Big Data-Driven Residents’ Travel Mode Choice: A Research Overview
ZHAO Juanjuan1, XU Chengzhong2, and MENG Tianhui1
(1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 51800, China;
2. University of Macao, Macau SAR 999078, China)
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 residents 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.
intelligent transportation; travel modes choice; urban computing