Smart Urban Mobility: Transport Planning in the Age of Big Data and Digital Twins

By Ivana Cavar Semanjski

Smart Urban Mobility: Transport Planning in the Age of Big Data and Digital Twins explores the data-driven paradigm shift in urban mobility planning and examines how well-established practices and strong data analytics efforts can be better aligned to fit transport planning practices and "smart" mobility management needs. The book provides a comprehensive survey of the major big data and technology resources derived from smart cities research which are collectively poised to transform urban mobility. Chapters highlight the important aspects of each data source affecting applicability, along with the outcomes of smart mobility measures and campaigns.Transport planners, urban policymakers, public administrators, city managers, data scientists, and consulting companies managing smart city interventions and data-driven urban transformation projects will gain a better understanding of this up-and-coming research from this book’s detailed overview and numerous practical examples and best practices for operational deployment.

• Addresses key principles underlying smart mobility, as well as opportunities and challenges of integrating big data-driven insights into transport planning and smart cities
• Presents practical advice on how to implement smart mobility advances, providing a benchmark reference by best practice examples in the field
• Examines synthesis of existing gaps, limitations, and big data potential beyond traditional data needs for transport planning, as well as examples of the best practices

• Language: English
• Publisher: Elsevier Science
• Release date: Feb 8, 2023
• ISBN: 9780128208915

Ivana Cavar Semanjski

Prof. Ivana Cavar Semanjski has over 20 years of work experience in mobility-related research. During this time, she has participated in more than 20 scientific projects, working with multidisciplinary teams on transport planning issues. She is affiliated with the Faculty of Engineering and Architecture, ISyE, at Ghent University, Belgium. She has published over 100 papers and has edited and co-authored seven books. She is also an expert evaluator at the European Commission for mobility planning- and urban data analytic- related topics, sharing her knowledge and passion for mobility data analytics with policy makers, industry partners, and researchers.

Book preview

Smart Urban Mobility

Transport Planning in the Age of Big Data and Digital Twins

Ivana Cavar Semanjski

Faculty of Engineering and Architecture, Department of Industrial Systems Engineering and Product Design, Ghent University, Ghent, Belgium

