to its characteristics, SD has already been successfully applied to urban transportation issues in several studies. The early scientific contributors that outlined the possibility of adopting SD as a transportation modeling approach were Abbas and Bell (1994). In particular, they suggested that SD is well-suited to strategic issues and that it could provide a useful tool for supporting policy analysis and decision-making in the transportation field. Consistent with the analysis and discussion produced in the first chapter of this book, Abbas and Bell (1994) argue that transportation systems are complex, involving a number of different stakeholders, resulting in feedbacks with different time lags between the responses of each type of user. SD models thus offer a comprehensive systemic approach to transport planning that can demonstrate to policy makers the importance of these feedbacks and lagged responses (Shepherd 2014). According to a literature review conducted by Shepherd (2014) on peer-reviewed articles related to the application of SD to the transportation sector from 1995 to

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2013, the most commonly explored areas in the transportation research field utilizing an SD approach are: • • • • •

Strategic policy at urban, regional, and national levels; Modeling the uptake of alternative fuel vehicles; Airlines and airports; Supply chain management with transportation; and. Highway maintenance/construction.

Among these areas, the most relevant for the purpose of this study is the first. This includes studies focusing at the strategic policy level. These studies mainly look at implications for the structure of cities and regions, as the economy, population, migration, infrastructure, and land use all interact with transportation. A subset of these build on the stream of land use interaction models (LUTI). SD is particularly appropriate to dealing with LUTI models because land use and transportation systems are profoundly interconnected, albeit they operate on different time scales. In fact, transportation systems’ users may respond relatively quickly to changes in transport policy or costs, while the land-use system includes a significant degree of inertia (Shepherd 2014). One of the most successful contributions of SD to LUTI models is the MARS model (Pfaffenbichler et  al. 2010; Pfaffenbichler 2011). Compared to the other LUTI models, the MARS model is more aggregated because the authors’ aim was to support decision-making at a strategic level, whereas the other LUTI models utilize more detailed models for operational purposes (Pfaffenbichler et al. 2010). The MARS model aims to test and identify the impacts of different policy instrument combinations, and it can also be considered a training tool for decision makers to foster their understanding of the transportation system. MARS models have now been applied in more than 20 cities worldwide and have been used as a training tool for planners and practitioners with the aim of optimizing strategic transport policies for a range of cities and regions (Shepherd 2014). Other examples of studies focused on the interrelationships between transportation systems and other urban/regional dimensions may be found in Hagani et  al. (2003a, b), Wang et al. (2008), Shen et al. (2009), and Feng and Hsieh (2009). Hagani et  al. (2003a, b) developed and simulated a regional model based on Montgomery County’s (Maryland, USA) data, comprising seven sub-models that incorporated population, migration, households, employment, residential and commercial development. Their model was aimed at assessing the impacts of highway-­ building capacity and associated changes in land use that in turn affected demand and the performance of the transport system during the period 1980–1990 (Hagani et al. 2003a, b). Wang et al. (2008) used SD to simulate the evolution of an urban transportation system. In particular, they developed a case study based on Dalian Central City (China) with the objective of investigating the impacts of vehicle ownership policy on urban transportation development. The resulting model, like that developed by Hagani et al. (2003a, b), is constituted of seven sub-models: population, economic development, number of vehicles, environmental influence, travel demand, ­transport

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supply, and traffic congestion. The interaction of these sub-models determines the overall system behavior. Shen et al. (2009) developed a model aimed at comparing low- and high-density land use policies for Hong Kong and their impacts on sustainability dimensions (environmental, social, and economic) in order achieve a perfect balance among them. Feng and Hsieh (2009) used SD to develop a hybrid model aimed at dealing with resource allocation in Taipei’s (Taiwan) transportation system, considering the interests and expectations of the various stakeholders involved. Other recent studies have tackled the environmental dimension. For instance, Han and Hayashi (2008) investigated emission reduction policies for intercity transport problems, while Armah et al. (2010) replicated the structure used by Sterman (2000) to explain counterintuitive problems related to a road-building problem, providing an SD model of transport congestion and environmental health risk applied to a case study in Accra, Ghana. Haghshenas et  al. (2015) analyzed the sustainability of various transportation policies using SD models based on pertinent data and the urban dynamic models of world cities. Trip generation, modal share, transportation supply, and equilibrium between supply and demand were the key modules of the model developed. The key output of their study was related to the definition of a set of economic, social, and environmental indicators. Lastly, Noto (2017) developed an SD model focused on the northern urban area of Buenos Aires, Argentina. The model sought to identify the key performance drivers of the urban system, using them as indicators to evaluate alternative policy options. Other interesting scientific works that demonstrate SD’s advantages in supporting urban transportation planning and management may be found in Bivona and Montemaggiore (2010), Liu et al. (2010), and Noto and Bianchi (2015).

2.2.2  O  pportunities and Limitations of Using System Dynamics in Supporting Urban Transportation Planning What emerges from Shepherd’s (2014) literature review, while also considering subsequent studies (Haghshenas et al. 2015; Noto and Bianchi 2015; Noto 2017), is that the SD approach is suited to providing a holistic view of urban transportation systems and dealing with feedbacks, delays, and non-linear causal relationships between the resources of the stakeholders involved. However, some limitations should be outlined with regard to the SD approach in supporting transportation planning and modeling. According to Shepherd (2014), SD is neither easily applicable to traditional network assignment problems nor capable of replacing microsimulation tools. In fact, although the SD model can be articulated in order to consider spatial elements, a large number of geographical areas may overcomplicate the modeling process and simulation, leading to an

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unnecessarily complex model in which stakeholders are no longer able to easily understand the main structures and dynamics characterizing the system’s behavior (Pfaffenbichler et al. 2010; Shepherd 2014). Using always more detailed models is the route in which other transportation models have gone during the last years (Pfaffenbichler et al. 2010). As a result these models are poorly understood by planners, decision makers, and stakeholders, who are not inclined to use such sophisticated “black boxes” (Pfaffenbichler et al. 2010; Saujot et al. 2016). Until today, with few exemptions (e.g., Pfaffenbichler et al. 2010), little effort has been made by academics to fill the gap between laboratory application and operational use for planning practice (Wegener 1994; Vonk et al. 2005; Brömmelstroet and Bertolini 2008; Waddell 2011; Saujot et al. 2016). The gap between modelers and end users is mainly contingent on the model’s complexity and the decision makers’ difficulty in understanding it (Saujot et al. 2016). According to this perspective, the use of SD in the transportation research field may represent an opportunity to develop models aimed at providing analysis at the strategic level and to share an understanding of the urban transportation system’s dynamic interactions with a broad range of stakeholders (Pfaffenbichler et al. 2010). SD should therefore be used to both understand and explore the nature of the problem, as well as to investigate general dynamic tendencies. Indeed, SD models “can be used to test which parameters play a significant role in the stability and response of the system and the tools such as CLD and stock-flow diagrams enable a transparent approach to communicating results with stakeholders including the use of flight simulators and gaming tools which other approaches often lack” (Shepherd 2014, p. 101). In summary, SD can be applied to urban transportation to support a holistic perspective that considers multiple dimensions of the wider urban system, such as population, economic development, and infrastructure, as well as to enable stakeholders to share an understanding of urban transportation dynamics in both the short and long term. With regard to the view of strategic management proposed by Poister and Streib (1999), what is missing in many of the scientific contributions examined (that use SD to support urban transportation planning) is the process of definition and the use of key performance indicators aimed at monitoring and guiding the strategic cycle of urban transportation systems. This activity may be developed by combining the SD approach with PM theory. The next section is devoted to exploring the opportunities and challenges of merging SD and PM to support urban transportation planning.

2.3  D  ynamic Performance Management: Opportunities and Challenges for Urban Transportation Planning The first scientific contributions that combined SD with strategic management theories can be traced back to 1984 (Morecroft 1984).

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The pioneers of this theoretical approach were John Morecroft (1984, 1985, 1999) and Kim Warren (1999, 2000, 2004, 2005). Their studies sought to develop a dynamic resource-based view (RBV) of business, in order to analyze the establishment and defense of competitive advantage (Cosenz and Noto 2016). RBV is a strategic management approach that promotes the adoption of a resource perspective in order to analyze an organization’s internal and external opportunities and challenges, that is, to analyze the firm from the resource side rather than the product side (Wernerfelt 1984). As such, dynamic RBV models are typically focused on both the build-up and depletion processes of core assets, also termed strategic resources (e.g., workers, equipment, population, workload, perceived service quality, and financial resources). Although each strategic resource can be managed independently from others, the dynamic RBV shifts the analyst’s focus to understanding the causal relationships that link them together. This is the key to sustainable development, or the maintenance of an equilibrium between key resources (Morecroft 2007; Warren 2008; Cosenz and Noto 2016). Based on this evidence, Bianchi (2012, 2016) proposed a DPM approach, adopting the instrumental view of PM illustrated before (Fig. 2.10). Certainly, Bianchi’s approach focused on identifying the system’s end results and related performance indicators, with the goal of driving strategic resource allocation (Bianchi 2012, 2016; Cosenz and Noto 2016; Noto 2017). DPM and dynamic RBV represent strategic resources as “stocks.” An example could be given by the “number of vehicles” in a transportation system, this being a variable that can be measured in units and that can be observed at a given moment in time. Other examples of strategic resources may be the “number of travelers,” the “financial resources available,” the “service quality level” (measured as a proxy of different characteristics), and so on.

Fig. 2.10  The dynamic performance management approach (adapted from Bianchi 2016)

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However, whereas the dynamic RBV approach seeks to analyze these variables, and related models are designed based on the increase and decrease of key strategic resources, DPM focuses on the achievement of the end results and analyzing the system backwards in order to define the performance drivers (or intermediate results) and their connections to the deployment of strategic resources according to an instrumental view of performance (Bianchi 2010, 2016). End results are typically represented as “flows.” A flow can be defined as what changes the stock over a period of time. Returning to the number of vehicles example, one of its flow is the “vehicle purchasing rate.” This can be measured in terms of units perm period of time, and informs us how far the “number of vehicles” has changed during the time period considered. As previously outlined, end results may refer to either outputs or outcomes based on the beneficiaries that take advantage of their achievement. The achievement of end results is contingent on the system’s intermediate results (or performance drivers/indicators), and determined by the employment of strategic resources (inputs). These outputs are usually measured in relative terms by comparing the actual state of a variable with a target value or a benchmark. The gap this comparison creates represents the intermediate result that management should monitor in order to drive performance toward the desired end results. When dealing with social issues such as transportation, indicators should not focus exclusively on economic measures, but should also include aspects that are related to other dimensions of performance, and especially social and environmental ones given their relevance to sustainable development theory (Litman 2016). The DPM model structure is then obtained by linking the system’s stocks and flows that determine a certain behavior of the system over time (Cosenz 2010). In fact, the feedback loops built by following this approach imply that the flows affecting strategic resources are measured over a time lag. Consistent with the SD theory, this approach provides a systemic view of a production/provision process, as each performance indicator demonstrates how the employment of the linked strategic resources affects all the other interdependent resources within the system (Cosenz and Noto 2014). Due to this interdependence, each strategic resource has the power to foster others in the same system. For example, financial resources affect the number of vehicles available to the service through new investments, which in turn influences service quality. An increase in service quality stimulates a growing number of travelers and consequently a rising number of ticket sales, thereby increasing the financial resources available. In initiating performance analysis by focusing on the achievement of the system’s end results, the DPM approach facilitates identification of all of the key strategic resources no matter who owns or controls them (Noto and Noto 2019). This concept refers to the “external” and “internal” perspectives defined by Bianchi (2010), related to the need to shift the boundaries of performance analysis from a single institutional perspective to the inter-institutional network in which public organizations usually operate. Whereas at the single institution level performance is assessed primarily in relation to the effects produced by decision makers at their own organizations, at the

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inter-institutional level performance is assessed in relation to the effects produced by decision makers within the wider system, comprising multiple institutions in the urban area (Bianchi 2016). The inter-institutional system’s performance does not correspond to the mere sum of the performance of every single institution, but can be defined as “the net relationships and synergies among the different institutions linked to each other” (Bianchi 2012, p. 147). This allows the analyst to understand how each organization that plays a role in the system is likely to contribute to the generation of public value for the overall urban transportation system. Public value creation will then provide the conditions for the generation of new value to the benefit of the institutions themselves (Bianchi 2012). Such a shift from a micro to a wider system perspective allows us to broaden the boundaries of the analysis without losing the focus on performance assessment. Figure 2.11 shows how a DPM approach is likely to include different governance levels. The strategic resources affecting service performance can be owned by multiple stakeholders. In order to ensure a proper endowment of such resources and to keep a proper balance between the different strategic resources, different governance levels must coordinate their actions (Noto and Noto 2019). This aspect will be explored further in the following chapter. The DPM framework has already been applied to the urban transportation sector (Noto and Bianchi 2015; Noto 2016, 2017). In the above-mentioned studies, DPM supported the analysts in defining a set of performance indicators that considers multiple performance dimensions. These indicators and their simulated trends proved helpful in providing insights to design and test urban transportation strategies. However, when applied to urban transportation planning, DPM also has some limitations. First, the aforementioned flaws of SD when applied to transportation research are also true in the DPM case (i.e., DPM is unable to deal with traditional network assignment problems and microsimulation). As such, the DPM approach is useful when dealing with problems at the strategic or tactical level, but less so when dealing with the operational one.

Multi-organization system: territory or industry performance

AL

N TIO ITU NST VEL I R LE NTE

I

Shared strategic resources Accumulation rate

L

NA TIO ITU EL T S V IN LE

Depletion rate

Organizational strategic resources Accumulation rate

Single organization system: Financial, Competitive and Social performance

Fig. 2.11  The inter-institutional perspective (Bianchi 2012, 2016)

Depletion rate

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Another challenge is represented by the need to include in the analysis a subjective view of performance according to which objectives are structured into processes that seek to facilitate the achievement of results. In fact, as outlined in the first chapter of this book, the strategic planning phase implies the need to define clear objectives that should be consistent with the urban vision statement. In order to combine instrumental and subjective perspectives into a single PM system, the desired end results and definition of the performance indicators should then be consistent with the transportation system’s objectives. Consequently, identification of the end results that need to be achieved represents a delicate phase because an erroneous translation of objectives into desired outcomes may result in performance distortions. Concerning the definition of performance indicators, in order to ensure correspondence with the subjective view, these should be assessed through a comparison with the specific target outcome of a process of developing the strategic objectives. Performance indicators are thus developed to align decision makers’ activity with the defined strategy. Without such measures, they would not be able to find evidence showing whether their assumptions or decisions are correct (or consistent with their strategy) and whether they are moving in the right direction (Marr 2006). The following graph portrays a synthetic view of the strategic planning process outlined in the first chapter of the present work (i.e., in terms of vision definition, diagnosis, design, and implementation) integrated with the subjective and instrumental perspectives of PM as framed by Bianchi (2010, 2016). The black arrows in Fig. 2.12 represent the strategic planning process, while the gray ones represent the feedback processes.

