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

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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)

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.

Suggested Citation

  • Xueli Chang & Haiyang Chen & Jianzhong Li & Xufeng Fei & Haitao Xu & Rui Xiao, 2024. "Travel Characteristics of Urban Residents Based on Taxi Trajectories in China: Beijing, Shanghai, Shenzhen, and Wuhan," Sustainability, MDPI, vol. 16(7), pages 1-15, March.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:7:p:2694-:d:1363546
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    References listed on IDEAS

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    1. Lixin Yan & Bowen Sheng & Yi He & Shan Lu & Junhua Guo, 2022. "Forecasting and Planning Method for Taxi Travel Combining Carbon Emission and Revenue Factors—A Case Study in China," IJERPH, MDPI, vol. 19(18), pages 1-20, September.
    2. Yang, Zhuo & Franz, Mark L. & Zhu, Shanjiang & Mahmoudi, Jina & Nasri, Arefeh & Zhang, Lei, 2018. "Analysis of Washington, DC taxi demand using GPS and land-use data," Journal of Transport Geography, Elsevier, vol. 66(C), pages 35-44.
    3. Wang, Hua & Zhao, De & Cai, Yutong & Meng, Qiang & Ong, Ghim Ping, 2021. "Taxi trajectory data based fast-charging facility planning for urban electric taxi systems," Applied Energy, Elsevier, vol. 286(C).
    4. Steven Farber & Antonio Páez, 2007. "A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations," Journal of Geographical Systems, Springer, vol. 9(4), pages 371-396, December.
    5. Yuyang Wu & Yao Yao & Shuliang Ren & Shiyi Zhang & Qingfeng Guan, 2023. "How do urban services facilities affect social segregation among people of different economic levels? A case study of Shenzhen city," Environment and Planning B, , vol. 50(6), pages 1502-1517, July.
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