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Exploring the Influence of the Built Environment on the Demand for Online Car-Hailing Services Using a Multi-Scale Geographically and Temporally Weighted Regression Model

Author

Listed:
  • Rongjun Cheng

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

  • Wenbao Zeng

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

  • Xingjian Wu

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

  • Fuzhou Chen

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

  • Baobin Miao

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

Abstract

Online car-hailing is gradually shifting towards a predominant use of electric vehicles, a change that is advantageous for developing a sustainable society. Understanding the patterns of changes in online car-hailing travel can assist transportation authorities in optimizing vehicle dispatching, reducing idle rates, and minimizing resource wastage. The built environment influences the demand for online car-hailing travel. Previous studies have commonly employed the geographically weighted regression (GWR) model and the geographically and temporally weighted regression (GTWR) model to examine the relationship between the demand for online car-hailing trips and the built environment. However, these studies have ignored that the impact range of the built environment also varies with time and space. To fully consider the variations in the impact range of the built environment, this study established multi-scale geographically and temporally weighted regression (MGTWR) to examine the spatiotemporal impacts of urban built environments on the demand for online car-hailing travel. An empirical study was conducted to assess the effectiveness of the MGTWR model using point of interest (POI) data and online car-hailing order data from Haikou. The evaluation indicators showed that the MGTWR model has higher fitting accuracy than the GTWR model. Moreover, the impact of each type of POI on the demand for online car-hailing travel was analyzed by examining the temporal and spatial distribution of the regression coefficients. Additionally, we observed that transport facility POIs and healthcare service POIs exerted the most pronounced influence on the demand for online car-hailing. In contrast, the impact of shopping service POIs and catering service POIs was relatively weaker.

Suggested Citation

  • Rongjun Cheng & Wenbao Zeng & Xingjian Wu & Fuzhou Chen & Baobin Miao, 2024. "Exploring the Influence of the Built Environment on the Demand for Online Car-Hailing Services Using a Multi-Scale Geographically and Temporally Weighted Regression Model," Sustainability, MDPI, vol. 16(5), pages 1-22, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:1794-:d:1343514
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    References listed on IDEAS

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    1. Reid Ewing & Robert Cervero, 2010. "Travel and the Built Environment," Journal of the American Planning Association, Taylor & Francis Journals, vol. 76(3), pages 265-294.
    2. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    3. Daniel P. McMillen, 2004. "Geographically Weighted Regression: The Analysis of Spatially Varying Relationships," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 554-556.
    4. Guanwei Zhao & Zhitao Li & Yuzhen Shang & Muzhuang Yang, 2022. "How Does the Urban Built Environment Affect Online Car-Hailing Ridership Intensity among Different Scales?," IJERPH, MDPI, vol. 19(9), pages 1-25, April.
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