Fig. 2.12  The planning process framed according to DPM theory

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The combination of the subjective and instrumental perspectives of performance allows us to adopt a dynamic perspective of the whole planning process. The vision box in Fig. 2.12 is related to the first step of defining the objectives of the urban transportation system (subjective view of performance). The diagnosis phase is related to the assessment of the strengths and weaknesses of the system that according to an instrumental view of performance necessitates analysis of the strategic resources that interact within the system. These resources are often owned by different stakeholders who should therefore be engaged in the planning process. The design phase is aimed at structuring the vision into specific objectives, and these objectives into the desired results to be achieved. Through the support of an instrumental view of performance, defining the end results allows us to identify the performance indicators that can be used in the implementation phase to monitor the progress toward their achievement. Based on the results achieved, expressed by the gap between the desired and the actual end results, strategic resources are fostered or deployed (see Fig. 2.12). The implementation phase, which focuses on the intermediate and end results, feeds back on all of the previous phases of the planning process: on the design phase through the definition of a gap between desired and actual end results; on the diagnosis phase through the changes in strategic resources; and on the vision phase through the re-definition of the objective once the end results have been achieved (or not).

2.4  Conclusions This chapter faces the methodological issues that characterize the development of strategic planning for urban transportation. In particular, the need to adopt a strategic management perspective has been articulated through the development and combination of two theoretical approaches: performance management and system dynamics. The first is used to provide planners and decision makers with the tools to define, monitor, and pursue the urban area’s goals with regard to transportation. The second enables us to widen the boundaries of analysis in terms of time (the trade-off between short- and long-term performance) and space (the trade-off between the performance of multiple institutions). Both approaches have been critically discussed to explore their benefits and limitations with regard to urban transportation planning.

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Chapter 3

Governance and Stakeholder Management in the Urban Transportation Sector

The second chapter of this book propounded the adoption of a systemic and performance-­based approach to support urban transportation strategic planning. In particular, through the combination of a subjective and instrumental perspective of performance, a framework was defined linking the strategic planning phases identified by the literature (i.e., vision development, diagnosis, design, and implementation) with PM theory. As mentioned in the first chapter of this book, the urban context is characterized by an institutional fragmentation in which different actors often work in isolation to pursue specific goals related to their individual missions and that are not necessarily synthesized in the development of an urban vision. Moreover, the strategic resources that characterize one of the key elements of the PM analysis are often owned or controlled by different institutions. Consequently, the adoption of a systemic and performance-based approach to support urban transportation planning should be conceived so as to negotiate the multi-level governance structure that characterizes the urban environment (see among others Scharpf 1997; Peters and Pierre 2001). Multi-level governance is characterized by the interplay of different stakeholders in a production/provision process. In a multi-level governance structure, various players (which may be public or private) influence the system by leveraging on one or more urban area’s strategic resources under their direct or indirect control. It is therefore clear that the coordination of resource utilization toward shared objectives represents a key challenge in the provision of public services and services of public interest (Pollit 2003; Christensen and Laegreid 2007; Head and Alford 2015). Therefore, in recent decades, public management literature and practice has shifted its focus toward the analysis and implementation of inter-institutional networks and collaborative governance in order to foster public value creation (Provan and Milward 1995; Kettl 2002; Agranoff and McGuire 2004; Ansell and Gash 2008; Turrini et  al. 2010). These networks have been implemented since the 1990s in

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order to deal with public problems such as local development, social care, health, and education (Provan and Milward 2001; Agranoff and McGuire 2004; Turrini et al. 2010). Evidence shows that urban transportation is also characterized by an institutional complexity that requires the adoption of such network strategies (Tornberg and Odhage 2018). Urban transportation strategic planning activities hence need to consider the formal or informal network structure that characterizes the urban transportation system by adopting an inter-institutional perspective, i.e., a perspective that considers the influence of multiple organizations on a system (Bryson et  al. 2006; Poister 2010; Halligan et  al. 2012; Bianchi and Tomaselli 2015). This may enable planners and decision makers to cope with the pluralism and institutional fragmentation that characterize local areas, and to determine their complexity (Head and Alford 2015; Noto and Bianchi 2015). In order to effectively adopt such an inter-institutional perspective, it is necessary to first identify the key stakeholders that claim ownership of the local strategic resources affecting performance (i.e., outcome achievement). The identification and engagement of these stakeholders—the network actors—represent a preliminary step to set up collaborative governance practices with regard to the management of the urban system’s strategic resources. According to Noto and Bianchi (2015), these collaborative practices should be developed around the “endogenization” of strategic resources within system management. Resource endogenization implies that each strategic resource is causally linked to the other system’s resources and, therefore, provides and receives feedbacks from them so that shared and informed decision-making processes can be put in place. For example, the number and type of vehicles and the decision to increase or decrease this figure should consider the financial resources available, as well as the infrastructure conditions and the number of users of the transportation system, which are also connected to the ticket price level. As previously mentioned, in an urban system these resources are usually managed or controlled by multiple institutions. Resource endogenization, and thus collaborative practices, is contingent on communication, information exchange, and the alignment of objectives among these stakeholders. Only through their active engagement is possible to integrate the transportation system and foster the design and implementation of outcome-­oriented policies (Bryson 2004; Wang et al. 2015). Stakeholder engagement represents a key phase of every strategic planning process (Bryson and Roering 1996; Pierre 1998; Albrechts 2004; Cavenago 2004; Trivellato and Cavenago 2014). Literature and practice provide several insights and tools regarding how to identify, map, and engage stakeholders, including through focus groups, snowball sampling, self-identification, and brainstorming (Bryson 2004). However, some limitations and challenges with respect to this practice remain. To address these issues, this chapter develops a literature review of the scientific contributions related to stakeholder engagement, strategic planning, and performance management. Based on this analysis, an instrumental approach is then developed to identify and assess the stakeholders of urban transportation systems.

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3.1  Stakeholder Management Stakeholder theory was primarily formulated by R. Edward Freeman in 1984 when he published his manuscript “Strategic management: A stakeholder approach.” The crux of his argument was that a strategy would prove effective when it satisfied the needs of the organization’s stakeholders. These were defined as “any group or individual, who can affect or is affected by the achievement of the organization’s objectives” (Freeman 1984, p. 46). Although Freeman’s work was primarily focused on the private sector, the following literature agrees that stakeholder management should also be considered by the public sector in spite of its greater complexity (Bryson 2004; Bovaird 2005; Thomas and Poister 2009; Noto and Noto 2019). In fact, the public sector usually comprises a wider set of stakeholders with different aims, expectations, and values within the context of planning and political problems. Although in the private sector stakeholders are concerned with promoting organizational success—usually expressed in terms of profit—in public decisions, they may have competing goals regarding the system’s management (Stave 2002; Bryson 2004). This increases the complexity of stakeholder analysis and engagement in the public sector, as aligning the conflicting aims of local players proves more difficult (Bryson and Roering 1996). Another difficulty intrinsic to strategic planning processes in the public sector is related to the identification of “who” should be in charge of managing stakeholders. According to Cavenago and Margheri (2006), this is the role of the “promoter,” an entity corresponding to the main actor in charge of local development. In the case of urban transportation planning, this is usually represented by the city council or the municipal administration. Freeman’s definition (1984), which has been supported by subsequent research, is clear about who can be a stakeholder (e.g., a person, group, organization, urban area, and even the natural environment). What is less clear is related to the existence and nature of the stake and what really counts to managers (Mitchell et al. 1997). Ackermann and Eden (2011) have identified three main open issues with regard to stakeholder management: • Identifying who the stakeholders are in the specific situation; • Acknowledging the multiple and interdependent interactions between stakeholders; and • Developing stakeholder management strategies, i.e., determining when and how it is appropriate to intervene in relationships with stakeholders. These three issues can work in synthesis, presented as stakeholder identification (1), mapping (2), and engagement (3). In order to deal with these topics, a broad branch of literature (i.e., stakeholder analysis) has been developed since the early 1990s (see among others Mitchell et al. 1997; Eden and Ackermann 1998; Brugha and Varvasovszky 2000; Aligica 2006; Duggan et al. 2013; Noto and Noto 2019).

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Stakeholder analysis is an activity aimed at evaluating and understanding stakeholders, as well as determining their relevance to a project or policy (Brugha and Varvasovszky 2000; Duggan et al. 2013). Stakeholder analyses in today’s world are of particular importance owing to the increasingly interconnected nature of the world. As previously mentioned, any public problem (e.g., mobility, economic development, natural resources management, crime, health prevention, global warming, terrorism) encompasses or affects a wide number of people, groups, and organizations, rendering identification of a solution problematic. In such “wicked” contexts, no one is fully in charge and no organization “contains” and controls the problem (Bryson 2004). Instead, many individuals, groups, and organizations are involved or affected, or have some partial responsibilities to act. Figuring out what the problem is and what solutions might work is actually part of the problem (Rittel and Webber 1973; Bardach 1998; Bryson 2004; Head and Alford 2015). Whereas in the second chapter of this book a subjective and instrumental view of performance was presented to link objectives with outcomes and strategic resources, this chapter aims to explore the ownership/control relationship of these resources, i.e., the entities who have the power to influence decisions related to which service to offer and how (e.g., providers, public organizations, political bodies). Resource dependence theory suggests that resource control is connected to the importance of stakeholders (Pfeffer 1981; Mitchell et al. 1997). As such, this work refers to the definition of stakeholders provided by Aligica (2006, p.  79): people or groups “whose interests and activities strongly affect and are affected by the issues concerned, who have a ‘stake’ in a change, who control relevant information and resources and whose support is needed in order to implement the change.” According to this perspective, in the following sub-sections and in-depth analysis of stakeholder identification, mapping, and engagement are developed with the objective of providing a comprehensive view of how to deal with the inter-­ institutional network that characterizes urban transportation.

3.1.1  Stakeholder Identification Stakeholder identification in the literature has been explored through two main approaches (Mitchell et al. 1997; Reed et al. 2009): normative and instrumental. Normative approaches focus on the legitimacy of stakeholder engagement in decision-making processes. Such approaches also enable the identification of stakeholders who may not have a direct impact on the desired outcomes, but have a democratic right to participate in the decision-making process. Instrumental approaches are largely devoted to the stakeholders who can influence the achievement of the desired goals. As such, they are based on an understanding of who to involve and how they should be engaged based on their ability to influence the activities, processes, and resource availability linked to the achievement of outcomes.

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According to the purpose of this book (i.e., a performance-based approach to strategic planning and management for urban transportation), we suggest the adoption of an instrumental approach, with stakeholder identification being closely linked to the formulation and deconstruction of the urban system’s strategic goals (Wang et al. 2015; Noto and Noto 2019). In strategic planning processes, stakeholders should be involved when they have information that cannot be gained otherwise, or if their participation is required to ensure the successful implementation of policy actions (Thomas 1993, 1995; Bryson 2004; Johnston et al. 2011). However, when undertaking stakeholder identification, there is always the risk that not all relevant stakeholders of the urban system are identified by the promoter of the strategic planning and management process (Clarkson 1995; Reed et al. 2009). On the other hand, when dealing with public issues such as transportation, it is often not possible to include all stakeholders, and a line must be drawn at some point (Clarke and Clegg 1998; Reed et al. 2009). Indeed, the identification process has an iterative nature (Reed et  al. 2009; Sanò and Medina 2012), and this may result in the identification of almost everybody in the urban area, overloading planners, and policy makers with redundant or perhaps useless information (Pouloudi and Whitley 1997). For instance, several specific categories of users (such as workers and students) may have the same interests and expectations with regard to urban transportation services, and thus offer the same information. However, as Crosby and Bryson (2005, p. 167) note, “there are no hard and fast rules, let alone good empirical evidence, on when, where, how, and why to draw the line.” Previous literature does not explicitly explain how to identify stakeholders in the first place (Mitchell et al. 1997; Thomas and Poister 2009; Noto and Noto 2019), but it mainly suggests the adoption of specific techniques, such as focus groups, brainstorming, snowball sampling, self-identification, experience-based methods, and intuition (Bryson 2004; Reed et al. 2009; Wang et al. 2015; Colvin et al. 2016). The limitations to this set of techniques lie in two main factors: (a) they lack a systemic view and a systematic process (Smudde and Courtright 2011; Salado and Nilchiani 2013; Wang et al. 2015); and (b) they lack a performance-based perspective (Wang et al. 2015; Noto and Noto 2019). In order to overcome the lack of systemic view, Simmons (2003), Simmons et al. (2005), Salado and Nilchiani (2013), and Wang et al. (2015) have suggested combining stakeholder analysis with a soft system methodology (SSM) in defining the system’s structure and boundaries. According to these authors, SSM is able to represent the key activity sets that are essential to achieving organizational objectives, and thus it helps managers to determine the functions and roles of each key stakeholder group in the process of achieving organizational objectives. Pouloudi and Whitley (1997) and Pouloudi (1999) have focused on the importance of considering the inter-institutional nature of the systems analyzed when identifying stakeholders (i.e., systems including different levels of government and private entities) through the network approach, which builds on the identification of the actors, resources, and activities of a system. However, with the exception of Wang et al. (2015), all of these studies lack a performance-oriented perspective, which is particularly relevant in every strategic management process.

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In fact, the identification and mapping of the various stakeholders must be understood by taking into account both the environment in which they operate and how they may influence it through their actions. In this regard, it is essential to understand who are the stakeholders that have a significant influence on the achievement of the desired outcomes of the urban area, i.e., the inter-institutional network. Even though Wang et al. (2015) focused on the stakeholders’ influence on performance, they exclusively adopted a subjective view of performance (Bianchi 2010, 2012). Thus, their main focus was oriented toward the identification of the key activities that the different stakeholders had to put in place in order to pursue the overall objectives. The subjective view of performance, as explained in the second chapter of this book, is aimed at mapping the existing links between objectives, activities, and results. However, it does not identify the resources that need to be employed to influence these results. This information can be gained through the contextual adoption of an instrumental view of performance (Bianchi 2010, 2012). In fact, the instrumental view frames strategic resources and outcomes (end results) in feedback loops that show whether a resource is being accumulated or dispersed over time, and how its holder can affect performance drivers to improve its management. Certainly, according to Pfeffer (1981) resource control is directly linked to power to influence an organization/system. Identifying the people who may leverage on these performance drivers through the control of the associate strategic resources enhances understanding of the inter-­ institutional context that typically characterizes cities and urban areas (Bianchi 2012; Bianchi and Tomaselli 2015). Consequently, Noto and Noto (2019) proposed a stakeholder identification approach based on a combination of subjective and instrumental perspectives of performance. This will be further explored in Sect. 3.2.

