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Exploring the Spatiotemporal Associations Between Ride-Hailing Demand, Visual Walkability, and the Built Environment: Evidence from Chengdu, China

Author

Listed:
  • Rui Si

    (School of Architecture, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
    Shenzhen Key Laboratory of Urban Planning and Simulation Decision, Shenzhen 518055, China)

  • Yaoyu Lin

    (School of Architecture, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
    Shenzhen Key Laboratory of Urban Planning and Simulation Decision, Shenzhen 518055, China)

Abstract

Ride-hailing services have reshaped urban commuting patterns, yet the spatiotemporal mechanisms linking built environment features to ride-hailing demand remain underexplored. Existing studies often overlook the joint effects of origin–destination visual walkability. This study integrates ride-hailing GPS trajectories and geospatial data to quantify mobility patterns and built-environment indicators in Chengdu, China. A dual analytical framework combining global regression and localized modeling was applied to disentangle spatial–temporal influences of urban form and socioeconomic factors. The results reveal that population density, floor–area ratio, and housing prices positively correlate with demand, while road density and distance to city center exhibit negative associations. Visual walkability metrics show divergent effects: psychological greenery and pavement visibility reduce ride-hailing usage, whereas outdoor enclosure enhances it. Temporal analysis identifies time-dependent impacts of built environment variables on main urban area travel. Housing price effects demonstrate spatial globality, while population density and city-center proximity exhibit geographically bounded correlations. Notably, improved visual walkability in specific zones reduces reliance on ride-hailing by facilitating sustainable alternatives. These findings provide empirical support for optimizing urban infrastructure and land-use policies to promote equitable mobility systems. The proposed methodology offers a replicable framework for assessing transportation–land-use interactions, informing targeted interventions to achieve metropolitan sustainability goals through coordinated spatial planning and pedestrian-centric design.

Suggested Citation

  • Rui Si & Yaoyu Lin, 2025. "Exploring the Spatiotemporal Associations Between Ride-Hailing Demand, Visual Walkability, and the Built Environment: Evidence from Chengdu, China," Sustainability, MDPI, vol. 17(12), pages 1-30, June.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:12:p:5441-:d:1677845
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    References listed on IDEAS

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    1. Mark R. Stevens, 2017. "Does Compact Development Make People Drive Less?," Journal of the American Planning Association, Taylor & Francis Journals, vol. 83(1), pages 7-18, January.
    2. Zhang, Chuanchuan & Jia, Shen & Yang, Rudai, 2016. "Housing affordability and housing vacancy in China: The role of income inequality," Journal of Housing Economics, Elsevier, vol. 33(C), pages 4-14.
    3. Clewlow, Regina R. & Mishra, Gouri S., 2017. "Disruptive Transportation: The Adoption, Utilization, and Impacts of Ride-Hailing in the United States," Institute of Transportation Studies, Working Paper Series qt82w2z91j, Institute of Transportation Studies, UC Davis.
    4. Neves, Andre & Brand, Christian, 2019. "Assessing the potential for carbon emissions savings from replacing short car trips with walking and cycling using a mixed GPS-travel diary approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 123(C), pages 130-146.
    5. Zhang, Haoran & Chen, Jinyu & Li, Wenjing & Song, Xuan & Shibasaki, Ryosuke, 2020. "Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential," Applied Energy, Elsevier, vol. 269(C).
    6. Wang, Donggen & Cao, Xinyu, 2017. "Impacts of the built environment on activity-travel behavior: Are there differences between public and private housing residents in Hong Kong?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 25-35.
    7. Yi Lu & Guibo Sun & Chinmoy Sarkar & Zhonghua Gou & Yang Xiao, 2018. "Commuting Mode Choice in a High-Density City: Do Land-Use Density and Diversity Matter in Hong Kong?," IJERPH, MDPI, vol. 15(5), pages 1-13, May.
    8. Mark R. Stevens, 2017. "Response to Commentaries on “Does Compact Development Make People Drive Less?”," Journal of the American Planning Association, Taylor & Francis Journals, vol. 83(2), pages 151-158, April.
    9. Tian Li & Peng Jing & Linchao Li & Dazhi Sun & Wenbo Yan, 2019. "Revealing the Varying Impact of Urban Built Environment on Online Car-Hailing Travel in Spatio-Temporal Dimension: An Exploratory Analysis in Chengdu, China," Sustainability, MDPI, vol. 11(5), pages 1-17, March.
    10. Hu, Lirong & He, Shenjing & Han, Zixuan & Xiao, He & Su, Shiliang & Weng, Min & Cai, Zhongliang, 2019. "Monitoring housing rental prices based on social media:An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies," Land Use Policy, Elsevier, vol. 82(C), pages 657-673.
    11. Wenbo Zhang & Satish V. Ukkusuri & Jian John Lu, 2017. "Impacts of urban built environment on empty taxi trips using limited geolocation data," Transportation, Springer, vol. 44(6), pages 1445-1473, November.
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