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How Does the Urban Built Environment Affect Online Car-Hailing Ridership Intensity among Different Scales?

Author

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  • Guanwei Zhao

    (School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
    Institute of Land Resources and Coastal Zone, Guangzhou University, Guangzhou 510006, China)

  • Zhitao Li

    (School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China)

  • Yuzhen Shang

    (School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China)

  • Muzhuang Yang

    (School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
    Institute of Land Resources and Coastal Zone, Guangzhou University, Guangzhou 510006, China)

Abstract

Understanding the effect of the urban built environment on online car-hailing ridership is crucial to urban planning. However, how the effects change with the analysis scales are still noteworthy. Therefore, a multiscale exploratory study was conducted in Chengdu, China, by using the stepwise regression selection and three spatial regression models. The main findings are summarized as follows. First, as the grid size increases, the number of built environment factors that have significant effects on trip intensity decrease continuously. Second, the effects of population density and road density are always positive from the 500 m grid to the 3000 m grid. As the analysis scale increases, the effect of proximity to public transportation shifts from inhibitory to facilitation, while the positive effect of land-use mix becomes stronger. Land-use type has both positive and negative effects and shows different characteristics at different scales. Third, the effects of built environment factors on online car-hailing trip intensity show different spatial variability characteristics at different scales. The effect of population density gradually decreases from north to south. The effect of road network density shows circling and wave patterns, with the former at relatively fine scales and the latter at relatively coarse scales. The spatial variation in the effect of land-use mix can only be observed more significantly at a relatively coarse scale. The effect of bus stop density is only obvious at the relatively fine and medium scales and shows a wave-like pattern and a circle-like pattern. The effect of various land-use types shows different spatial patterns at different scales, including wave-like pattern, circle-like pattern, and multi-core-like pattern. The spatial variation in the effects of various land-use factors gradually decrease with the increase in the analysis scale.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:9:p:5325-:d:803563
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    References listed on IDEAS

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    Cited by:

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    2. Zhenbao Wang & Xin Gong & Yuchen Zhang & Shuyue Liu & Ning Chen, 2023. "Multi-Scale Geographically Weighted Elasticity Regression Model to Explore the Elastic Effects of the Built Environment on Ride-Hailing Ridership," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    3. Jianqi Li & Wenbao Zeng & Weiqi Liu & Rongjun Cheng, 2024. "Prediction on Demand for Regional Online Car-Hailing Travel Based on Self-Attention Memory and ConvLSTM," Sustainability, MDPI, vol. 16(13), pages 1-18, July.
    4. 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.
    5. HUO, Zhengqi & YANG, Xiaobao & LIU, Xiaobing & YAN, Xuedong, 2024. "Spatio-temporal analysis on online designated driving based on empirical data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).

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