3.1.2  Stakeholder Mapping Once the stakeholders have been identified, the literature suggests alternative criteria to map them and to assess their salience, i.e., “the degree to which managers give priority to competing stakeholder claim” (Mitchell et al. 1997, p. 854). This issue goes beyond the identification phase because it implies the exploration of complex relationships that would not easily emerge from the stakeholder identification process previously outlined (Mitchell et al. 1997). Moreover, understanding these relationships could be considered preparatory in order to differentiate engagement practices according to stakeholders’ roles and characteristics. The main studies focusing on this topic are Mitchell et al. (1997) and Eden and Ackermann (1998). Mitchell et al. (1997) defined three relationship attributes that stakeholder identification and mapping should consider: • The power to influence the organizational structure. Power is defined as a dynamic relationship between two or more parties in which one of these can gain

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Fig. 3.1  Mitchell et al. (1997) stakeholder classification

access to coercive, utilitarian, or normative means to impose its choices in the relationship. • The legitimacy of the relationship with the organization. This attribute is distinct from power and refers to the perception that an action is desirable, proper, or appropriate within some socially constructed system of norms, values, or beliefs (Weber 1947; Suchman 1995). • The urgency of the stakeholder’s claim. This exists when a relationship or claim is time-sensitive or when it is critical to the stakeholders. The combination of these attributes results in the identification of seven stakeholder types (see Fig. 3.1). This classification aims to support decision makers in distinguishing those stakeholders they must engage and how. Definitive stakeholders are salient players whose relationships with the urban system exhibit all three attributes above. In urban transportation systems, these stakeholders are usually involved in providing services (e.g., city council, bus companies, local authorities, infrastructure owners). Dominant stakeholders have power and are legitimized to exercise it. However, they do not have an urgent claim. This group does not differ from the previous because the urgency characteristic is dynamic, and when the urgent claims of definitive stakeholders are satisfied, these can automatically be considered dominant. Dangerous stakeholders have the power to influence a relationship and the urgency to exercise this power, but they lack the legitimacy. According to Mitchell et al. (1997), these stakeholders can be deemed potentially dangerous to the functioning of the system. Imagine, for instance, a key supplier (e.g., a maintenance provider) that decides to suspend a supply and terminates its contract from one day to the next. This may cause several issues that result in the interruption of the public transportation service for some days. Dependent stakeholders have no power to impose their will but do have legitimate and urgent claims. This group includes small groups of citizens asking for a change to a service, which may not be a priority to the provider.

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Dormant stakeholders are those who have the power to influence a relationship but do not use it because they do not have a legitimate relationship or an urgent claim. This could be the case of a group of entrepreneurs external from the urban context who may decide in future to invest in an area of the city (e.g., by building a mall), thereby influencing significantly the urban travel demand. Discretionary stakeholders are legitimate in their relationships but do not have either the power or urgency to claim their will. For example, we may imagine the case of a small firm asking for the closure to traffic of a street with the aim of improving the quality of a commercial area. Demanding stakeholders have a relationship characterized by urgency but not the power or legitimacy, e.g., a picketer asking for absurd claims. These categories are not static, but are characterized by dynamic relationships in which some players may gain or lose power or legitimacy, or some urgent claim may arise. Mitchell et al. (1997) developed this systematic sorting of stakeholder relationships in order to help decision makers to deal with multiple interests. Another conceptual model developed in the literature with the aim of classifying the stakeholders identified has been suggested by Eden and Ackermann (1998), albeit according to the dimensions identified by Freeman: power and interest. These authors developed a “power-interest grid,” mapping potential stakeholders on a two-dimensional grid (Fig.  3.2). The result is a four-category matrix (Eden and Ackermann 1998; Ackermann and Eden 2011; Wang et al. 2015): • “Players,” who have strong interest and power (e.g., city council, bus companies, key suppliers);

Fig. 3.2  The power-interest grid (adapted by Ackermann and Eden (2011))

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• “Subjects,” who have strong interest but lack power (e.g., citizens using the service); • “Context setters,” who have power but relatively little interest (e.g., national government); and • The “crowd,” who are low on both (e.g., non-users of the service). Although these frameworks are non-exhaustive of the schemes produced in the literature, they represent two of the main tools that can be used to understand what a stakeholder wants and how it is going to work to achieve it. As such, they provide the basic information to design engagement strategies and actions. However, these frameworks do not consider the ownership of strategic resources, which is the key to implementing the policies and actions designed. The information produced by these frameworks could thus be enhanced by mapping the dynamic relationships that link each stakeholder category through the system’s strategic resources that they own/control. This dynamic view may support the development of a shared mental model (Kim et al. 2013) for the urban area with respect to the transportation system. The process of making explicit the shared mental model or the urban transportation system is critical for two main reasons. On the one hand, it fosters communication and information exchange between the stakeholders involved. On the other hand, it enables double-loop learning mechanisms (Argyris 1991). This kind of process could be implemented through the adoption of stakeholder engagement techniques. These are explored in the following section.

3.1.3  Stakeholder Engagement According to Gao and Zhang (2006), the engagement approach with stakeholders should differ depending on the characteristics of the distinct groups derived from the mapping process. Gao and Zhang maintain that the more relevant the stakeholder (e.g., in terms of power, legitimacy, and urgency), the more inclusive and proactive the engagement approach adopted by the plan promoter should be (Table 3.1). A passive approach may be limited to (for instance) informing stakeholders through public media, reports, and documents. Meanwhile, listening is related to the collection of information from a set of selected stakeholders, while a proactive approach involves including key stakeholders in decision-making processes. The engagement process should be guided by the mapping activity previously mentioned. As such, the tools explored to map stakeholders may help determine Table 3.1 Stakeholder classification and level of engagement (adapted from Gao and Zhang (2006))

Stakeholder type Mass stakeholders Selected stakeholders Key stakeholders

Level of engagement Passive Listening Proactive

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Fig. 3.3  Engagement level, adapted from Burford (2013)

which category of stakeholder needs what. For instance, Burford (2013) links an engagement level to each category of the power-interest grid (see Fig. 3.3). Planners and policy makers should then choose the specific processes through which to achieve the desired engagement level with each stakeholder. Bingham et  al. (2005) distinguish between the quasi-legislative and quasi-­ judicial processes that can be used by public planners when engaging stakeholders and the public as a whole. Quasi-legislative processes comprise deliberative democracy, e-democracy, public conversations, participatory budgeting, citizen juries, study circles, collaborative policy making, and other forms of deliberation and dialog among groups of stakeholders or citizens. These may be carried out through focus groups, roundtables, social networks, web-platforms, town meetings, and so on. Such techniques may differ depending on the target stakeholders that need to be reached. Smaller and informal processes, such as focus groups, may be aimed at building trust with key and selected stakeholders, whereas larger and more structured processes (e.g., workshops, polls, conferences) may be suitable for involving the broader community. For example, Olokesusi and Aiyegbajeje (2017) report that e-democracy represented a successful tool to foster participation in urban transportation planning in Lagos, Nigeria. E-democracy seeks to develop digital citizenship through the use of ICT to create personal contact, dialog, and consultation among participants in a democracy (Briony 2003). In Lagos, e-democracy has helped the movement of about 7 million passengers per day through the introduction of e-­ticketing, the involvement of the masses in decision-making via media, and the

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introduction of traffic radio stations to divulge information pertaining to traffic situations in all areas of the metropolis (Olokesusi and Aiyegbajeje 2017). Quasi-judicial processes include mediation, facilitation, fact-finding, and other forms of arbitration (Bingham et  al. 2005). These processes always involve an impartial third party to facilitate dialog and conflict resolution between stakeholders. Among these processes, some innovative tools such as group model building (GMB)1 may foster the effective engagement of key stakeholders. GMB refers to a SD model-building process in which stakeholders are deeply involved in the process of model construction. For instance, Stave (2003) used GMB to engender public understanding of water management options in Las Vegas (USA). In particular, the author tackled the problem of communicating with resource stakeholders about a complex and dynamic resource system with the aim of reducing its complexity, while explaining the key elements that govern its response to policy interventions. Resource managers were enabled to engage the interests of stakeholders with different levels of technical expertise. It was found that GMB greatly enhanced participant discussions about the system and to create a shared understanding and mental model of the system. “The use of the model shifted the discussion from who was to blame for the water problem […] and how to solve it […] to how the system works and why it responds to policy changes as it does” (Stave 2003, p. 311). Both kinds of processes require the development of a legal framework and the development of public managers’ skills in collaboration, negotiation, and facilitation (Bingham et al. 2005). These skills are key to answering a set of questions that may arise during a citizen and stakeholder engagement process, such as: Which process should be used? At what point(s) during the public policy cycle are new governance processes best used and most effective? How can differences in stakeholders’ knowledge, participation, power, and authority be considered during the process? How do these processes affect participants’ perceptions of the legitimacy of policy, the policy cycle, and government? And so on. Emerging from the literature on stakeholder analysis and the experiences reported here (see Box 3.1), some limitations may be identified with respect to the practices of identifying, mapping, and engaging the stakeholders of an urban transportation system. First, identification remains a critical issue. Second, mapping processes do not explicitly take into account the strategic resources’ ownership/control relationship, which is pivotal to understanding the functioning of the urban transportation system and the feasibility of the policies designed. Third, a push toward tools such as GMB should be activated to foster the development of a shared mental model between all of the governance levels and key stakeholders of the system. To address these issues, the next section suggests the adoption of DPM.

 In-depth explanations of GMB may be found in Vennix (1996) and Andersen and Richardson (1997).

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Box 3.1 The Process of Stakeholder Engagement in Two Strategic Planning Experiences This box aims to provide an overview of two real experiences of stakeholder engagement during urban strategic planning processes. The two selected cases are: Trento, a medium-sized city located in northern Italy (about 120,000 inhabitants); and Auckland, the biggest city in New Zealand (about 1.3 million inhabitants). Although the experiences do not specifically refer to the transportation sector, they represent interesting examples of how planners and plan promoters decided to deal with urban stakeholders. The Trento Strategic Plan2 Trento’s strategic planning experience was initiated in 2001 and released in 2003 and was promoted by the Municipality in collaboration with the local university. According to the literature’s prescriptions (see Chap. 1), the planning process was articulated in four phases: (a) preliminary agreement, (b) diagnosis phase, (c) design phase, and (d) implementation. The Municipality of Trento and some key city stakeholders—identified by the Municipal Committee—subscribed to the preliminary agreement in 2001. This represented the very first step in the planning process. The aim of this phase was to involve everyone who had a responsibility regarding local development. Stakeholders were selected by both the Municipality and the city’s university through a top-down process. They belonged to diverse institutional levels and were representative of various sectors, such as education, economy, culture, and research. During the diagnosis phase, the planning team established three technical panels focused on the following topics: territory, culture, and services. A total of 250 people belonging to different groups and organizations participated in these panels, with the aim of identifying the strengths, weaknesses, opportunities, and threats of Trento. This second phase was open to everyone interested in the panels’ topic, and the Municipality solicited the participation of potentially interested parties to create an identification method based on self-­ selection and the use of media. The outputs generated by this process were then harmonized with other specific planning experiences that were being implemented at that time. The third phase (design phase) saw the publication of a draft document containing the results of the previous phase. This document was articulated in strategic axes and objectives. In order to develop each action in greater depth and to test the consensus on the designed objectives, the planning team established five panels. These were focused on: infrastructure, services, education, tourism and culture, and urban environment and quality of living. This time,

 Further details can be found in Noto and Noto (2019).

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321 people participated in the panels with the aim of effectively designing the previously identified measures and actions. In this phase, the selection process was bottom-up: interested parties self-selected themselves. The Auckland Strategic Plan3 The Auckland Plan was promoted by the City Council and released in 2012. According to the Local Government Act of 2002, New Zealand’s local governments are required to interact with the wider community when developing plans. In order to do this during the development of the Auckland Plan, the City Council had to adopt a special consultation procedure. The plan formulation process began in September 2010. The first step to involve external actors was to identify the different groups carrying interest in the Auckland region. The first collaborations were installed with the local boards (elected entities that represent local communities) and the Council-controlled organizations. The latter represent an important junction between the Council and citizens as they deliver the principal public services. In some cases, these stakeholders already had their own plans and strategies, helping the Council’s planners to consider and represent their interests in the Auckland Plan. The consultation with the wider group of stakeholder occurred through some key discussion documents prepared by the Council: • • • • •

Auckland Unleashed: Discussion Document (March 2011); Draft Auckland Plan (September 2011); Draft Economic Development Strategy (September 2011); Draft City Centre Masterplan (September 2011); Draft Waterfront Plan (September 2011).

These represented the most important inputs in order to ensure the presence of dialog within the Plan’s development. Auckland Unleashed was the first step toward the development of a shared vision, and was presented to the community in March 2011. The writing of the first document was conducted primarily through contributions and analyses from previous strategic planning experiences, which had highlighted the strengths and weaknesses of the region. Auckland Unleashed outlined the needs of the community and the objectives that had to be met. Due to the limited time schedule provided by the governmental act, collaboration with stakeholders in the writing of this document was limited to a small group identified by a concentrated selection process. The discussion in Auckland Unleashed included meetings and workshops with the 21 local boards, public services providers, firms, academic

 Further details can be found in Noto (2012).

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and professional institutions, and community groups. In this first phase, it was difficult to involve the local boards as they were newly established organizations. Although Auckland Unleashed did not represent a wide discussion process involving the whole community, it did represent the starting point for stakeholder engagement. During this process, about 9000 answers were collected. Through the feedback analysis on the document, the Council was able to produce a draft Auckland Plan. The draft plan was published in September 2011 and collected almost 3000 submissions (of which 2000 were about the draft Auckland Plan and 200 the draft Economic Development Strategy). The spreading of awareness regarding these documents was undertaken by the media. The submission procedure occurred between September 20 and October 31, 2011. This was followed by a hearing process in order to allow everybody to share their thoughts about the plan’s programs within the following 24 days. The total number of submitters who shared ideas on the draft plan was 671. In addition to the hearing process, the Council management attempted to gauge the perceptions of the community by using and interacting with social networks, independent blogs, and 2.0 platforms. Workshops and meetings were also organized on specific issues regarding Auckland: economy, infrastructure, society, environment, Council-controlled organizations, city center, sports, and elderly people. Before these meetings, the draft documents were distributed to possible groups of interest through websites, libraries, and ad hoc centers. Stands/stalls with experts on the information were set up in different parts of the city, specific newsletters were provided, and strong presence on traditional media was also initiated. Local boards were constantly called upon to offer their contributions to the consultation and development of the plan during this process, and were encouraged to engage until its completion. After the publishing of the plan in March 2012, energy was shifted to informing the community about the progress of its implementation through newsletters, websites, and monthly magazines. From analyzing the Auckland Plan’s stakeholder engagement strategy, it is possible to extrapolate and identify a summary list of key stakeholders chosen by the regional council. For each group, the related engagement techniques have been identified. • • • • • •

Council-controlled organizations: council and technical meetings; Central government: ministerial and officer meetings; Local boards’ partners: business meetings, symposium, cluster workshop; Maori Statutory Board: regular meetings and technical meetings; Advisory panels: regular meetings; Tangata Whenua: technical meetings;

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• • • • • •

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Targeted individual sector: workshops and meetings; Targeted multi-party stakeholders: workshops and presentations; Community organizations: summits; Neighboring councils: meetings; Social Policy Forum: regular meetings; Community: survey, meetings, media releases, newspapers, social media, display.

Lessons learned In both cases, the first key stakeholders were identified by the plan promoters (city councils, and the university in the Trento experience) through a topdown approach. This was a political choice made in order to give a clear direction to the plan and the future of the cities, consistent with the municipal administrations’ visions. Such a method has been adopted by many strategic planning experiences for two main reasons: elected political representatives tend to feel that they are legitimate in enforcing their visions because of their political mandate; and because quite often the vision has already been discussed in the pre-election period. From the diagnosis phase onwards, the planning processes were then open to a wider group of stakeholders, which were identified through the self-selection of the interested parties (bottom-up selection). This activity was also used to obtain wider legitimation with respect to the wider community. In both cases the stakeholders’ contributions allowed them to receive important feedback and to improve the final output. These experiences demonstrate the difficulty in initiating the process of identifying both key and mass stakeholders. This is explained in part by resistance from the political party that does not want to risk losing control of the overall process, and in part by the lack of a scientifically sound methodology. The risks related to this challenge are linked to the possibility of failing to involve all of the key stakeholders that genuinely influence the performance of the local area, as well as to losing legitimacy in the overall process.

