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Exploring spatiotemporal characteristics of ride-hailing ridership connecting with metro stations: A comparative analysis of holidays, weekdays, and weekends

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  • Li, Zhitao
  • Gao, Fan
  • Hao, Jingjing
  • Liang, Jian
  • Han, Chunyang
  • Tang, Jinjun

Abstract

Ride-hailing services offer practical solutions for addressing “first- and last-mile” connectivity challenges at metro stations. While previous research has explored the spatiotemporal patterns of metro station-based ride-hailing ridership (MBRR) on weekdays and weekends, it has largely overlooked the unique dynamics of holiday periods. Furthermore, the influence of the built environment on first-mile MBRR (FM-MBRR) and last-mile MBRR (LM-MBRR) has received insufficient attention. To address these gaps, this study investigates the characteristics of MBRR across regular weekdays, weekends, Valentine's Day, and the Spring Festival. We employed ordinary least squares (OLS) and spatial lag regression (SLR) models to analyze the impact of the built environment on MBRR at the station level. Using data from Shenzhen, our findings reveal that: 1) Metro station-based ride-hailing is predominantly used for accessing metro stations, with FM-MBRR consistently exceeding LM-MBRR. 2) The Spring Festival results in a decrease in MBRR, while Valentine's Day exhibits an increase in post-work activity and nighttime MBRR. 3) On Valentine's Day, travel distance positively influences FM-MBRR, reflecting longer ride-hailing trips for holiday-related activities. During the Spring Festival, tourist attractions significantly influence both FM-MBRR and LM-MBRR, highlighting the role of tourism in shaping holiday mobility patterns. These findings provide valuable insights for integrating ride-hailing services with metro systems, emphasizing the need to account for holiday-specific dynamics and local built environment characteristics in urban transportation planning.

Suggested Citation

  • Li, Zhitao & Gao, Fan & Hao, Jingjing & Liang, Jian & Han, Chunyang & Tang, Jinjun, 2025. "Exploring spatiotemporal characteristics of ride-hailing ridership connecting with metro stations: A comparative analysis of holidays, weekdays, and weekends," Journal of Transport Geography, Elsevier, vol. 123(C).
    • Handle: RePEc:eee:jotrge:v:123:y:2025:i:c:s0966692324003132
    DOI: 10.1016/j.jtrangeo.2024.104104
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    1. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
    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. Liao, Yuan, 2021. "Ride-sourcing compared to its public-transit alternative using big trip data," Journal of Transport Geography, Elsevier, vol. 95(C).
    4. Tu, Wei & Santi, Paolo & Zhao, Tianhong & He, Xiaoyi & Li, Qingquan & Dong, Lei & Wallington, Timothy J. & Ratti, Carlo, 2019. "Acceptability, energy consumption, and costs of electric vehicle for ride-hailing drivers in Beijing," Applied Energy, Elsevier, vol. 250(C), pages 147-160.
    5. 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).
    6. Cheng, Long & Wang, Kailai & De Vos, Jonas & Huang, Jie & Witlox, Frank, 2022. "Exploring non-linear built environment effects on the integration of free-floating bike-share and urban rail transport: A quantile regression approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 162(C), pages 175-187.
    7. Chen, Xian & Bai, Shuotian & Wei, Yongqin & Zhao, Yanhui & Yan, Peng & Jiang, Hai, 2023. "Passenger engagement dynamics in ride-hailing services: A heterogeneous hidden Markov approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    8. Thais Rangel & Juan Nicolas Gonzalez & Juan Gomez & Fernando Romero & Jose Manuel Vassallo, 2022. "Exploring ride-hailing fares: an empirical analysis of the case of Madrid," Transportation, Springer, vol. 49(2), pages 373-393, April.
    9. Zgheib, Najib & Abou-Zeid, Maya & Kaysi, Isam, 2020. "Modeling demand for ridesourcing as feeder for high capacity mass transit systems with an application to the planned Beirut BRT," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 70-91.
    10. Oded Cats & Rafal Kucharski & Santosh Rao Danda & Menno Yap, 2022. "Beyond the dichotomy: How ride-hailing competes with and complements public transport," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-17, January.
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