Travel Characteristics of Urban Residents Based on Taxi Trajectories in China: Beijing, Shanghai, Shenzhen, and Wuhan

Xueli Chang, Haiyang Chen (), Jianzhong Li, Xufeng Fei, Haitao Xu and Rui Xiao
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Xueli Chang: School of Computer Science, Hubei University of Technology, Wuhan 430068, China
Haiyang Chen: School of Computer Science, Hubei University of Technology, Wuhan 430068, China
Jianzhong Li: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Xufeng Fei: Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
Haitao Xu: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
Rui Xiao: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China

Sustainability, 2024, vol. 16, issue 7, 1-15

Abstract: With the advancement of urban modernization, more and more residents are flocking to large cities, leading to problems such as severe traffic congestion, uneven distribution of spatial resources, and deterioration of the urban environment. These challenges pose a serious threat to the coordinated development of cities. In order to better understand the travel behavior of metropolitan residents and provide valuable insights for urban planning, this study utilizes taxi trajectory data from the central areas of Beijing, Shanghai, Shenzhen, and Wuhan. First, the relationship between daytime taxi drop-off points and urban amenities is explored using Ordinary Least Squares (OLS). Subsequently, Geographically Weighted Regression (GWR) techniques were applied to identify spatial differences in these urban drivers. The results show that commonalities emerge across the four cities in the interaction between external transport stops and commercial areas. In addition, the average daily travel patterns of residents in these four cities show a trend of “three peaks and three valleys”, indicating the commonality of travel behavior. In summary, this study explores the travel characteristics of urban residents, which can help urban planners understand travel patterns more effectively. This is crucial for the strategic allocation of transport resources across regions, the promotion of sustainable urban transport, and the reduction in carbon emissions.

Keywords: traffic sustainability; residents travel pattern; urban morphology; least squares regression; geographically weighted regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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