3.2  S  upporting Stakeholder Analysis Through Dynamic Performance Management As mentioned in the first chapter of this book, in the public sector the need to understand the inter-institutional context is critical to undertaking successful stakeholder analysis. Thus, the literature identifies the “network approach” as one of the main theoretical tools that can support the creation of public value in fragmented governance contexts (Cunningham and Tynan 1993; Pouloudi and Whitley 1997; Agranoff and McGuire 2001; Bovaird 2005; Andrews and Beynon 2017; Noto and Noto 2019). Håkansson (1987, p. 14) defines the actors of a network as “those who perform activities and/or control resources within a certain field.”

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The idea behind the adoption of a network approach is related to its support in defining the boundaries of the system, i.e., who the network actors are. Given that the identification of actors in a network is closely linked to their impact on the system’s resources (e.g., Do they control any strategic resource? Do their activities impact strategic resources’ deployment/restoration?), detecting the strategic resources of a system is a preliminary step in defining the network of actors that control these resources. In the second chapter of this book, DPM was introduced as an approach that could potentially support the urban transportation sector. In particular, it was maintained that by combining SD modeling with PM, the DPM approach enables us to adopt a systemic view of an urban area’s development because each performance driver shows how the deployment of the linked strategic resources affects all of the other interdependent resources within the system (Bianchi 2016). Due to this interdependence, each strategic resource has the power to foster others in the same system through the achievement of the system’s end result. For example, one may imagine that a more sustainable transportation system will impact, in the long run, on other strategic resources, such as firms’ production and income levels, with levels of firm production and income conceived here as strategic resources (information stocks) because they directly determine the financial resources that are collected yearly by the public administration to deliver public services. These are directly related to the taxes that can be collected from local businesses and then invested in the design and implementation of the sustainable policies. According to Noto and Noto (2019), DPM may also support planners and policy makers in adopting a performance-based perspective when managing stakeholders. In fact, by making the desired end results (outcomes) of the urban area explicit, DPM helps us to analyze backwards how these are achieved through the identification of the system’s performance drivers, which are directly and causally determined by the deployment of strategic resources (Bianchi 2012, 2016; Noto and Bianchi 2015). Through the identification of these resources, planners may then identify the stakeholders claiming control over them, i.e., the network. Each stakeholder identified will then contribute to the mapping of the system, identifying new strategic resources and stakeholders that must be engaged in order to influence the intermediate and end results. The iterative process will cease when all of the resources’ owners are identified and involved. Such a process will move planners toward the “endogenization” of all the system strategic resources in a causal model representing the urban area’s main dynamics (Noto and Bianchi 2015; Noto and Noto 2019). In order to design comprehensive strategies aimed at improving urban transportation performance, it is necessary that each resource receives physical/information feedbacks from the system performance itself (Noto and Bianchi 2015). This may benefit from quasi-judicial engagement techniques such as GMB. Summing up, DPM may support stakeholder identification, mapping, and engagement through four sub-sequential phases (Noto and Noto 2019): • Outcome definition: the urban area’s objectives should be converted into a set of desired end results, e.g., to improve service quality in terms of network extensiveness.

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• Performance drivers’ elicitation: the second phase is devoted to the understanding of how outcomes may be realized through the achievement of instrumental intermediate results, e.g., the average distance between stops over a desired distance; the number of connections between key nodes of the network over desired node connections, etc. • Strategic resource identification: after the elicitation of the system’s performance drivers, planners should identify the resources that must be deployed in order to achieve these outputs, e.g., infrastructure, vehicles, staff. • Stakeholder (who owns/controls strategic resources) identification: the last phase of the process concerns a thorough analysis of the resources identified that allow planners to identify the urban network of actors that own/control or have a strong influence on each of them, e.g., the municipality, transportation company, or governmental agencies. In Fig. 3.4, the DPM approach suggested in the second chapter is combined with stakeholder identification and engagement. As one can see, the identification and analysis of stakeholders through the DPM approach is linked according to an iterative process to the development of a vision for the urban transportation system. The vision is then declined into specific ­objectives, which in turn lead to the identification of outcomes, performance drivers, and strategic resources. If owned or controlled by not already engaged stakeholders, this leads to the involvement of new stakeholders and to the potential redevelopment of the original vision.

Fig. 3.4  Stakeholder analysis and strategic management

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In the first instance, the DPM approach may thus support planners and decision makers in the identification of the key urban transportation system stakeholders. Through the linking of results, performance drivers, strategic resources, and key actors resulting from the analysis, DPM also provides a first map of the stakeholders’ impacts on the urban transportation system. This is instrumental to developing the diagnosis phase of the planning process (see Chap. 1). Lastly, DPM provides those measures (performance drivers) that planners and decision makers need in order to monitor the urban transportation system’s performance during the implementation phase. Performance drivers may be represented as performance indicators that benchmark current performance in terms of intermediate results with standards, goals, or the performance of other cities and systems. Therefore, as suggested by Bianchi and Tomaselli (2015), DPM may support planners not only in identifying and engaging with stakeholders, but also in guiding the whole strategic planning process introduced in the first chapter of this book: • Diagnosis: DPM provides a causal map of the urban transportation system and helps in the identification of the key stakeholders; • Design: based on the causal map, DPM elicits stakeholders’ knowledge of the system (i.e., their mental model) and activates double-loop learning processes (Argyris 1991). This allows local players to experiment with changes in the system structure and, with the help of simulation, to explore the resulting behaviors. This process will then result in the definition of outcome-based policies. • Implementation: the definition of the performance drivers/indicators allows planners and policy makers to monitor whether the result achieved is aligned with that expected, and to promptly react if necessary. The main limitation of the DPM perspective in performing stakeholder management is related to the nature of the instrumental approach. As a result, the exclusive adoption of DPM may discriminate against those groups of stakeholders who, although influenced by the urban transportation system’s performance, do not control any strategic resources and would therefore be excluded in the first place from participating in the planning process. Although this may be considered scarcely relevant for private organizations, it is not entirely negligible when dealing with the public domain. In order to cope with such a limitation, the DPM approach should be supported by effective public participation methods, such as social media campaigns, surveys, and public debates. Another limitation of DPM in supporting stakeholder management is related to the lack of proper classification of stakeholders (e.g., players, subjects, context setters, crowds), as these are exclusively labeled as “resources” owners/controllers. Thus, in order to facilitate the design of proper engagement strategies, DPM could be supported by other methods of analysis, such as those suggested by Mitchell et al. (1997) or Ackermann and Eden (2011).

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3.3  A  n Application of Dynamic Performance Management to Analyze the Governance of Palermo’s Urban Transportation System Palermo is an Italian city located on the island of Sicily. Its metropolitan area accounts for about one million inhabitants. Since the NPM reform introduced in the 1990s, the Italian transportation sector has been mainly characterized by a political orientation toward power decentralization to local governments and the privatization of public companies (Noto and Bianchi 2015). Today, public services in Italy are often run by joint stock companies controlled by public administrations such as regions or municipalities (Mussari 2005; Grossi and Mussari 2008). Due to this transformation, these companies must comply with a multi-level governance system whose stakeholders may have different expectations in terms of economic, social, and environmental performance. In order to understand Palermo’s urban transportation governance structure through the identification of the key stakeholders and their levers on the management of the urban transportation sector, this section illustrates the stakeholders’ identification process, developed according to the DPM perspective. This case study is not focused on decisions related to infrastructure investments, but it concerns the management of the public transportation service. As previously mentioned, the first step of the DPM analysis is related to outcome identification. Thus, various interviews and focus groups were conducted with the management of the Municipality of Palermo and AMAT, the urban public transportation provider. AMAT is a joint stock company that was founded in 2005 with the personnel and other resources of the Municipality of Palermo, which today still holds 100% of its shares. The interviews with AMAT’s management date back to 2013–2014, when the urban public transportation service was run exclusively through a bus fleet.4 According to these interviews, the desired outcomes that AMAT should pursue are related to: (a) improving the quality of the bus transportation service; (b) financial sustainability; and (c) the accessibility of the public transportation service. Quality is mainly influenced by three characteristics of the service: • Extensiveness of the service network: This is related to the capability of the system to connect the different urban areas within the metropolitan region. • Frequency of the service: This concerns how many times each line runs during the day. • Timeliness: This is related to the ability to perform each trip on time with respect to the planned schedule. The financial sustainability of the provided service depends on the relationship between the revenues collected to perform the service and its costs.  Today AMAT also manages the tram service that was started in 2017.

4

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Fig. 3.5  Palermo’s transportation system DPM model

Revenues depend on the number of tickets sold and the incomes AMAT receives from its service contract with the Municipality (not based on service characteristics) and the regional administration (based on the number of kilometers traveled during a year). The expenses are mainly determined by the staff and variable costs related to the use and maintenance of the buses. The third desired outcome, accessibility, is contingent both on the population’s ability to afford the public service, and on the extensiveness of the network previously described. In this work, considering that extensiveness has been addressed as a characteristic of quality, accessibility was measured by focusing on the ticket price lever. From these three main outcomes, it was possible to define some performance drivers and to identify the strategic resources causally linked to them (see Fig. 3.5). According to the analysis previously described, the six performance drivers are the following: frequency and timeliness; extensiveness; kilometers traveled; public transportation; bus age; ticket price. These represent the key levers on which the network stakeholders should act in order to foster outcome achievement. • Frequency and timeliness: This indicator was measured as a ratio between the actual service’s frequency/timeliness and that desired. The frequency quality ratio is an indicator of effectiveness and measures the ability of AMAT to deploy its own resources (buses and drivers) in order to ensure the greatest frequency/ timeliness possible. • Extensiveness: The extensiveness of service was measured as the number of lines the company offers. During the analysis, the AMAT service runs 90 lines. • Kilometers traveled: This depended on the extensiveness (number of lines), the frequency of service, and the average length of lines. This indicator was built by comparing distance traveled (measured in number of km) by the company’s buses during the year, with the distance requested by the region in order to perform the service contract’s duties. This could be considered as an indicator of effectiveness that shows AMAT’s ability to perform specific service requirements.

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• Public transportation: This considers the likeliness to adopt public transportation instead of private transportation. The driver was formulated by comparing the number of trips on public transportation with the number of trips in private transportation. This has a direct impact on traffic congestion, which determines the average speed of public transportation, and consequently its timeliness. • Bus age: This indicator directly affects the cost of maintenance. Buses younger than 5  years require an ordinary cost of maintenance (i.e., filters, oil, etc.), whereas non-ordinary costs (i.e., engine repair) are important expenses for bus suppliers. However, buses older than 5 years determine a greater cost for AMAT, as it is in charge of both ordinary and non-ordinary costs. This intermediate result is affected by the investment rate (bus purchase rate). • Ticket price: Ticket price, as microeconomic literature demonstrates, has a direct impact on demand for a service. The performance indicator was built by comparing the ticket price level with users’ willingness to pay for the service. These performance drivers depend on the deployment of the following system’s strategic resources: frequency and timeliness levels, number of lines, quality of the service, buses, staff, financial resources, and ticket price level. Overall, these strategic resources rely on the actions of multiple stakeholders. According to the stakeholder analysis process suggested by the DPM approach, by analyzing in depth each resource, is it possible to identify the key stakeholders who control, own, or have a major influence on them. In the following table, each strategic resource is linked to the key urban transportation system that stakeholders that have the legitimacy or power to influence. Regarding levels of frequency and timeliness, the analysis shows that this resource mainly depends on AMAT’s performance and ability to provide an effective service to citizens. In contrast, the number of lines is determined at the political level in order to guarantee access to all urban areas, even when the routes are not profitable for AMAT. The decision regarding how many and which lines to provide to citizens is taken by the Municipality together with AMAT. Service quality relies on the deployment of the above-mentioned resources. Therefore, we can consider the quality service under the managerial levers of both AMAT and the Municipality of Palermo. The purchasing/dismissing of buses should be under the control of AMAT. However, the company’s economic model structure does not allow it to plan such investments. The investment rate thus appears to be determined at a municipal level through the allocation of specific funds. Concerning the hiring/attrition rate of the staff, AMAT faces legal constraints on the one side determined by the Italian Stability Pact (Articles 550–569 of the Italian Law n.147/2013), and on the other by the nature of the employees who are contracted as civil servants. As such, employment policies are mainly determined at the municipal level. Financial resources are managed by AMAT, but they depend on revenues, of which 50% are allocated by the regional administration, 40% by the municipal administration, and 10% from the selling of tickets.

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Fig. 3.6  Palermo’s transportation system SD model, adapted from Noto and Bianchi (2015)

Lastly, the minimum ticket price level is determined at the regional level. AMAT may decide to lower this level only if the Municipality guarantees that it will cover the difference. Through the adoption of DPM, it was possible to identify the key stakeholders and their role in affecting the urban transportation service. Furthermore, from this analysis emerged a first causal map of the system analyzed. The resulting causal loop diagram is displayed in the following graph. This representation is used because it facilitates comprehension of the dynamic relationship within the urban system and, thus, the diagnosis of the transportation system. Figure 3.65 shows three main reinforcing loops (Rs) related to the dynamics generating revenues, and two balancing loops (Bs) concerning the origins of costs. Two further reinforcing loops show the feedbacks from the community to the service quality standard (Noto and Bianchi 2015). R1 loop shows how the achievement of financial results allows AMAT to invest in new buses. The presence of new buses enables the company to provide the service with greater frequency. This directly affects the number of kilometers traveled every year, which, based on the service contract with the regional administration, determines the amount of funds the same region allocates to AMAT for its service. These funds are one of the main components of the financial result. B1 loop represents the dynamics by which an increase in the number of buses (determined by positive financial results) stimulates an increase in the cost of main This figure is a simplified version of the running model used to simulate Palermo’s transportation system’s behavior. 5

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tenance, which will be added to the total cost the company yearly incurs. Total cost is the negative component of the net income. B2 loop represents the higher cost the company incurs when the number of kilometers traveled per year grows. In fact, when the number of kilometers traveled increases, owing to greater frequency related to an increase in the number of buses available, the total cost of gasoline and other variable costs increases, too. R2 loop represents the dynamics related to citizens’ satisfaction. This loop was built based on the hypothesis that citizens’ satisfaction mainly depends on the quality of service. An improvement in quality would stimulate greater adoption of the service by citizens and, thus, higher revenues from ticket sales. This will positively affect the financial result, which may allow the manager to invest in new buses in order to ensure better service quality. In conclusion, R3 loops show how an increase in service timeliness and frequency may cause a reduction in private transportation and hence traffic congestion over time, which will allow buses to run faster and more frequently. The model displayed in Fig. 3.6 may represent a first strategic map related to the functioning of the urban transportation system of Palermo. Through the support of simulation, planners, and policy makers can experiment with solutions to enhance collaborative governance among the key stakeholders, namely AMAT, the Municipality, the regional administration, and the citizens. In the graphs displayed in Figs. 3.7 and 3.8, historical data on the financial results were compared with the simulation model in order to test its validity. Replicating the system behavior with a low deviation from the historical data is important, but it is not enough to demonstrate the reliability of the model. Therefore, some other tests (structure verification test, extreme condition test, and sensitivity analysis) have been conducted in order to validate the model (Barlas 1996). One can see that AMAT has encountered a dynamic problem, which consists of a declining financial result and a declining quality of service.

