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ZTE Communications ›› 2019, Vol. 17 ›› Issue (3): 9-14.doi: 10.12142/ZTECOM.201903003

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  • 收稿日期:2019-05-09 出版日期:2019-09-29 发布日期:2019-12-06

Big Data-Driven Residents’ Travel Mode Choice: A Research Overview

ZHAO Juanjuan1, XU Chengzhong2, MENG Tianhui1   

  1. 1.Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 51800, China
    2.University of Macao, Macau SAR 999078, China
  • Received:2019-05-09 Online:2019-09-29 Published:2019-12-06
  • About author:ZHAO Juanjuan received the Ph.D. degree from Chinese Academy of Sciences, China in 2017. She is an assistant professor with Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China. Her research interests include big data processing, data privacy, and urban computing.|XU Chengzhong received the Ph.D. degree from the University of Hong Kong, China in 1993. He is the Dean of the Faculty of Science and Technology, University of Macau, China and a Chair Professor of Computer Science of UM. He was a Chief Scientist of Shenzhen Institutes of Advanced Technology (SIAT) of Chinese Academy of Sciences and the Director of Institute of Advanced Computing and Digital Engineering of SIAT. He was also in the faculty of Wayne State University, USA for 18 years. Dr. Xu’s research interest is mainly in the areas of parallel and distributed systems, cloud and edge computing, and data-driven intelligence. He has published over 300 peer-reviewed papers on these topics with over 10K citations. Dr. Xu served in the editorial boards of leading journals, including IEEE Transactions on Computers, IEEE Transactions on Cloud Computing, IEEE Transactions on Parallel and Distributed Systems, and Journal of Parallel and Distributed Computing. He is the Associate Editor-in-Chief of ZTE Communication. He is IEEE Fellow and the Chair of IEEE Technical Committee of Distributed Processing.|MENG Tianhui received the Ph.D. degree in computer science from Free University of Berlin, Germany in 2017. He is currently an assistant professor with Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China. His research interests include mobile edge computing, big data processing, blockchain and Internet of Things.
  • Supported by:
    This work was supported in part by National Natural Science Foundation of China(No. 61802387);the Shenzhen Discipline Construction Project for Urban Computing and Data Intelligence


The research on residents’ travel mode choice mainly studies how traffic flows are shared by different traffic modes, which is the prerequisite for the government to establish transportation planning and policy. Traditional methods based on survey or small data sources are difficult to accurately describe, explain and verify residents’ travel mode choice behavior. Recently, thanks to upgrades of urban infrastructures, many real-time location-tracking devices become available. These devices generate massive real-time data, which provides new opportunities to analyze and explain resident travel mode choice behavior more accurately and more comprehensively. This paper surveys the current research status of big data-driven residents’ travel mode choice from three aspects: residents’ travel mode identification, acquisition of travel mode influencing factors, and travel mode choice model construction. Finally, the limitations of current research and directions of future research are discussed.

Key words: intelligent transportation, travel modes choice, urban computing