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How does ridesourcing substitute for public transit? A geospatial perspective in Chengdu, China

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  • Kong, Hui
  • Zhang, Xiaohu
  • Zhao, Jinhua

Abstract

The explosive growth of ridesourcing services has stimulated a debate on whether they represent a net substitute for or a complement to public transit. Among the empirical evidence that supports discussion of the net effect at the city level, analysis at the disaggregated level from a geospatial perspective is lacking. Besides, it remains unexplored the spatiotemporal pattern of ridesourcing's effect on public transit, and the factors that impact the effect. Using DiDi Chuxing data in Chengdu, China, this paper develops a three-level structure to recognize the potential substitution or complementary effects of ridesourcing on public transit. Furthermore, this paper investigates the effects through exploratory spatiotemporal data analysis, and examines the factors influencing the degree of substitution via linear, spatial autoregressive, and zero-inflated beta regression models. The results show that 33.1% of DiDi trips have the potential to substitute for public transit. The substitution rate is higher during the day (8:00–18:00), and the trend follows changes in public transit coverage. The substitution effect is more exhibited in the city center and the areas covered by the subway, while the complementary effect is more exhibited in suburban areas as public transit has poor coverage. Further examination of the factors impacting the relationship indicates that housing price is positively associated with the substitution rate, and distance to the nearest subway station has a negative association with it, while the effects of most built environment factors become insignificant in zero-inflated beta regression. Based on these findings, policy implications are drawn regarding the partnership between transit agencies and ridesourcing companies, the spatial-differentiated policies in the central and suburban areas, and the potential problems in providing ridesourcing service to the economically disadvantaged population.

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  • Kong, Hui & Zhang, Xiaohu & Zhao, Jinhua, 2020. "How does ridesourcing substitute for public transit? A geospatial perspective in Chengdu, China," Journal of Transport Geography, Elsevier, vol. 86(C).
    • Handle: RePEc:eee:jotrge:v:86:y:2020:i:c:s0966692319304934
    DOI: 10.1016/j.jtrangeo.2020.102769
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    7. Zhang, Xiaohu, 2021. "Beyond expected regularity of aggregate urban mobility: A case study of ridesourcing service," Journal of Transport Geography, Elsevier, vol. 95(C).
    8. Qiao, Si & Zhang, Mengzhu & Yeh, Anthony Gar-On, 2023. "Mind the gender gap in ride-hailing from the demand side," Journal of Transport Geography, Elsevier, vol. 107(C).
    9. André de Palma & Lucas Javaudin & Patrick Stokkink & Léandre Tarpin-Pitre, 2022. "Ride-sharing with inflexible drivers in the Paris metropolitan area," Post-Print hal-03880692, HAL.
    10. Liu, Yicong & Loa, Patrick & Wang, Kaili & Habib, Khandker Nurul, 2023. "Theory-driven or data-driven? Modelling ride-sourcing mode choices using integrated choice and latent variable model and multi-task learning deep neural networks," Journal of choice modelling, Elsevier, vol. 48(C).
    11. Ngo, Nicole S. & Götschi, Thomas & Clark, Benjamin Y., 2021. "The effects of ride-hailing services on bus ridership in a medium-sized urban area using micro-level data: Evidence from the Lane Transit District," Transport Policy, Elsevier, vol. 105(C), pages 44-53.
    12. 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.
    13. Zhang, Zhaolin & Zhai, Guocong & Xie, Kun & Xiao, Feng, 2022. "Exploring the nonlinear effects of ridesharing on public transit usage: A case study of San Diego," Journal of Transport Geography, Elsevier, vol. 104(C).
    14. Eldeeb, Gamal & Sears, Sean & Mohamed, Moataz, 2023. "What do users want from transit? Qualitative analysis of current and potential users' perceptions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 171(C).
    15. Zhang, Xiaojian & Zhao, Xilei, 2022. "Machine learning approach for spatial modeling of ridesourcing demand," Journal of Transport Geography, Elsevier, vol. 100(C).
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    17. André de Palma & Lucas Javaudin & Patrick Stokkink & Léandre Tarpin-Pitre, 2021. "Modelling Ridesharing in a Large Network with Dynamic Congestion," THEMA Working Papers 2021-16, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.

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