Fig. 3.7  Behavior of financial results

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Fig. 3.8  Behavior of quality of service

Fig. 3.9  Palermo’s transportation system SD model with policies

In order to foster collaboration and coordination between the stakeholders involved, it is necessary to reform the system structure so that the exogenous strategic resources (i.e., the resources managed without receiving any feedback from other systems’ variables) can be “endogenized” (Noto and Bianchi 2015). These structural changes are thus based on communication and information exchange between the stakeholders that possess strategic resources. As a result, two loops have been added to the system, “endogenizing” the exogenous variable: ticket price and municipal service contract (Fig. 3.9). “Tickets sold” is a strategic resource influenced by both user satisfaction and ticket price level. In Palermo’s case, the ticket price level is a strategic resource controlled by the regional administration. This governance level does not receive

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Fig. 3.10  Simulation of the ticket price policy

any feedback from the company’s performance (in terms of tickets sold) in order to decide how much to ask users for a ticket. Adding a new causal link to the extant system’s structure would give the regional administration the possibility of intervening on this variable, acknowledging the effect of its decision on the whole system. With this new link, a new feedback loop (B3) may be put in place so as to strengthen coordination between the governance levels involved. The simulated result of this policy is displayed in Fig. 3.10. By looking at the simulation results, we notice that the proposed solution is not solving the dynamic problem related to AMAT’s negative financial result. Moreover, in observing the pattern of service quality, which can be considered a good measure of value created, we see that it remains below the reference value and does not present a clear growth path after the policy’s application.

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Concerning the endogenization of the municipal service’s contract, this variable was connected with bus age to ascertain whether system performance would be supported. In this second endogenization solution, whenever the average bus age is higher than the benchmark, the Municipality provides more funds to the service provider in order to buy new buses. The diagram displayed in Fig. 3.9 shows the cost loop (B1) already described, as well as the new reinforcing loop (R4) that relates the system performance (bus age) to an exogenous resource (municipality service contract). Whenever the bus age indicator scores below its reference benchmark (10 years), the Municipality receives this information and allocates more funds to the provider to invest in new assets. Applying this policy, the financial result becomes positive within 8 years. The first graph displayed in Fig.  3.11 shows the financial result trend without any

Fig. 3.11  Simulation of municipality service contract policy

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p­ olicy application, and the same variable simulated with the implementation of the new R4 loop. The second graph shows how the buses’ age performance indicator develops by applying this policy. Even in this case, we may observe a decisive change of trend in the performance indicator’s behavior from when the policy was applied. This solution also had a stronger impact on the creation of public value. As we may notice in the following graph, the service quality underwent rapid and determined growth from 2013, the first year of the policy’s application. What emerges from the analysis is that allowing information exchange between the stakeholders of an urban system is key to fostering performance. This happens because a system’s strategic resources are owned or controlled by multiple institutions, which may have different and sometimes competing goals. In the case of Palermo, the six performance indicators are under the control of three different stakeholders (see Table 3.2). This makes it particularly challenging for each to make decisions, as they do not know what the other stakeholders are doing with respect to specific aspects of the service. The set of performance indicators provided could thus inform each decision maker about how the system works and therefore encourage them to adopt a systemic view and exchange information. The endogenization of resources in the system (that is to say, the feedbacks a resource attains from the system) may be pursued through the effective identification and engagement of the key stakeholders.

3.4  Conclusions This chapter has discussed a key topic of strategic planning that is often under-­ evaluated by urban transportation planning studies: dealing with the institutional framework, stakeholders, and the community. It has been demonstrated that this activity crosses every single phase of strategic planning processes, from vision development to the implementation of the actions designed. The chapter has presented and explained a tailored approach to stakeholder analysis and engagement in urban transportation planning processes. In particular, it has discussed how DPM may offer an instrumental solution to identify the key stakeholders of an urban transportation system, to make a diagnosis of the urban context, and to support policy development and the implementation phase through the development of performance measures. Such an approach also provides the basis for the implementation of shared accountability among the strategic resource owners or controllers involved in a collaborative governance arrangement linked to a public issue, such as transportation.

1st level stakeholders 2nd level stakeholders 3rd level stakeholders

Frequency and timeliness levels AMAT Municipality

AMAT Citizens

Quality AMAT

Number of lines Municipality

Table 3.2  Palermo’s transportation system stakeholders

AMAT

Buses Municipality AMAT

Staff Municipality

AMAT

Financial resources Regional administration Municipality

AMAT

Ticket price level Regional administration Municipality

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Chapter 4

Modelling Urban Transportation System Through Dynamic Performance Management

The previous chapters of this book have focused on the development of a new approach to support strategic management and the planning of urban transportation through the adoption of dynamic performance management (DPM). The present chapter explores the technical details of how to develop DPM models to analyze urban transportation dynamics. In particular, this chapter is aimed at discussing how DPM may support planners, policy makers, and stakeholders of various institutional levels in understanding the dynamics behind urban transportation systems. To pursue this objective, a general model of the urban transportation system is presented and discussed. It frames the key modules that characterize every urban transportation system according to a DPM perspective. These are: travel demand, transport supply, travel mode choice, the economy, and the population. The different modules are explored in detail in the following sections, discussing through various examples the potential modeling solutions that might be adopted to describe the associated dynamics. A case study based on an urban area of Buenos Aires, Argentina is then developed and discussed to provide a concrete example of how DPM could be used to support strategic planning in a real urban context. The final section is devoted to exploring the process of validating DPM models both at a conceptual and at a technical level.

4.1  A  General Model to Understand the Drivers of Urban Transportation Performance Urban transportation systems are characterized by considerable specificity derived from environmental, physical, social, and institutional characteristics that distinguish every city and every urban area around the world. Consequently, it is not possible to develop a quantitative model applicable to every urban system without

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considering the context-specific factors that interact with inter-institutional arrangements in generating the outcomes (Fedele et al. 2016). However, it is possible to define a general system structure that includes the main dimensions that usually characterize urban transportation and that makes explicit the dynamics linking those dimensions together. The literature identifies travel demand, transport supply, and modal share as the key components of every transportation system (Manheim 1979; Florian et al. 1988; McNally 2000; Wang et al. 2008; Haghshenas et al. 2015). These subsystems, or modules, receive a feedback from the system’s results in a continuous adaptation process toward the achievement of overall system equilibrium. This dynamic can be framed according to the instrumental view of performance suggested by the DPM framework, described in the second chapter of this book. The resulting model structure is characterized by the interplay of end results, performance drivers, and strategic resources. According to Wang et al. (2008) and Haghshenas et al. (2015), the main determinants of urban transportation systems’ performance are travel demand and transport supply, resulting in a specific equilibrium and modal share. These dimensions, which include the main strategic resources of transportation systems (e.g., people, vehicles, and other structural characteristics), are linked through mutual direct relationships, and therefore influence each other. In fact, on the one hand, an increase in demand determines an effort toward the adaptation of transport supply. This can take different forms based on the local area’s specific objectives and goals, such as new infrastructure (road building, new metro lines, new cycle lanes, etc.) or services (new bus routes, web facilities, the frequency of public services, etc.). On the other hand, transport supply provides a set of transportation modes and infrastructure that compels users to react by choosing between them based on their preferences. This may also create a new demand for a transportation service (e.g., when a transportation mode is made accessible and effective, some users may be incentivized by changing their behavior, such as going to do shopping in the city center rather than in their local neighborhood). As discussed in the third chapter of this book, identifying and mapping the system’s strategic resources is key to identifying the key stakeholders that contribute to urban transportation governance and thus need to collaborate so as to achieve the desired intermediate and end results. The interplay between transport demand and supply and the resulting modal share determines the overall system quality. This can be measured through a set of performance drivers framed according to sustainable development studies, namely economic, social, and environmental (Haghshenas et al. 2015; Litman 2016). These performance drivers are the intermediate results explaining and monitoring how the end results are achieved by the systems. According to a subjective view of performance, the desired end results should be derived by the objectives defined consistently with the vision developed in the first place. The end results that a transportation system aims to achieve should be related to broad community outcomes (Della Porta and Gitto 2013), such as improved quality of living, a reduction in transportation costs, and so forth. These outcomes contribute to an urban area’s attractiveness, producing effects in terms of economic

4.2  Travel Demand

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Fig. 4.1  A general model for urban transportation planning and management, adapted from Noto (2017)

development and in- and out-migration (Wang et al. 2008). In the latter instance, the end results influence both travel demand and transport supply: for instance, a bigger population with a growing income creates increases travel demand, with such financial resources providing the potential to enhance transport supply. Figure 4.1 demonstrates the general urban transportation system model structure described. Figure 4.1 shows how the interplay between the main performance determinants—namely travel demand, transport supply, and travel mode choice—results in the achievement of a certain level of system quality, which can be measured through a set of performance drivers framed according to the multiple domains and dimensions of sustainable development (Black et al. 2002; Litman 2016). System quality has a direct effect on the economy and the population of the urban area. Both feed back to the system’s strategic resources in terms of demand and/or pressure on supply adequacy. The following sections explore the above-mentioned modules that characterize the general structure of the model depicted in Fig. 4.1 in detail.

4.2  Travel Demand Travel demand is the main cause of trip generation. One of the main activities that transportation planners need to consider is trip demand forecasting. Certainly, one of the goals of transportation planning is to meet actual and future transportation demand with an adequate supply in terms of vehicles and infrastructure.

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Understanding transportation demand and its behavior over time requires analysts to take into account the main socio-economic characteristics of the urban population. The adoption of SD modeling allows them to focus on the physical elements of the urban transportation system. In particular, the starting point of the analysis should consider the main driver of trip generation, i.e., people. The rationale behind modeling travel demand through SD modeling is that this method enables us to frame population in homogenous groups in terms of needs. Travel needs depend on a number of factors, including age, sex, income, employment conditions, household characteristics, and so on. Thus, in order to understand the main dynamics influencing population trends, it is necessary to analyze population characteristics. A simplistic model would look at the historical trend of population growth in an area. Assuming that this remains constant over time, trip generation will be given by the number of users multiplied by the average number of trips per time, and adjusted by the other parameters influencing it (e.g., income). However, population growth is strictly related to age distribution. A first segmentation could then be made on the basis of the population’s age. This would allow us to build an aging chain that also considers fertility aspects. Figure 4.2 shows a simple population aging chain. As one may note in the above figure, the population was divided into age categories. Each stock (measured in terms of number of people) fosters the subsequent one in a defined time period (e.g., time 1). The flows connecting stocks are the result of the ratio between the stock from which they originate (e.g., teenagers) and the correspondent time to mature (e.g., 6 years). In order to determine travel demand, each stock should be multiplied by the average number of trips per time of the corresponding category. In Fig.  4.2, this structure has been made explicit for the “Teenager” stock. Framing the distribution of the population in age classes is key to understanding its future dynamics. In fact, as shown in Box 4.1, different population distributions (e.g., more younger people than adults) may lead to different growth rates.

Fig. 4.2  An aging chain model to frame population in age classes

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The representation displayed in Fig. 4.2 shows a basic aging chain. Based on the scope of the analysis and the detail level required, this structure can be explored further. For example, each stock could be analyzed considering migration in- and out-flows such as the death rate. These two structural characteristics significantly affect the results of the analysis.

Box 4.1 Same Population, Different Behaviors In this example, the aging chain portrayed in Fig. 4.3 was simulated in two alternative cases. In the first case, given an overall population of 70 people, the population distribution is hypothesized as follows: 20 children, 20 teenagers, 10 fertile adults, 10 adults, and 10 elderly. In the second case, the same overall population is more skewed toward the tail of the aging chain: 10 children, 10 teenagers, 10 fertile adults, 20 non-­ fertile adults, and 20 elderly. In both cases the average fertility rate is assumed to be two, meaning that each fertile woman on average gives birth to two children, and that half of the fertile adults are women. The results of the simulations are shown in the following figure. As one can see, although in both simulations the initial population is the same (70 people), the different population distributions lead to contrasting results. According to the first hypothesis (younger population), in 20 years the population will grow to up to 800 people, while according to the second hypothesis (older population), during the same time frame the population will only grow to up to 600 people, resulting in an overall difference of 200 people (i.e., 33%).

Fig. 4.3  Simulation of the aging chain model with two different distributions in age classes

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Fig. 4.4  Details of the aging chain model: migration flows

Figure 4.4 shows the flows that usually determine the stock of population dynamics. For the purpose of transportation planning, it is important that the split of population into multiple stocks follows both the fertility criteria and the travel need criteria. In other words, each stock should gather groups of people with similar needs, from which it is possible to derive the expected number of trips per period of time. For example, if the needs of one group age are too heterogeneous, this should be split into different stocks. For instance, fertile adults may have different needs depending on whether they are male or female, and unemployed or working. Figure 4.5 provides an example of how age groups can be split into a more segmented structure so as to consider each specific need. Other criteria that could support the segmentation of the population include socio-economic characteristics such as income level or being a member of a family. Another key characteristic that should be taken into account is related to the spatial dimension. The number of daily/weekly/monthly trips depends, in the first place, on the number of people living in the analyzed area or that have an interest in traveling there (e.g., job, relatives, school). Therefore, the urban population should be segmented into the main urban areas and routes. In fact, travel needs also depend on the attractiveness of the urban area in which transportation users are living. People living in the most attractive urban areas (in terms of facilities and activities) have different needs from people living in less attractive areas (e.g., peripheral areas). According to this perspective, the process of modeling the population should also consider territorial attractiveness in order to effectively understand their current and future needs. As such, key land-use characteristics should be included in the model (e.g., the presence of schools, malls, business centers, and hospitals.).

4.2  Travel Demand

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Fig. 4.5  Details of the aging chain model: sex and employment conditions

The general criteria when framing the population in order to predict its evolution and impact on trip generation is to divide it into different stocks as long as these sub-population segments own different characteristics in terms of travel needs. Theoretically speaking, this process could be reduced to the individual level. However, splitting the population into multiple stocks may overcomplicate the model and require additional effort in gathering new information related to the population’s characteristics. Thus, this process should be undertaken to the level to which the benefits in terms of understanding the system are greater than the cost of producing and managing new information. Modeling travel demand is pivotal in order to understand it and consequently manage it. Demand management is defined as “any action or set of actions intended to influence the intensity, timing, and spatial distribution of transportation demand for the purpose of reducing the impact of traffic or enhancing mobility options” (Meyer and Miller 2001, p.  11). Such actions are usually related to offering and incentivizing alternative transportation and service modes. Evaluation of these actions, and therefore the planning activity related to transportation demand management, can be supported through simulation. In particular, simulation may support on the one hand travel demand forecasting by discovering how population groups with homogenous needs change in the medium to long term, and on the other hand facilitates the experimentation of scenario analysis in order to understand the potential effect of incentives or the introduction of new services, i.e., intervening in transport supply.