Table of Contents

Cover image

Title page

Copyright

Preface

Chapter 1. Introduction

1.1. Objectives of the chapter

1.2. Word cloud

1.3. Introduction

1.4. Background

1.5. Why smart mobility and why now?

1.6. Audiences

1.7. Chapter structure

Chapter 2. Introduction to smart mobility

2.1. Objectives of the chapter

2.2. Word cloud

2.3. Mobility

2.4. Smart city

2.5. Smart mobility

Chapter 3. The new challenge of smart urban mobility

3.1. Objectives of the chapter

3.2. Word cloud

3.3. Urban population trends

3.4. Multimodality

3.5. Connected mobility

3.6. ConnectedX

3.7. Electric vehicles

3.8. Shared mobility

3.9. Mobility as a service

3.10. Governance

3.11. Smart mobility innovations

3.12. Change management

3.13. State of the affairs

Chapter 4. Small and big data for mobility studies

4.1. Objectives of the chapter

4.2. Word cloud

4.3. Introduction

4.4. Traditional data collection approaches

4.5. Big data for mobility studies

Chapter 5. Data analytics

5.1. Objectives of the chapter

5.2. Word cloud

5.3. Data analytics introduction

5.4. Data analytics workflow

5.5. Machine learning

5.6. Data anonymization

Chapter 6. Transport planning and big data

6.1. Objectives of the chapter

6.2. Word cloud

6.3. Four-step transportation planning model

6.4. Literature review of big data advances for four-step transport planning model

Chapter 7. Data-driven mobility management

7.1. Objectives of the chapter

7.2. Word cloud

7.3. Introduction

7.4. Big data-driven mobility system monitoring

7.5. Analytics-based mobility management decision making support

7.6. Example: incentivization of mobility behavior

7.7. Example: mobility management as a service

Chapter 8. Digital twin

8.1. Objectives of the chapter

8.2. Word cloud

8.3. Digital twin

8.4. Example: electric vehicle's digital shadow

8.5. Example: urban air mobility

Chapter 9. Summary

9.1. Objectives of the chapter

9.2. Word cloud

9.3. About the book

9.4. Features

9.5. Summary of chapters

9.6. Some smart mobility lessons learned

List of acronyms

Index

Copyright

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Notices

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Preface

Traditionally, transport studies have mainly relied on surveys, or so-called small data, to understand travel behavior and mobility dynamics. Recent availability of various location-based techniques, such as Global Navigation Satellite Systems (GNSS) or Call Record Details (CDR), has created new opportunities to better understand mobility needs and patterns. However, to bridge between traditional techniques, new possibilities, and mobility planning or decision-making needs, plenty challenges still need to be addressed. The author believes that there is much to be gained in the meaningful application of big data analytics to smart city and smart mobility context and hopes that this book will be instrumental in facilitating this fusion for the good of urban mobility and increased quality of life.

The book addresses current challenges that arise in urban mobility context, provides systematic overview of big data sources and techniques, as well as data-driven advances and identified limitations, together with a number of examples and good practices, hopefully to provide a point of reference to all those who are interested in exploring the topic further.

With this, the author would also like to thank to a large number of people who have provided impact, influence, and support in creation of this book. To my parents, Mate and Mirjana for unconditional support and understanding, to my husband Silvio for all the kindness, encouragement, and constructive discussions that fueled this creative process, to Dragana and Dario, and to Toni and Petar for all the joy that they have brought and much needed focus on important things. Also, to the whole Ghent University's ISyE team, for fruitful collaboration and shared research curiosity, as well as knowledge, and inspiration that they have broth.

Chapter 1: Introduction

Abstract

This chapter provides a general introduction to the book and its outline. It introduces the motivation behind the book, sets the scene for the book content from the perspective of different audiences, and explains how the topics will be tacked.

Keywords

Audiences; Big data; Chapters; Introduction; Smart mobility; Transport planning

1.1. Objectives of the chapter

What is the background of this book?

What topics are addressed in this book?

Why is it relevant now?

Who is the intended audience of this book?

What intended audiences can expect to achieve reading this book?

1.2. Word cloud

Fig. 1.1 illustrates a word cloud with an overview of the content of this chapter.

1.3. Introduction

This book explores the data-driven paradigm-shift in urban mobility planning and how well-established practices that are not likely to be abandoned by transport planners, and strong data analytics efforts, mainly invested by computer scientist, can be better aligned to fit transport planning practices and mobility management needs. Hence, the book will explore the boundaries and interface between major subject areas as urban, mobility and smart city planning, and data science and analytics. This chapter sets the scene for the reminder of the book, gives more details regarding the background, intended audiences, and explains how the content will be tacked.

1.4. Background

The beginning of 2020s finds connected multimodal mobility in the forefront of urban transformation. Cities grow larger, and based on the United Nations (UN) estimates, it is expected that Europe's population in urban areas will increase from today's 74% to about 83.7% in 2050 (United Nations, 2018). At the same time, cities increasingly face problems caused by transport and traffic. European Environment Agency reports that congestion costs nearly 2% of the European Union's (EU) Gross Domestic Product annually while 85% of the EU's urban population is exposed to fine particulate matter PM2.5 at levels deemed harmful to health (European Environment Agency, 2016). To face this challenge, cities increasingly strive to implement SUMPs (Sustainable Urban Mobility Plans) aiming to increase the quality of life in their areas and encourage economic growth. Cities currently generate 80% (World Bank, 2020) of all economic growth; hence, their advances resonate strongly through a wider area.

On the other hand, over the past decades, there has been strong development in the domain of sensing technologies. It comes as no surprise that today most of us carry mobile phones with integrated various sensors (e.g., Global Navigation Satellite System sensors, accelerometer, microphone, etc.) or interact with different sensors while doing our daily activities (e.g., bank card payments, inductive loops on roads, etc.). All such interaction and activities produce torrents of data as a by-product of their operations, and some estimations indicate that this equals to an average of 1GB of content per individual daily (Semanjski et al., 2016). This huge data stream is often being referred to as big data and, among many things, is affecting the way we shape our urban space into smart cities and our mobility into smart mobility.

Figure 1.1  Introduction chapter word cloud.

