Smart Urban Mobility
Transport Planning in the Age of Big Data and Digital Twins
- 1st Edition - February 8, 2023
- Imprint: Elsevier Science
- Author: Ivana Cavar Semanjski
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 0 7 1 7 - 8
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 0 8 9 1 - 5
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-e… Read more
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Request a sales quoteSmart 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.
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
Transport planners, urban policymakers, public administrators, city managers, data scientists, and consulting companies managing smart city interventions and data-driven urban transformation projects
- Cover image
- Title page
- Table of Contents
- 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
- Edition: 1
- Published: February 8, 2023
- No. of pages (Paperback): 266
- No. of pages (eBook): 266
- Imprint: Elsevier Science
- Language: English
- Paperback ISBN: 9780128207178
- eBook ISBN: 9780128208915
IS
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.
Affiliations and expertise
Faculty of Engineering and Architecture, Department of Industrial Systems Engineering and Product Design, Ghent University, Ghent, BelgiumRead Smart Urban Mobility on ScienceDirect