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4.3  Transport Supply Transport supply is the combination of supply given by each transportation mode available and the intermodal supply. In general terms, this can be measured as the capacity of the available facilities to carry a given number of users passing a given point during a given time period (Meyer and Miller 2001). As such, overall transportation supply, which alongside population represents one of the main strategic resources of the urban transportation system, would be given by the sum of the maximum trip per time capacity of every transportation mode available. However, in contrast to the population and economy, the strategic resources that belong to the transportation supply category (e.g., number of buses) are directly related to the decisions of the system’s stakeholders. Therefore, it is essential that decision-making processes beyond the decision to act on these resources are shared among governance players. The DPM model may support this process by making explicit the urban transportation system’s dynamics and key performance drivers. Transportation supply should be leveled to respond to a given travel demand. When the transportation supply of a given mode is below its travel demand, a negative performance that would cause people to move from one mode to the others is stimulated. When this happens at the global level (i.e., the sum of the trip per time capacity of every transportation mode is insufficient to meeting overall travel demand), serious performance-related problems such as traffic congestion, the overuse of infrastructure, and so on result. The following figure displays the dynamic according to which a gap between the actual trip demand (e.g., a bus trip) and supply (e.g., bus capacity) of a certain mode determines (alongside other characteristics that will be explored further in the following section) its attractiveness compared to other transportation modes. A ­reduction in attractiveness implies a switch of users toward other transportation modes, determining a reduction in the mode’s demand (Fig. 4.6). The perceived gap between demand and supply—through the mediation of public and political pressure—also has the effect of instigating decision makers to increase the transportation supply of a specific mode so as to take its decreased attractiveness into account. This adjustment process represents the pathway toward equilibrium between demand and supply. In the following figure, the two feedback loops generated by the perceived gap in transportation demand and supply are represented (Fig. 4.7). The process of transportation supply adjustment described mirrors the typical approach of managing the transportation system by adding new facilities or ­improving service performance (i.e., new transit facilities, new highways, etc.). In a case where new capacity is continually added to accommodate demand (e.g., building a new highway), this can solve the congestion problem only in the short term. In fact, the new capacity will create a growing demand for the transportation mode (e.g., car), which in the long run again results in congestion (Sterman 2000; Meyer and Miller 2001). This is clearly explained by Sterman (2000) in his book Business dynamics, in which he discusses an example of traffic congestion with the help of SD modeling.

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Fig. 4.6  Dynamics related to demand and supply (1/2)

Fig. 4.7  Dynamics related to demand and supply (2/2)

The limitations of modeling transport supply and travel demand using SD modeling are related to its simulation characteristics. SD simulation is continuous and in contrast to discrete simulation unsuitable to simulating traffic congestion dynamics and other operational issues. As such, SD is more applicable at the strategic than the operational level (Shepherd 2014).

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Box 4.2 Demand and Supply, Adjustments Toward Equilibrium Let us suppose that an urban area has a travel demand of 2000 trips per month. Its transportation system is characterized by two modes with the same demand: buses and trains. The train mode has a carrying capacity of 1000 trips per year, while the bus system’s carrying capacity, following an unintended event, accounts only for 700 trips per year. This would stimulate people to adopt the alternative transportation mode, i.e., the train. However, supposing that the train has a carrying capacity of 1000 trips per year, a shift toward the train mode would cause it to exceed its carrying capacity. Based on the perceived gap between the demand and supply of the two modes, decision makers would then be called upon to adjust capacity until it matched the overall travel demand. The model behind the dynamics described presents a decision-making process that is shown in Fig. 4.8. In the simple model portrayed in Fig. 4.8, the two stocks in the center represent the transportation demand (expressed in terms of trips per period of time) of the two modes of transportation. The comparison between such demand and the actual mode capacity (i.e., the stocks of “Bus capacity” and “Train capacity”) determines the relative attractiveness of the modes, affecting the flows that influence travel mode choice, i.e., the switch in modes. The gap between demand and supply also affects the decision to invest in the mode capacity. As shown by the simulation in Fig. 4.9, a gap between demand and supply in a travel mode would cause an outflow of users to other transportation modes due to its decreased attractiveness (e.g., overcrowded buses), until it reacts by adapting its carrying capacity.

Fig. 4.8  A SFD model to analyze the relationship between demand and supply

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Fig. 4.9  Simulation of user-switching behaviors between bus and train

Fig. 4.10  Simulation of buses’ and trains’ perceived attractiveness

According to these dynamics, the overall quality of the transportation system, measured as the average modes’ attractiveness (in this case bus and train), would then be perceived as decreasing because once the bus users switch to the train, this mode also exceeds its capacity. The result is that both modes are overcrowded and potentially dysfunctional. This initial dynamic will be reverted over time through the ability of the system to adapt its carrying capacity to the travel demand. In the following graph, the train, bus, and the overall systems’ attractiveness behavior is displayed. The initial value of this variable (1) represents the average satisfaction of users with regard to transportation mode availability. The graph portrayed in Fig. 4.10 represents the dynamics characterized by the capacity adjustment loop (B2 in Fig. 4.7). After the initial demand shock, the system reacts by providing additional capacity to both transportation modes until their target attractiveness is reached. Figure 4.11 shows that after the initial difference between the demand and supply of bus services, the system reacts by adjusting the carrying capacity of both transportation modes so as to deal with the reduced overall quality.

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Fig. 4.11  Simulation of bus and train capacity

Fig. 4.12  Simulation of system capacity with no possibility of reducing modes’ capacity when built

In this simple example, the train capacity increases once the bus travel demand switches toward the train mode. It then decreases when the bus capacity gap is filled (e.g., starting from the fourth month of the simulation displayed in Fig.  4.11). However, in real life, once the carrying capacity of a mode has been increased, it is not always possible to decrease it again and, as a result, the overall system capacity overshoots its demand. This may bring additional costs and inefficiencies for unnecessary services. Figure 4.12 displays overall carrying capacity behavior when it is not possible to decrease the capacity already available. This is compared to the overall system’s travel demand. As one can see, in this case, the transport supply overshot the travel demand of about 30 trips per month. This exemplifies the importance of considering system responses to the policy action designed and implemented pertaining to transportation supply. This case is provided to show the complexity of planning transport supply adaptation and the need to develop a decision-making process that prevents such potential distortions.

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4.4  Travel Mode Choice The interplay between travel demand and transport supply results in an equilibrium that is also determined by users’ behavior in terms of travel mode choice. The previous section demonstrated how transportation supply in terms of different modes’ carrying capacities is one of the key determinants of overall modal share. However, according to the literature, travel mode choice is also influenced by several macroscopic (e.g., urbanization, technology, incentives) and microscopic (e.g., income, travel time, and cost) factors (Golob 1990; Kitamura 1990). According to Golob (1990), the main features that influence users’ travel mode choice are travel time and cost. However, in some cases, other important factors (such as household conditions) may also have a strong influence. Thus, urban transportation planners’ task is to analyze users’ travel needs in order to formulate a utility function with reference to their travel mode choice (Oppenheim 1995).

U = f ( travel time, cost of travels, …)



(4.1)

This utility function should be formulated for each group of homogeneous users in terms of needs, as determined in Sect. 4.2. The result of this analysis is related to how different groups of people switch from one travel mode to another based on the policies (both in terms of services and infrastructure) that policy makers are willing to implement. In a simulation model, such an understanding enables the modeler to gather the expected impact of mobility solutions with reference to travel mode adoption. When modeling urban transportation policies, policy makers should also take into account the feedback that travel mode adoption has on the same users’ decisions. In fact, when the most attractive travel mode is adopted by a number of people such that it exceeds its maximum carrying capacity, its attractiveness will start to decline due to inefficiencies, lack of comfort and safety. This renders other travel modes more attractive, and users will switch again to these until an equilibrium is reached (see Sect. 4.3). Thus, when encouraging a specific transportation mode, it is essential to plan its carrying capacity so as to sustain the increase in the number of people that will adopt it, and to consider the overall supply of the urban transportation system given by the actual and future capacities of other modes. Travel mode adoption determines overall transportation system performance. If the transportation supply (roads, mass transportation, cycle lanes, pedestrian walkways) fails to accommodate travel needs (i.e., if the travel mode’s adoption exceeds its carrying capacity, generating traffic congestion and inefficiencies), then the urban transportation system’s performance will negatively affect the overall urban quality of life.

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4.5  Urban Transportation System Quality and Results The travel mode choice influences overall system performance, consisting of a multi-dimensional construct depending on the features of the different transportation modes (e.g., travel time, costs, emissions, line extensiveness) weighted with their rate of adoption (i.e., how many trips are run with each mode). Therefore, when evaluating how policy makers’ actions impact on the systems’ performance, we have to consider on the one hand the short-term effects they will produce (e.g., a reduction in travel time, wider extensiveness, variation in travel cost), and on the other hand how these effects influence citizens’ behavior in terms of travel mode adoption in the long run (i.e., a switch from one mode to another). The interplay between the urban transportation systems’ strategic resources belonging to the different modules (i.e., transport supply, travel demand, and travel mode choice) has an impact on all of the performance drivers. Due to the strong relationship between the transportation system and the sustainable development of cities, performance drivers can be developed according to the three domains of sustainability (Noto 2017): economic, social, and environmental. The economic domain is affected by two key aspects: travel time and cost of travel. The growing use of inefficient or slow modes negatively impacts overall transportation system quality. On the other hand, if more people choose the more efficient and effective travel mode, it becomes less attractive (e.g., more people choosing to travel by car generates traffic congestion, which increases travel time and cost). The social domain comprises factors such as equity, accessibility, safety, and security. A good transportation mode has good accessibility in terms of geographical distribution (network extensiveness, e.g., car network extensiveness includes all viable roads, whereas train network extensiveness only includes train stations), has an equitable fare, and guarantees its passengers safe and secure travel. At the environmental level, each travel mode has different characteristics in terms of emissions and energy consumption. Given that the carrying capacities of transportation modes differ, the measurement of emissions and consumption should take into account the number of passengers per vehicle (e.g., CO2 emissions per passenger), the emissions per vehicle, and the energy consumption. In Chap. 2, a comprehensive set of performance measures that could further support the monitoring of performance assessment in strategic management processes was provided. However, for DPM modeling purposes it is important that the key set of performance drivers is chosen among those measures that explain how strategic resources are causally linked to the system’s end results. In the urban transportation system, end results are usually identified in the population outcomes (Della Porta and Gitto 2013). Quality of life might be considered as one of the most representative end results that can also be achieved through the performance of urban transportation. Transportation contributes to improvements in quality of life through the following two factors (Meyer and Miller 2001, p. 95):

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• “Mobility: the ability and knowledge to travel from one location to another in a reasonable amount of time and for acceptable costs. • Accessibility: the means by which an individual can accomplish some economic or social activity through access to that activity.” These “traditional” transportation system goals should be complemented, in a sustainable development perspective, by the careful management of non-renewable resources and environment protection. In fact, according to the European Commission (2013, p.  7), the purpose of urban transportation plans should be related to “accessibility and quality of life, as well as sustainability, economic viability, social equity, health and environmental quality.” In particular, urban transportation planning objectives should be related to the following (European Commission 2013): • Ensure that all citizens are offered transport options that enable access to key destinations and services; • Improve safety and security; • Reduce air and noise pollution, greenhouse gas emissions, and energy consumption; • Improve the efficiency and cost effectiveness of the transportation of persons and goods; • Contribute to enhancing the attractiveness and quality of the urban environment and urban design for the benefit of citizens, the economy and society as a whole. All of these objectives should be declined into end results and intermediate results (i.e., performance drivers) according to the DPM approach. Another characteristic that should be taken into account when adopting a DPM approach is assessment of the effect that the end results achieved have on the system’s strategic resources, such as how the achievement of a better quality of life affects the migration of people and firms, and how these in turn affect tax collection. In fact, according to the instrumental view of performance, the end results achieved by a system provide new input to the system’s strategic resources (Bianchi 2010, 2016). For example, greater mobility through mass modes of transportation and the related increase in number of tickets sold (an end result) augments the stock of financial resources (strategic resource) available to the system. In the following section, an example of an urban transportation model developed according to the DPM approach is discussed. This example seeks to build greater awareness as to the potential benefits of applying DPM to urban transportation.

4.6  Exploring the Buenos Aires Case Study The case study developed here focuses on the urban area of Buenos Aires. The capital city of Argentina is a “megacity” in which the population exceeds territorial boundaries, and considering the neighboring municipalities, now has approximately

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Fig. 4.13  Buenos Aires and the RMN municipalities

15 million inhabitants. The case study focuses on the transportation system between the city center and some of these neighboring municipalities, specifically the so-­ called Region Metropolitana Norte (RMN), a complex of four municipalities, namely Vicente Lopez, San Isidro, San Fernando, and Tigre (Fig. 4.13). This region is one of the most productive in the whole Province of Buenos Aires. Even though the four municipalities represent a small portion of the total province area, they contribute about 10% to the total province’s gross domestic production (GDP) (Direccion Provincial de Estadística1). The case study was developed through both deep document analysis and interviews with experts (the former Director of

 http://www.estadistica.ec.gba.gov.ar/dpe/Estadistica/pobvivob.html.

1

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Infrastructure of the City of Buenos Aires, the former Director of the Ministry of Public Works of Argentina, and an independent researcher). The data presented are derived from the key documents analyzed,2 official Argentinian statistics, and other data. Due to its proximity to the capital city of Argentina, many of the people residing in these municipalities conduct their business and activities there. Therefore, every morning and evening, they go back and forth from home to the capital city (CABA). This behavior creates traffic congestion on the main roads connecting the two areas. This and the associated effect on local attractiveness and urban transportation system performance were identified as the main problem of the RMN’s transportation system. This supposition is related to the inadequacy of the existing transport supply with respect to current demand. In order to tackle this problem and to implement appropriate solutions through adjusting transport supply and engendering improvements in quality of life, it is necessary to consider the set of causal relationships that affect the urban system and their related dynamics (Sterman 2000). The risk is of incurring counterintuitive behaviors that in the long run may result in poorer performance. Increasing the performance of peripheral area transportation systems not only means improving people’s ability to move back and forth from the city center more easily (output), but also more importantly to impact local attractiveness in both the short and long term through leveraging on travel demand and travel mode choice. These are influenced by changes in transport supply (e.g., new infrastructure) on the basis of several factors (Kitamura 1990), both at the macroscopic level—continuing urbanization, evolving consumer technology and products, telecommunications systems, highway and transit improvements, and energy and air quality policies— and microscopic level, such as household conditions (e.g., income, composition), travel time, and cost (Golob 1990, Wegener 2011). Traffic congestion is determined by the number of vehicles traveling on the same road at the same time (Wang et al. 2008). Its impact on mobility is twofold (European Commission 2013): first, it has a direct economic impact on citizens in terms of delays and costs associated with the increased depreciation of vehicles and additional fuel and oil consumption; second, it negatively impacts the environment (air and noise pollution, and greenhouse gas emissions). In order to run the analysis, it was assumed that the main features influencing people’s travel mode choice in RMN are travel time and costs. Therefore, in order to reduce the number of vehicles on the road at a given moment in time, thereby reducing traffic congestion, a solution might be to make collective modes of transportation more attractive in terms of cost and time. 2   These documents include: “Enmodo: Encuesta de movilidad domiciliaria, 2009-2010”; “Intrupuba: Investigaciòn de transporte urbano de Buenos Aires”; “Observatorio de Movilidad Urbana  - Información disponible en línea, 2007”; “Comisión Nacional de Regulación del Transporte (CNRT). Red ferroviaria argentina, Informe estadístico 2010-2011”; “Academia Nacional de Ingeniería. Accesos a la Región Metropolitana de Buenos Aires. El transporte ferroviario y los subterraneos, 2011”.