These circumstances create exciting time to be urban mobility professional. Increased urban mobility complexity, rapidly rising data science capabilities, and mobility-related data availability all together create stimulating landscape for innovations and new possibilities, and it is a privilege to have the opportunity to write this book and communicate about many of these advances. Much of the book's content is derived from a series of research and innovation activities that I had privilege to be involved in over the past decades, as either a researcher, project manager, or expert evaluation for funding bodies as the European commission. These activities afforded a unique opportunity to collaborate with different domains and experts linked with both urban planning, decision making, and data science from both private and public sector. In my modest oppinion, much of the success related to the data-driven mobility innovation is closely linked with good understanding of the balance between capabilities andlimitations of big data applications as well as mobility planning practices. Hence, good understanding of data availability and limitations as well as mobility and urban planning needs is crucial. This brings us to the motivation to write this book, that is to support bridging between mobility planning and data sciences domains as key driving forces behind the smart mobility developments.

1.5. Why smart mobility and why now?

Both urban and mobility planning cater to human needs. Understanding human behavior seems to be a core element in achieving liveable cities and big data availability provides us with a possibility to gain insights into this behavior at levels and volumes that were not possible ever before. This provides significant opportunities to improved decision making at all levels. The book is my modest attempt to share experience gained through fusion of a background in transport and traffic sciences with a big data analytics passion. These two branches are entwined throughout all of my professional experience, and this path has been full of revelations and new insights into how data analytics can benefit urban mobility. The aim of this book is to build awareness, interest, and understanding within the smart mobility community, motivating researchers, and practitioners to become familiar with and endeavor into new big data-driven possibilities for smart mobility. And while the term big data itself might seem as omnipresent lately, there are significant opportunities and added value that can be achieved by integrating big data potential into mobility practice. The frequent use of the term can also indicate that a larger community has at least an awareness of the evolving domain, although this awareness might not always be built on a solid foundation. This is an additional motivation for writing this book at this particular moment. The hope is that this common interest in big data and related analytics will minister as a platform for gaining a better understanding of urban mobility interactions and increased efficiency that caters to more liveable cities.

1.6. Audiences

There is a twofold motivation behind highlighting the intended audience for the book. Firstly, it empowers the author with a clear vision of the intended target readership to steer the selection and presentation of the content. Secondly, it provides understanding to the readers regarding the value that can be attained by reading the content of the book. The book intends to address the following readers:

• Transport planners and practitioners;

• City officials and policy makers;

• University professors and students;

• Business analysts, data scientist, data engineers, and developers;

• Smart city and smart mobility advocates, consultants, and implementers;

• Citizen scientists and members of citizens' participation initiatives;

• Multidisciplinary urban planning and mobility projects managers.

The subsequent section describes each of these readership groups and explains the value they can obtain from reading the book.

1.6.1. Transport planners and practitioners

This group includes urban mobility planners and practitioners as traffic engineers, public transport operators, fright planners, and executive leaders in mobility. The book aims to provide this readership group with general understanding of big data, sensing techniques and analytics and how they can be applied in a practical and benificial way to urban mobility domain. To achieve this, book gives an overview of the main big data sources used for this purpose and the related best practices. This overview is shaped in an easy to understand way for those who have no technical or computer science background, but still clearly highlighting the important aspects of each of the data sources that can greatly affect the applicability and the outcomes of mobility measures and campaigns. This is also complemented with the practical examples, best practices, and lessons learned.

1.6.2. City officials and policy makers

This group includes city officials, city mayors, and other policy makers and executive leaders charged with implementing smart city and smart mobility vision. The book aims in providing this group with understanding of big data-driven potential and its added value, as well as existing limitations, when it comes to supporting the smart mobility planning and policy making. It provides illustrative examples of best practices and hands on advices based on the lessons learned by their peers.

1.6.3. University professors and students

University professors and students stand at the frontier of mobility-related research. This topic rarely comes isolated; hence, this book aims to support the readership group across related domains as transport and traffic sciences and engineering, computer sciences, urban planners, social geographers, social sciences, Geographic Information System scientist, engineers, Information and communication technology, and telecommunication by providing the state-of-the-art overview on big data applications in smart mobility. It is hoped that systematic overview and insight into practical needs and applications of smart mobility professionals as well as future smart mobility prospects will spur the research curiosity and inspire innovative research lines and efforts.

