This book starts from the relationship between urban built environment and travel behavior and focuses on analyzing the origin of traffic phenomena behind the data through multi-source traffic big data, which makes the book unique and different from the previous data-driven traffic big data analysis literature. This book focuses on understanding, estimating, predicting, and optimizing mobility patterns. Readers can find multi-source traffic big data processing methods, related statistical analysis models, and practical case applications from this book.

This book bridges the gap between traffic big data, statistical analysis models, and mobility pattern analysis with a systematic investigation of traffic big data's impact on mobility patterns and urban planning.

Auteur

Dr. Shaopeng Zhong is an associate professor in School of Transportation and Logistics at Dalian University of Technology. In 2005, he received his bachelor's degree in transportation engineering from Harbin Institute of Technology, China. In 2010, he obtained his doctorate from Southeast University, China. He is a visiting scholar in urban and regional planning at University of North Carolina at Chapel Hill (2008-2010). He is a guest professor at Technical University of Denmark (2017-2018).

He has more than 20 years of professional experience in the field of sustainable urban planning and transportation planning, land use and transportation integration modeling, road congestion pricing, logic-driven transport big data analysis, emergency logistics, and shared autonomous mobility. He has written and published four books and more than thirty scientific papers in the top journals in the field of transportation planning, such as Transportation Research Part A, C, and E, European Journal of Operational Research, Journal of Transport Geography, Computers, Environment and Urban Systems, Journal of Transport & Health, Journal of Transport and Land Use, and Journal of Transportation Engineering.

He is a member of the Youth Expert Committee of China Intelligent Transportation Systems Association and a member of the Intelligent Transportation Professional Committee of China artificial intelligence society. He is the guest editor of Journal of Transport and Land Use and Journal of Advanced Transportation, editorial board member of Transportation Letters, Transportation Management, Journal of Civil Engineering Inter Disciplinaries, and Frontiers in Future Transportation. He is the chairman of the traffic behavior investigation and analysis technical committee of the World Transport Convention. He is the organizing committee and scientific committee of seven international conferences, such as the International Workshop on Integrated Land Use and Transport Modeling (ILUTM), 6th International Symposium on Travel Demand Management (TDM), Transportation Research Congress (TRC), etc.

Personal website: http://faculty.dlut.edu.cn/2010011103/en/index.htm

Dr. Daniel (Jian) Sun is a professor and executive dean of School of Future Transportation, Chang'an University. He has been working as director and professor of Smart City and Intelligent Transportation (SCIT) Interdisplinary Center, Shanghai Jiao Tong University (2011-2021). He obtained his Ph.D. in Transportation Research Center, University of Florida in 2009, and has been a senior visiting scholar at ETH-Zurich (2018.9-2019.3). His main research interests include urban transportation planning and land use, traffic control, urban driver behavior and simulation, urban transportation environment. He has serving as the committee chair of Smart City and Intelligent Transportation sub-committee in World Transport Convention (WTC) and has published more than 60 SCI/SSCI indexed journal papers since 2010, and has more than 30 papers accepted and presented in TRB annual meeting. He has been served in editorial committee board of several journals, including Transportation Research Interdisciplinary Perspectives (since 2019), Journal of International Transportation (since 2012), Journal of Traffic and Transportation Engineering (English Version) (since 2014), and the chief member of road and traffic engineering sub-committee, Shanghai Society of Civil Engineering (since 2012). Moreover, he has been an Expert Reviewer for the Transportation Science & Technology Project, Ministry of Transport, China, and the National Science & Technology Award since 2014.

Personal website: http://js.chd.edu.cn/jiaotong/sj2_en/list.htm

Contenu
Chapter 1 Logic driven traffic big data analytics: An introduction (New Material)Part I: MethodologyChapter 2 Built environment and travel behavior (New Material)Chapter 3 Data description and preparation (New Material)Chapter 4 Statistical models and methods (New Material)Chapter 5 Big data analytics and machine learning methods (New Material)Part II: ApplicationsTravel Demand AnalysisChapter 6 Spatial-temporal distribution model for travel origin-destination based on multi-source data (American Society of Civil Engineers, ASCE)Chapter 7 Spatiotemporal evolution of ride-sourcing markets under the new restriction policy: A case study in Shanghai (ELSEVIER)Traffic Congestion and Travel Time/SpeedChapter 8 Exploring spatially varying relationships between urban built environment and road travel time (ASCE)Chapter 9 Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data (World Society for Transport and Land Use Research, WSTLUR)Traffic Safety and Environmental AnalysisChapter 10 Analysis of the spatial-temporal distribution of traffic incidents based on urban built environment attributes and microblog data (New Material)Chapter 11 Analyzing spatiotemporal traffic line source emissions based on massive didi online car-hailing service data (ELSEVIER)Policy and OptimizationChapter 12 Evidence on the impact of exclusive bus lane on the average speed of bus and car (New Material)Chapter 13 Optimization of traffic signal timing based on computer vision and reinforcement learning (New Material)Travel Pattern AnalysisChapter 14 Taxi driver speeding: Who, when, where, and how? A comparative study between Shanghai and New York City (Taylor & Francis)Chapter 15 A ride-sourcing group prediction model based on convolutional neural network (New Material)