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Fig. 4.14  The DPM model of RMN mobility

As previously mentioned, travel mode choice influences the overall system’s performance, consisting of a multi-dimensional construct contingent on the features of the different transportation modes (e.g., travel time, cost, emissions, line extensiveness) weighted with their rate of adoption (i.e., how many trips are run through each mode). Thus, when evaluating how the decision taken affects the different systems’ performance, we have to consider on the one hand the short-term effects they will produce (e.g., reduction in travel time, wider extensiveness, variations in travel cost), and on the other hand how these effects influence citizens’ behavior in terms of travel mode adoption in the long run (i.e., switch from one mode to another). The model presented here is an aggregate and easy-to-read model (Fig. 4.14). The model structure is formed by two connected sub-models with a similar structure: one (RMN-CABA) focuses on mobility between the northern region and the city center, whereas the other (RMN) represents mobility within the northern region. This second sub-model was developed in order to understand what might imply a change in the RMN’s attractiveness in terms of transportation demand. In both sub-models, the relationship between the two main strategic resources— i.e., transport supply (the different travel modes available) and the travel demand— determined the overall system’s quality, measured through a mix of economic, social, and environmental aspects (performance drivers). According to the general model previously displayed, the transportation system’s quality influences the RMN’s attractiveness (end result), which in the long run has an impact on both the population (attracting new residents) and the economy (supporting firms’ development). The money coming from taxes paid by both people and firms may then be invested again into the strategic resources of the transportation system—i.e., the transport supply—to improve its quality. These investments may be in different combinations of available policy options (e.g., railroads, bus lines, tolls, cycle lines, monorails, underpasses) that have varying impacts on overall performance, seen as a construct of the different dimensions previously identified (economic, social, environmental).

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4.6  Exploring the Buenos Aires Case Study

Each performance dimension was measured thanks to a set of performance drivers built according to the DPM approach, and declining them from the end results identified by the literature and the people interviewed. The performance drivers that determine the urban area’s attractiveness related to the transportation system are as follows: • Economic: Cost of travel (average cost of travel/historical value); travel time (average travel time/historical value); • Social: Extensiveness (number of stops/historical value); safety (number of accidents/historical value); • Environmental: CO2 emissions (CO2 emissions/CO2 emissions if no investments occur). As previously stated, these indicators do not just depend on the policies’ inputs, but in the long run they are also influenced by users’ behavior. In Fig. 4.15, a partial version of the causal model structure was sketched so as to display the dynamics and the feedback loops characterizing the RMN transportation system. It is partial because it displays the system’s relationships associated with an investment in a specific mode in one area (e.g., RMN-CABA viability). The full model comprises all the potential investment modes in interaction in both subsystems. As one can see, the partial model structure is formed by four reinforcing loops (feedback loops leading to exponential behavior) and two balancing loops (feedback loops leading to exponential decay behavior). The reinforcing loops describe Investment in mode 1

Strategic Resources

Taxes collected Economy & Population

R4

R3 R2 Cost of travel

Travel time

Capacity utilization B1

Extensivess & security R1

Attractiveness of mode 1

Trips with mode 1

B2

Social indicators

Performance Drivers

End Results

Economic indicators

Environmental indicators

System Quality

RMN attractiveness

Fig. 4.15  Detail regarding the DPM model of RMN mobility

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the dynamics by which an investment in a certain mode improves its performance (economic, social, and environmental characteristics) and therefore transportation system quality, because it affects a share of the total trips of the area. Improved system quality influences the RMN’s attractiveness, with a direct, indirect, and induced effect on the economy and population. A growth in population and the economy can be translated into a greater amount of taxes collected by the public administration, which may be used to pay back previous investments or undertake new ones. The first balancing loop (B1) is related to the carrying capacity of the modes. Once a mode becomes more attractive due to a reduced travel time or cost, users will adopt it until its maximum capacity is reached. Then that mode’s performance will start to decrease due to its excessive adoption, and users will switch to other transportation modes until an equilibrium is reached. The second balancing loop (B2) is related to the dynamics that a growth in population and the economy would generate an increase in travel demand with negative consequences on the environment and, therefore, on system quality and RMN attractiveness. The causal link connecting “trips with mode 1” and the environmental indicator does not have a defined polarity because this depends on the environmental performance of the other transportation mode available (non-linear function). These two balancing loops represent the main “limits to growth” of the transportation system (Meadows et al. 2004). In this model, the contribution of each performance dimension to overall system quality was assumed to be equal for each group of indicators (economic, social, and environmental). In order to estimate the effect of service quality on the RMN’s attractiveness, system quality and the RMN’s attractiveness were measured on the same scale with a time delay of 6 months. The effect of territory attractiveness on population and economic growth was estimated thanks to a study conducted by Weisbrod et al. (2009). Tax system equations were taken from Argentinian fiscal legislation. The potential investments were selected through analyzing the set of policy options discussed by the main stakeholders when the study was run (Table 4.1).3 The solutions suggested were framed into three categories. Two options (i.e., investments in the existing rail infrastructure and tolling) were aimed at addressing viability improvement between the two areas; two other options (investment in cycle line and a new monorail infrastructure) were aimed at addressing viability improvement in the northern region; and two other options (investment in buses and in underpass) were designed so as to impact on both subsystems. In Table 4.1, the investment options considered in this study are summarized.

3  The alternative investment options’ costs were estimated in the project: P.  Bereciartua and D. Vereertbrugglen, C. Logascio, L. De Caro (2012), Proyecto “RMN + Territorio Inteligente” Bereco SA winner of the “Concurso de Ideas  – Proyecto “Soluciones para el transporte en el Corredor Norte de la Región Metropolitana Buenos Aires” organize by the four municipalities of the RMN (Vicente Lopez, San Isidro, San Fernando and Tigre) and the Fundación Metropolitana.

Investments directed at improving mobility within the RMN

Investments directed at improving viability within the two areas and within the RMN

Investment options Investments directed at improving viability within the two areas

6,000,000.00

600,000,000.00

Monorail (30 km)

Cycling lane

100,000,000.00

5,000,000.00

100,000,000.00

Cost (in US dollars) 146,000,000.00

Bus system improvement

Toll on the motorway for cars

Underpasses in the railway path

Investment options Railway improvement

Table 4.1  The alternative policy option for mobility in the RMN

2

5

1

1

2

Implementation time (in years) 2

Consequences This investment will improve capacity, extensiveness and quality of service. This will generate a shift in travel from the private mode to the railway mode Investing in underpasses will make the railway system more efficient and safe, bringing benefits to the social sphere This small investment will allow a future cash flow and will have the effect of discouraging the use of cars. However, citizens’ quality perception in the short term will decrease This investment is an alternative to the railway one. It has the benefit of increasing the service’s extensiveness; however, it has a smaller environmental benefit This investment will have the effect of reducing travel by private modes inside the RMN. It will also be a driver of shifting ‘centrality’ to this metropolitan area This small investment will incentive the use of cycling inside the RMN, which will result in less emissions of CO2 in the area

4.6  Exploring the Buenos Aires Case Study 113

114 4  Modelling Urban Transportation System Through Dynamic Performance Management

Each travel mode investment has the effect of impacting different service quality features. When these effects pertain to time and cost of travel, they will also impact travel mode choice. The latter depends on travel mode attractiveness, which is a strategic resource that changes based on the flow “change in attractiveness.” Such a flow, comparing travel time and the cost of the different modes available, is determined as the function displayed in Eq. (4.2). In order to determine the overall travel mode choice (namely how many trips per month per mode), Eq. (4.2) was used to compare each mode with the others available.



Attractiveness of mode ( i ) compared to mode ( ii )   Travel time i 1 Travel cos t i 1   ∗ + ∗  − Previous conditions    Travell time ii 2 Travel time ii 2   (4.2) = Time to change perception

The graphs in Figs. 4.16 and 4.17 compare how travel mode adoption develops in the two subsystems considered when no policies are adopted (left column), when all

Fig. 4.16  Simulation of trip generation per mode in the next 20  years (240  months) with and without investments—CABA

4.6  Exploring the Buenos Aires Case Study

115

Fig. 4.17  Simulation of trip generation per mode in the next 20  years (240  months) with and without investments—RMN

possible investments are made (center column), and when a single investment is made in underpasses (right column). The travel modes analyzed are the most relevant to the two subsystems. Concerning the CABA-RMN subsystem, these are private mode (car), bus, and train. Concerning the RMN subsystem, these are private mode (car), bus, no vehicles (here called “green”) and a non-existent infrastructure, the monorail. The time scale of this simulation is 20 years, and the investments are made once at the beginning of the period. The y-axes of the above graphs measure the number of trips per month per transportation mode. The graphs on the left side show that when no investments are made, the number of trips per month grows linearly (assuming a population growing linearly), and with the same rate for each travel mode. In contrast, when all potential investments are made, trips grow at different rates and shapes for each travel mode. In particular, we notice that even though service quality improvements result in a number of trips growing faster than in the previous case (due to the influence of improved urban area attractiveness), the overall impact of these measures is addressed at discouraging the use of private modes of transportation and, as a result, the RMN’s attractiveness improves. Emerging from this case is that improvements in overall transportation system quality (and consequently the RMN’s attractiveness) create a new transportation

116 4  Modelling Urban Transportation System Through Dynamic Performance Management

Fig. 4.18  Simulation of economic performance indicators

Fig. 4.19  Simulation of social performance indicators

demand that policy makers should consider. Such results may foster agreements and collaboration between stakeholders in determining the transport supply’s investment priorities. In order to evaluate how these actions might affect the performance of transportation systems, the graphs in Figs. 4.18, 4.19, and 4.20 show the simulation results of the indicators related to the three selected sustainability dimensions. Even though the simulations come from a model based on strong assumptions (especially with regard to the calibration of performance drivers’ effects on the RMN’s attractiveness), the result is that a change in the system structure in terms of new mobility solutions drives a user behavior that determines the system’s performance in the long term. The performance drivers designed through the DPM approach allowed us to understand and explore the impact of policy makers’ actions with regard to the overall performance of the peripheral area analyzed.

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Fig. 4.20  Simulation of environmental performance indicators

Fig. 4.21  Simulation of financial resource dynamics

This is more evident in the Figs. 4.18, 4.19, and 4.20, in which the impact of a single policy option is measured according to the performance drivers defined. In particular, it can be seen that investment in the underpass does not improve economic performance, but it does have a strong impact on both the social dimension (security of the network) and the environmental dimension, as it makes the train more attractive (train emissions per person are significantly lower than those produced by other modes). Such a modeling approach could also be used to support financial planning. In fact, we may evaluate in how much time an investment can be refunded through the collected tax. In Fig.  4.21, two investment examples are displayed. In the right graph, investment in buses is shown; in the left graph, investment in a monorail is presented. Line 2 represents the financial equilibrium, while line 1 represents the stock of funds initially deployed by the investments made and then fostered by the taxes collected. The graphs show that in about 100 months (8 years), public administrations may pay back their investments in buses, whereas in the case of a monorail, they would need more than 20 years to pay back its construction. The rate at which financial

118 4  Modelling Urban Transportation System Through Dynamic Performance Management

resources are recovered depends on the territory’s attractiveness, which in this model drives both population growth and the incomes of local enterprises. Larger populations and incomes determine a greater amount of taxes collected. The limitation of this analysis is related to the fact that not all of the taxes collected are invested in the transportation sector, even though in Argentina around 70% of public expenditure is devoted to transportation (Barbero et al. 2011). The implications of such an analysis are of particular interest regarding the public sector’s borrowing. Knowing how many years are required to pay back an investment could help decision makers in planning their finances. In the monorail case, for instance, when making a loan to build the new infrastructure, it is important that this loan expires in no fewer than 30 years, otherwise there will be insolvency.

4.7  DPM Model Use and Validation Given that this chapter is focused on the technical features and solutions for building a DPM model to support urban transportation strategic planning, it is important to provide some key insights related to the validation process of these models. DPM models follow the validation criteria and processes that apply to SD. According to Sterman (2002), “all models are wrong.” This statement is intended to express how our knowledge of a complex system is limited, and therefore one needs to simplify reality in order to obtain the main characteristics that describe its complexity through the use of hypotheses and simplifications. Modelers should thus be able to define the level of detail of the modeling activity according to its purposes. As mentioned previously, DPM models may help policy makers at the strategic level. Indeed, it is important to discuss the support that quantitative models may provide policy makers. Even though it is widely acknowledged that wicked problems require evidence-based policy making (Kates et al. 2001), according to some authors, even though research focuses on the development of increasingly accurate models to assess performance and predict systems’ behavior, policy makers are resistant to use them and instead make decisions based on alternative schemes (Nilsson et al. 2008; Saujot et al. 2016). The causes of the non-use or “distorted” use (e.g., instrumental use aimed at supporting an already taken decision or a formal use without any real impact on the decision-making process) may be found in several aspects that range from the type of bureaucracy, policy maker preferences, and political consensus (Radaelli 2005), to the usability and intelligibility of the tool (Saujot et al. 2016). This final characteristic may be tackled through the design of DPM models aimed at describing the main urban transportation dynamics, while avoiding an excessive degree of detail that may preclude policy makers’ and stakeholders’ ­comprehension of the system under analysis. Many of the transportation models that have been developed in recent decades are scientifically sound and able to effectively represent the real dynamics characterizing urban transportation, yet are considered by their final users (e.g., planners, policy makers, other stakeholders) as

4.7  DPM Model Use and Validation

119

“black boxes” through which it is difficult to understand how the simulation of certain policies produce particular results (Pfaffenbichler et  al. 2010; Saujot et  al. 2016). The opportunity of using the DPM model to analyze urban transportation is thus related to the possibility of more effectively engaging with model users and supporting policy makers and stakeholders in understanding problems. Given that there are no general models that can fit every urban system, the modeling solutions that could be adopted by modelers (e.g., which categories to consider in the aging chain, which modes to include in the analysis, etc.) are dependent on the structural characteristics, emerging problems, challenges, and opportunities that characterize the transportation system analyzed. Based on the above premises, the validity of the proposed modeling solutions and the overall model has to be found in its utility (Sterman 2000, 2002). An SD model can be considered useful and effective when (Sterman 2000): • It is used to solve problems; • It has broad boundaries to capture the feedbacks, time delays, and interactions unaccounted for in people’s mental models; • It draws upon the widest array of data, both quantitative and qualitative; • Other tools and methods are integrated into the effort; • Stakeholders are actively engaged as partners in the modeling process from the start; • The model is an open box used to catalyze learning rather than a black box used to advocate policy positions. This key point should drive planners and modelers when addressing an urban strategic planning transportation process. The main risk beyond the adoption of a DPM approach is related to the relative ease of SD modeling, which may compel modelers to under-evaluate the technical validation process of models (Sterman 2000). Regarding this concern, modelers and planners should adopt a rigorous but relativist/holistic scientific approach.4 DPM models, such as every SD model, are causal-descriptive, i.e., they are aimed at explaining how real systems actually operate in certain aspects. In contrast to “black-box” models, validation cannot refer exclusively to the generation of an accurate output behavior; rather, it implies investigation of the internal structure of the model (Barlas 1996). At the technical level, DPM model validation should take place in every stage of modeling. Barlas (1996) distinguishes between structure validity and behavioral validity. In Table 4.2, the related set of tests is displayed (Forrester and Senge 1980; Balci 1994; Barlas 1996). Structure validity can be assessed through direct structure tests and structure-­ oriented behavior tests. The former does not involve simulation, but a comparison 4  According to a relativist/holistic approach, “[n]o particular representation is superior to others in any absolute sense, although one could prove to be more effective. No model can claim absolute objectivity, for every model carries in it the modeler’s worldview. Models are not true or false, but lie on a continuum of usefulness” (Barlas and Carpenter 1990, p. 187).