1.6.4. Business analysts, data scientist, data engineers, and developers

Business analysts, data scientist, data engineers, developers, and other data and analytics professionals are likely to be involved in the development of data-driven solutions for smart mobility and/or application of existing market solutions to mobility problems. The book aims to assist them in understanding the needs of transport planners and smart mobility decisions makers in order to derive clearly focused and enriching research questions as well as to provide an overview of how their expertise and solutions can be applied in a practical and useful way to smart mobility domain. To achieve this, the book also offers an introduction to the transport planning information needs and the state-of-the-art research literature overview of current advances in this domain.

1.6.5. Multidisciplinary urban planning and mobility projects managers

Managing smart city and smart mobility projects integrates knowledge, skills, tools, and techniques to project activities to meet the project requirements. It also means managing multidisciplinary teams including various sectors (industry, administration, research, end-users …). The book aims to provide this group with a trough understanding of how big data and analytics can be applied to mobility in a practical way. It assists in building common vocabulary and understanding of existing challenges among multidisciplinary team members to facilitate their collaboration and bridging among their expertises.

1.6.6. Citizen scientists and members of citizens' participation initiatives

Recent years have been colored with a number of co-creation activities in mobility domain. Very often, these activities involve citizen scientists (nonprofessional scientist involved in scientific research) and/or citizen initiatives (participatory involvement of citizens to influence their local institutions) that aim to support shaping of liveable smart cities. For this readership group, the book aims to provide up-to-date and useful resources on the world of big data and analytics within smart city and smart mobility context. It is hoped that an explanation of how latest data analytic techniques and technologies can be applied to liveable cities will facilitate communication and understanding among the initiatives and professionals to a mutual benefit.

1.6.7. Smart city and smart mobility advocates, consultants, and implementers

This readership group includes wide spectrum of smart city enthusiast and advocates that contribute to the landscape and shaping of new ideas and smart mobility applications. The book provides this group with information on current advances and best practices and assists in building the understanding regarding the multidisciplinary vocabulary and existing challenges within smart city and smart mobility domains.

1.7. Chapter structure

Whether you are student, professional trying to balance private life with work or manager trying to find couple of minutes between the projects to grow, finding time to read the full book in one breath seems to be challenging task in today's world. Hence, I have divided each chapter into several subchapters; smaller junks of content dedicated to a specific topic or a question. The idea behind this division is to make the content readable and digestible in small time pockets that might become available in our schedules; time on a train commute, while waiting for the boarding in the airport, those several minutes before falling asleep or a quarter to sit in the sun at the park before the next course starts. The subchapters are created to be readable in not more than 15min of time. This makes easier to follow the content and round up ideas until next opportunity to read comes.

Overall, the book contains nine chapters. Each chapter has a similar structure. It starts with three basic elements: introduction, objectives of the chapter, and the word cloud. The word cloud presents the words used most often within the chapter, with the size of the font proportional to the frequency of their mentioning in the chapter. This way it provides you with a quick and simple visual overview of the chapters' content. Chapter's objectives highlight the key topics and questions that will be tackled in that chapter, while short introduction sets the scene for the content.

1.7.1. Topics/chapters

As already mentioned, the book incorporates nine chapters. Each chapter addresses a range of subjects related to understanding smart mobility and data analytics challenges and follows a similar structure. The content of the subsequent chapters is summarized in the following sections.

1.7.1.1. Chapter 2: Introduction to smart mobility

This chapter sets the scene for the reminder of the book by introducing the key terminology and building the understanding related to the scope of the smart cities and smart mobility that will be tackled in more detail trough the following chapters.

1.7.1.2. Chapter 3: The new challenge of smart urban mobility

This chapter explores the paradigm-shift in urban mobility planning and motivation behind this shift. It introduces key challenges of modern urban mobility as connected mobility, multimodal mobility, and mobility-related governance challenges.

1.7.1.3. Chapter 4: Small and big data for mobility studies

The chapter small and big data for mobility studies sets the scene and presents the connection between the traditionally used data sources for mobility planning and big data potential. To do so, the chapter contains definitions of these terms, description of the data collection approaches, data examples, lists the main characteristic of each data source relevant for the transport planning and highlights the advantages and disadvantages of each of them. This includes, but is not limited to:

(i) survey-based mobility data collection,

(ii) global navigation satellite systems (as GPS, Galileo …) mobility data collection,

(iii) smartphone-based mobility data collected,

(iv) Call details records (CDR)-based mobility data collection,

(v) other big data sources for mobility studies (e.g., Internet of Things (IoT), public transport ticketing data).