120 4  Modelling Urban Transportation System Through Dynamic Performance Management Table 4.2  Validation tests Structure validity

Direct structure test

Structure-oriented behavior tests

Behavior validity

Structure confirmation test Parameter confirmation test Direct extreme condition test Dimensional consistency test Formal inspection/reviews Walkthroughs Semantic analysis Extreme-condition test Behavior sensitivity test Modified-behavior prediction Boundary adequacy test Phase relationship test Qualitative features analysis Turing test

Behavior pattern test

of each individual relationship with knowledge about the real system or generalized knowledge concerning the system exists in the literature. Direct structure tests, although powerful in theory, have the disadvantage of being too qualitative and informal by their nature (Barlas 1996). Structure-oriented behavior tests are much more suitable to formalizing and quantifying. They aim to assess the validity of the structure indirectly by applying certain behavior tests on model-generated behavior patterns. For instance, extreme-­ condition (indirect) tests involve assigning extreme values to selected parameters and comparing the simulated behavior to the observed behavior of the real system under the same “extreme condition” (e.g., if the population is equal to 0 people, we expect that the transportation demand will be 0 trips per month). Lastly, behavior pattern tests are designed to “measure how accurately the model major behaviour patterns exhibited by the real system. It is crucial to note that the emphasis is on pattern prediction (periods, frequencies, trends, phase lags, amplitudes, …), rather than point (event) prediction” (Barlas 1996, p. 193). Among such tests, one may note the multiple-test procedure by Barlas (1989), an overall summary statistic proposed by Sterman (1984), and several tests discussed in Forrester and Senge (1980). Further insights on model validation processes may be found in Forrester and Senge (1980) and Barlas (1996).

4.8  Conclusions This chapter has sought to discuss the technical features that DPM modeling must address in order to support urban strategic transportation planning.

References

121

The general framework presented allows planners and decision makers to model urban transportation system according to the instrumental view of performance and the feedback relationships that link the key modules to one another. Each module was analyzed in depth, and various examples were provided to explain the criteria that should be followed when adapting the general framework to the urban system analyzed. However, no prescriptions have been given by the author in terms of modeling due to the heterogeneity that characterizes every transportation system and that renders every context unique. A final section has discussed the use and validation process that should be performed in order to develop a useful model and to legitimate it in the eyes of policy makers, stakeholders, and the broader community.

References Balci, O. (1994). Validation, verification and testing techniques throughout the life cycle of a simulation study. Annals of Operations Research, 53(1), 121–173. Barbero, J., Castro, L., Abad, J., & Szenkman, P. (2011). Un transporte para la equidad y el crecimiento. Aportes para una estrategia nacional de movilidad y logística para la Argentina del Bicentenario. CIPPEC, Working paper n.79. Barlas, Y. (1989). Multiple tests for validation of system dynamics type of simulation models. European Journal of Operational Research, 42(1), 59–87. Barlas, Y. (1996). Formal aspects of model validity and validation in system dynamics. System Dynamics Review, 12, 183–210. Barlas, Y., & Carpenter, S. (1990). Philosophical roots of model validation: Two paradigms. System Dynamics Review, 6(2), 148–166. Bianchi, C. (2010). Improving performance and fostering accountability in the public sector through system dynamics modelling: From an ‘external’ to an ‘internal’ perspective. Systems Research and Behavioral Science, 27(4), 361–384. Bianchi, C. (2016). Dynamic performance management. Berlin: Springer. Black, J. A., Paez, A., & Suthanaya, P. A. (2002). Sustainable urban transportation: performance indicators and some analytical approaches. Journal of Urban Planning and Development, 128(4), 184–209. Della Porta, A., & Gitto, A. (2013). Reforming public transport management in Italy: The continuous search for spending better. In M.  Sargiacomo (Ed.), Public sector management in Italy (pp. 155–180). New York: McGraw-Hill. European Commission (2013). Guidelines. Developing and Implementing a Sustainable Urban Mobility Plan. Accessed at: http://capacitybuildingunhabitat.org/wp-content/uploads/ workshops/2019-sustainable-transportation-in-asian-cities-for-a-greener-globe-and-better-life/ Pre-course%20readings/A-1%20sump_guidelines_en.pdf Fedele, P., Brusati, L., & Ianniello, M. (2016). Organizational underpinnings of interactive decision making: An empirical inquiry. International Journal of Public Sector Management, 29(4), 310–326. Florian, M., Gaudry, M., & Lardinois, C. (1988). A two-dimensional framework for the understanding of transportation planning models. Transportation Research Part B: Methodological, 22(6), 411–419. Forrester, J. W., & Senge, P. M. (1980). Tests for building confidence in system dynamics models. In A. A. Legasto, J. W. Forrester, & J. M. Lyneis (Eds.), System dynamics. Amsterdam: North-Holland.

122 4  Modelling Urban Transportation System Through Dynamic Performance Management Golob, T. F. (1990). The dynamics of household travel time expenditures and car ownership decisions. Transportation Research Part A: General, 24(6), 443–463. Haghshenas, H., Vaziri, M., & Gholamialam, A. (2015). Evaluation of sustainable policy in urban transportation using system dynamics and world cities data: A case study in Isfahan. Cities, 45, 104–115. Kates, R.  W., Clark, W.  C., Corell, R., Hall, J.  M., Jaeger, C.  C., Lowe, I., McCarthy, J.  J., Schellnhuber, H. J., Bolin, B., Dickson, N. M., & Faucheux, S. (2001). Sustainability science. Science, 292(5517), 641–642. Kitamura, R. (1990). Panel analysis in transportation planning: An overview. Transportation Research Part A: General, 24(6), 401–415. Litman, T. (2016). Well measured: Developing indicators for sustainable and livable transport planning. Victoria, BC: Victoria Transport Policy Institute. Manheim, M. L. (1979). Fundamentals of transportation systems analysis. Cambridge, MA: MIT Press. McNally, M. G. (2000). The four step model. In D. Hensher & K. Button (Eds.), Handbook of transport modelling (pp. 35–53). Bradford: Emerald Group Publishing Limited. Meadows, D., Randers, J., & Meadows, D. (2004). Limits to growth: the thirty year update. Chelsea Green, White River Junction, VT. Meyer, M. D., & Miller, E. J. (2001). Urban transportation planning: A decision oriented approach. New York, NY: McGraw-Hill. Nilsson, M., Jordan, A., Turnpenny, J., Hertin, J., Nykvist, B., & Russel, D. (2008). The use and non-use of policy appraisal tools in public policy making: An analysis of three European countries and the European Union. Policy Sciences, 41(4), 335–355. Noto, G. (2017). Combining system dynamics and performance management to support sustainable urban transportation planning. Journal of Urban and Regional Analysis, 9(1), 51–71. Oppenheim, N. (1995). Urban travel demand modeling: From individual choices to general equilibrium. John Wiley and Sons. Pfaffenbichler, P., Emberger, G., & Shepherd, S. (2010). A system dynamics approach to land use transport interaction modelling: The strategic model MARS and its application. System Dynamics Review, 26(3), 262–282. Radaelli, C. M. (2005). Diffusion without convergence: How political context shapes the adoption of regulatory impact assessment. Journal of European Public Policy, 12(5), 924–943. Saujot, M., De Lapparent, M., Arnaud, E., & Prados, E. (2016). Making land use—Transport models operational tools for planning: From a top-down to an end-user approach. Transport Policy, 49, 20–29. Shepherd, S. P. (2014). A review of system dynamics models applied in transportation. Transportmetrica B: Transport Dynamics, 2(2), 83–105. Sterman, J. D. (1984). Appropriate summary statistics for evaluating the historical fit of system dynamics models. Dynamica, 10(2), 51–66. Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. London: McGraw-Hill. Sterman, J. D. (2002). All models are wrong: Reflections on becoming a systems scientist. System Dynamics Review, 18, 501–531. Wang, J., Lu, H., & Peng, H. (2008). System dynamics model of urban transportation system and its application. Journal of Transportation Systems Engineering and Information Technology, 8(3), 83–89. Wegener, M. (2011). From macro to micro—How much micro is too much? Transport Reviews, 31(2), 161–177. Weisbrod, G., Cutler, D., & Duncan, C. (2009). Economic impact of public transportation investment. American Public Transportation Association. Retrieved from www.apta.com

Index

A Aging chain model, 96–98 migration flows, 98 sex and employment, 99 Auckland Plan, 73 B Behavior pattern tests, 120 Bianchi’s approach, 51 Business dynamics (Book), 100 C Causal loop diagrams (CLD), 42–44 Comprehensive systemic approach, 47 “Culture of measurement”, 41 D Dangerous stakeholders, 67 Definitive stakeholders, 67 Demanding stakeholders, 68 Dependent stakeholders, 67 Design phase/strategy formulation, 23 Discretionary stakeholders, 68 Dominant stakeholders, 67 Dormant stakeholders, 68 Dynamic performance management (DPM) adoption, 71, 93, 119 approach, 30, 51–53 Bianchi’s approach, 51 conceptual and technical level, 93

design, 118 dynamic RBV models, 51 evidence-based policy, 118 inter-institutional perspective, 53 limitation, 78 performance, 54, 116 planners and policy makers, 76 policy makers, 118 SD model, 76, 119 SD theory, 52 social issues, 52 strategic management approach, 51 strategic planning process, 54 subjective and instrumental perspectives, 55 theoretical approach, 51 urban transportation, 107, 118 urban transportation dynamics, 93 urban transportation planning, 53, 55 validation, 119 Dynamic RBV approach, 52 E Economic performance indicators, 116 “Environmental assessment”, 22 Environmental performance indicators, 117 F Financial resource dynamics, 117 Financial resources, 81 First balancing loop (B1), 112

© Springer Nature Switzerland AG 2020 G. Noto, Strategic Planning for Urban Transportation, System Dynamics for Performance Management & Governance 3, https://doi.org/10.1007/978-3-030-36883-8

123

Index

124 G Governance, 14–17 Gross domestic production (GDP), 108 Group model building (GMB), 71 I Inter-institutional perspective, 62 Italian transportation sector, 79 L Land use interaction models (LUTI), 48 Learning-based approach, 14 M MARS model, 48 Model validation processes, 120 Modeling approach, 117 Modeling travel demand, 99 Municipal administration, 63 Municipality of Trento, 72 Municipality service contract policy, 86 N New Public Management (NPM) reform, 12, 13 O Open-minded relational approach, 14 P Palermo’s transportation system, 80, 82, 84, 88 Performance management (PM) advantages, 30 application, 29 definition, 29 designing, 32 DPM, 30 financial performance, 34 instrumental view, 31, 32 measurement and management systems, 31 NPM reform, 30, 33 objective view, 31 performance traps/paradoxes, 33, 34 performance-measuring practices, 30 SD (see System dynamics (SD)) subjective view, 31, 32 superficial/mechanistic approach, 33 traditional approaches, 33

traditional frameworks, 33 traditional systems, 30 Performance management approach, 15, 25 Performance traps/paradoxes, 33, 34 Performance, transportation systems categories and conceptual frameworks, 40 “culture of measurement”, 41 objectives, 36–37 performance indicators, 36–38, 41 performance measures, 35, 39–40 PM systems, 35 SD modeling, 42 and service, 38 social reporting purposes, 37 strategic planning and management, 35 sustainable transport goals, 36–37 urban transportation planning, 35 “Predict-and-provide” approach, 20 Power-interest grid, 68 Public value management communication and participation, 20 design phase, 23 diagnosis, 22, 23 dynamic and social complexity, 17 implementation, 23 key propositions, 14 literature identifies, 21 monitoring, 24 paradigmatic change, 20 performance indicators, 24 “predict-and-provide” approach, 20 urban strategic plans, 20, 21 vision, 21, 22 wicked problems, 18–20 Q Quasi-judicial processes, 71 Quasi-legislative processes, 70 R Region Metropolitana Norte (RMN) DPM model, 110, 111 mobility, 113 municipalities, 108 quality influences, 110 transportation system, 109 Resource dependence theory, 64 S Second balancing loop (B2), 112 Social performance indicators, 116

Index Soft system methodology (SSM), 65 Stakeholder analysis and strategic management, 77 Stakeholder engagement, 62 classification and level, 69 planners and policy makers, 70 process, 69 Stakeholder identification, 64 instrumental approaches, 64 literature, 65 normative approaches, 64 performance-oriented perspective, 65 process, 65 SSM, 65 urban system, 65 Stakeholder management, 63 identification, 64 literature, 63 performance, 64 planning and political problems, 63 urban transportation, 64 Stakeholder mapping and identification, 66 roles and characteristics, 66 stakeholder types, 67 Stakeholder theory, 63 Stock and flow diagrams (SFD), 42, 44, 45 Strategic management approaches, 2, 51 Strategic planning, see Urban transportation Structure-oriented behavior tests, 120 Superficial/mechanistic approach, 33 Sustainable urban transportation access to mobility, 6–8 definitions, 6, 7 ecosystem protection, 6, 8, 9 resource use, 6, 8 System dynamics (SD) adequate method, 46 application, 46 approach, 42, 48–50 balancing loop, 43, 44 CLD, 42–44 “exponential decay” behavior, 43, 44 exponential growth behaviors, 43 opportunities and limitations, 49–50 scientific approach, 42, 46 SFD, 42, 44, 45 single- vs. double-loop learning, 47 S-shaped behavior, 45 urban transportation performance, 47–49 Systemic and performance-oriented approach, 25 Systemic governance approach, 20

125 T Tax system equations, 112 Ticket price policy, 85 Traditional bureaucratic approach, 12 Traditional planning methods, 1 Traditional PM approaches, 33 Traditional transportation system, 107 Traditional urban transportation planning approaches, 1 Traffic congestion, 109 Transport supply bus and train capacity, 104 bus services, 103 decision-making process, 102 and demand, 101 SD modeling, 101 SFD model, 102 train capacity, 104 transportation mode, 100 travel demand, 100 user-switching behaviors, 103 Transportation demand management, 99 Transportation modeling approach, 47 Transportation modes, 100 Transportation system, 111 Travel demand age categories, 96 aging chain, 97 simplistic model, 96 transportation planning, 95 Travel mode choice, 110 policy makers, 105 time and cost, 105 Travel mode investment, 114 Travel modes, 10 Trip generation per mode, 114 U Urban issues, 4 Urban population, 96 Urban transportation definition, 1 modern social systems, 1 planning (see Urban transportation planning) strategic approach, 2 sustainable (see also Sustainable urban transportation) traditional planning methods, 1 Urban transportation planning, 63, 107 challenges, 3 changes institutional framework, 10–11 society, 3–5 technology, 9–10

Index

126 Urban transportation planning (cont.) governance, 14–17 institutional fragmentation, 11–14 and management, 95 managing land use, 3 managing transportation demand, 3 managing transportation system supply, 3 opportunities, 3 planning policy mandates, 5–9 public sector reforms, 11–14 public value management (see Public value management) subsystems, 2 Urban transportation systems, 93, 105 accessibility, 107 community outcomes, 94 economic domain, 106

environmental level, 106 financial resources, 95 literature, 94 mobility, 107 performance, 94 resources, 106 social domain, 106 strategic resources, 95 and supply, 94 system performance, 106 V Validation tests, 120 W Wicked problems, 18–20