1.7.1.4. Chapter 5: Data analytics

Chapter on data analytics introduces key data analytics concepts linked with the small and big data and their applications in the smart city and smart mobility contexts.

The chapter provides a comprehensive and systematic overview of data analytics fundamentals with a focus on machine learning techniques. It presents several selected methods in detail, as support vector machines, k-nearest neighbors, k-means, decision tree, neural networks, and cross-validation, and provides a number of illustrative and practical examples of their applications in the smart mobility context. The chapter is also intended to provide the requisite background to the reader for reading the chapters that follow.

1.7.1.5. Chapter 6: Four step transport planning model and big data

The chapter moves the story forward by giving a short insight into the way transport planning is done and has been done for decades. It introduces the general transport planning framework and main transport planning and forecasting models. It gives a more detailed description of one of the best known transport planning models, the four-step transport planning model and includes subchapters dedicated to each step of the four-step transport planning model (trip generation, trip distribution, mode choice, and route assignment).

For each of these steps, the overview of the state-of-the-art literature and the best results is given (based on each big data set introduced in the previous chapter). The idea behind this overview is to give a systematic reference, to both the researchers and the practitioners, on where are we at this point, what are the plausible applications of big data for smart mobility, what are the open questions that are crucial for fruitful implementation of big data-driven insights into smart mobility and transport planning. To researchers, this is a point of reference on where to focus their research in order to support smart mobility developments and for the transport planners and practitioners, it is a reference point to the existing advances and barriers related to the big data integrations.

1.7.1.6. Chapter 7: Data driven mobility management

As the previous chapter is more related to strategic and longitudinal transport planning, this chapter tackles the big data potential for operational smart mobility management in smart cities. It concerns questions such as data availability and real-time data analytics, data quality, and privacy, and open and commercial data use in the unified framework. It gives an overview and lessons learned based on two examples, one related to the provision of incentives to support mobility behavior changes and one related to the development of data-driven mobility management as a service framework.

1.7.1.7. Chapter 8: Digital twin

This chapter tackles the breakthrough methods for transport planning and mobility managements as a digital twin of urban area concept. It gives stratified overview of the digital twin, its components, and architecture. It discusses the role of the digital twin in the smart city context, either as a smart mobility tool or as a general tool for smart city assets life cycle monitoring, maintenance, and/or management. The chapter includes examples of digital twin applications in the smart mobility context.

1.7.1.8. Chapter 9: Summary

The book concludes with Chapter 9, which provides an overview of essential elements covered within the book. It distils the key information provided in the book to define advice for a wide range of smart mobility professionals, providing concise summary of actions to consider after digesting the content of the book.

It is followed with the list of acronyms that gives a brief overview of key acronyms and terminology used throughout book.

References

1. European Environment Agency, . Air quality in Europe report. Copenhagen, Denmark: European Environment Agency; 2016.

2. Semanjski I, Bellens R, Gautama S, Witlox F. Integrating big data into a sustainable mobility policy 2.0 planning support system. Sustainability. 2016;11(1142):8.

3. United Nations, . World urbanization prospects: The 2018 revision. New York, USA: United Nations, Department of Economic and Social Affairs, Population Division; 2018.

4. World Bank, . Urban development report. Washington, USA: World Bank; 2020.

Chapter 2: Introduction to smart mobility

Abstract

This chapter sets the scene for the remainder of the book by introducing the key terminologies such as mobility, sustainability, quality of life, smart city, and smart mobility through the literature-based discussion. The idea is to build the understanding related to the scope of the smart cities and smart mobility among readers coming from different domains and backgrounds, so that the concepts, challenges, and potential applications that would be built on these ideas and discussed in the following chapters can be read and comprehend with ease and joint understanding.

Keywords

Mobility; Quality of life; Responsive city; Smart city; Smart mobility; Sustainable city; Transport entities; Transport modes; Urban mobility

2.1. Objectives of the chapter

What is mobility?

What is urban mobility?

What is smart city?

What is sustainable city?

What is quality of life?

What are the smart city domains?

What is smart mobility?

2.2. Word cloud

Fig. 2.1 presents a word cloud with an overview of the content of this chapter.

2.3. Mobility

At the beginning of this book, it seems particularly important to have a clear common understanding of the key terms that will be used through the book. It also helps readers coming from different backgrounds to gain understanding about the scope of the book and context in which specific topics will be considered and discussed further on. The intention is to have as holistic as possible terminology definitions that are then put in the context of particular uses, or in our case, the mobility context. Having said this, it seems natural to start from the mobility definition itself.

Etymologically, the word mobility comes from the Latin word mōbilis indicating that something is movable or loose. In the 18th century, the term mobility came into wider utilization, particularly in the military context when it was used to indicate the ability of a military unit to move or be transported to a new position. This trend continued in the upcoming centuries and in the 19th century the term mobility can also be found in the physics domain (to describe the degree to which particles of a liquid or gas are in movement) and sociology (to depict people's ability to move between different social levels or professional occupations) (Sorokin, 1998).

Nowadays, the term mobility is so entwined into our everyday lives that some consider it a key component of the world today (Peter, 2017), and it is preeminently used in two main contexts: spatial and social. In the social context, it refers to the same usage that originates from the 19th century (to describe movement between different social levels or professional occupations), and in the spatial context, it refers to a movement between two spatial coordinates, hence capturing well the terminology both from the military and the physic domains.

However, in the scope of this book, we will focus only on the spatial mobility context. In more detail, we will consider mobility from a traffic and transport perspective as movement (change of spatial coordinates over time) of transport entities (humans, freight, information) by means of transport modes utilizing the transport infrastructure over the predefined rules and the context linked with this activity. Please note that in this aspect we do not consider, for example, pipelines as transport and/or transport infrastructure. As in this case, the moving object (e.g., water, gas, oil …) does not utilize the infrastructure over the predefined rules but rather moves freely following the basic physical laws. Hence, a little to nothing will be said about the pipelines within this book.

Figure 2.1  Introduction to smart mobility word cloud.

As the presented mobility definition seems rather long, it is worthwhile having a look at the specific segments of the definition. If we consider traffic and transport (Fig. 2.2) to be a movement of transport entities by means of transport modes utilizing the transport infrastructure over the predefined rules, then mobility can be seen as a wider term in its scope. Mobility (Fig. 2.3) encapsulates both the traffic and transport, but with the addition of a wider context. This means that mobility does not look merely at the movement, but alongside tries to understand a wider context of this movement, like understanding the purpose and the reasoning behind the need for the movement of the entities, understanding the consequences (e.g., pollution), accessibility to the linked infrastructure and services and interaction with the overall, build and nonbuild, environment.

Considering the transport entity mentioned in the definition, the three categories, humans, freight, and information, seem quite intuitive. They all use transport modes (for instance, freight uses containers, humans trains, information data packages) to move across the transport network (infrastructure) with the in advance known and accepted rules (e.g., for the utilization of the limited network capacity, resolving conflicting flows at the network nodes as intersections, etc.).

Figure 2.2  Transport and traffic.

All three categories of the transport entities are relevant for this book and will be captured throughout its content, although the information will be viewed from the big data analytics perspective and not from the data network optimization and/or design point of view. Nevertheless, it seems worthily noting that from the prospects of this book, our interest mainly lies with humans. Why? Because, both freight and information are being transported to satisfy the needs of people, either for goods or information. Hence, understanding human behavior and human needs are at the heart of any mobility planning. This brings us back to the notion that mobility concerns a wider context than just a technique and technology associated with the movement itself, but rather considers the full context of this movement's intricately tied causalities such as human needs.

Some of the key working definitions that will be used in this book related to mobility are indicated in the following section.

Figure 2.3  Mobility.

2.3.1. Terminology/definitions

Mobility: movement of transport entities by means of transport modes utilizing the transport infrastructure over the predefined rules and the context associated with this activity.

Movement or transport: change of spatial coordinates over time, but without changing the characteristics of the entity.

Transport entities: content that is being moved: humans, freight, and/or information.

Transport modes: the means or the way in which transport entities are being moved/transported.

Transport infrastructure: the basic static objects necessary for the operation of transport.

Traffic: stream of transport entities moving over the transport infrastructure (e.g., private cars on a public road or the messages or signals transmitted through a communications system).

Small terminology note: transportation is the same as transport, it is just that one term is used more in American English (transportation) and another in British English (transport). In this book, we will use British English terminology, hence the term transport.

2.3.2. Urban mobility

Urban mobility is a form of mobility that takes place in urban areas. This means that the whole movement or a trip, or at least a part of it, unfolds

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