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Spatial variation in shared ride-hail trip demand and factors contributing to sharing: Lessons from Chicago

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  • Dean, Matthew D.
  • Kockelman, Kara M.

Abstract

As ride-hailing becomes more common in cities, public agencies increasingly seek transportation network company (TNC) service data to understand (and potentially regulate) demand and service response. Despite the increase in ride-hailing or TNC demand and subsequent research into its determinants, there remains little research on shared TNC trips and the spatial distribution of trip demand across demographic and land use variables. Using Chicago as a case study, shared TNC trip data from 2019 was used to estimate the count and ratio of shared ride services, based on built environment, demographic, location, time of day, and trip details. Findings reveal that trip length, day of week designation, density of pedestrian and multi-modal infrastructure, and underlying socioeconomic characteristics of the origin zones influence the proportion and count of shared ride-hail trips. Of concern is that those using transit or active modes may be taking more ride-hailing trips, but these Chicago-region results indicate that the provision of pedestrian infrastructure and remoteness to transit stops result in fewer shared trips.

Suggested Citation

  • Dean, Matthew D. & Kockelman, Kara M., 2021. "Spatial variation in shared ride-hail trip demand and factors contributing to sharing: Lessons from Chicago," Journal of Transport Geography, Elsevier, vol. 91(C).
    • Handle: RePEc:eee:jotrge:v:91:y:2021:i:c:s0966692320310218
    DOI: 10.1016/j.jtrangeo.2020.102944
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    Cited by:

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    5. Li, Wu & Zhao, Shengchuan & Ma, Jingwen & Nielsen, Otto Anker & Jiang, Yu, 2023. "Book-ahead ride-hailing trip and its determinants: Findings from large-scale trip records in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
    6. Yang, Hongtai & Luo, Peng & Li, Chaojing & Zhai, Guocong & Yeh, Anthony G.O., 2023. "Nonlinear effects of fare discounts and built environment on ridesplitting adoption rates," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    7. Zhitao Li & Yuzhen Shang & Guanwei Zhao & Muzhuang Yang, 2022. "Exploring the Multiscale Relationship between the Built Environment and the Metro-Oriented Dockless Bike-Sharing Usage," IJERPH, MDPI, vol. 19(4), pages 1-21, February.
    8. Brown, Anne, 2022. "Not all fees are created equal: Equity implications of ride-hail fee structures and revenues," Transport Policy, Elsevier, vol. 125(C), pages 1-10.
    9. Gödde, Jan & Ruhrort, Lisa & Allert, Viktoria & Scheiner, Joachim, 2023. "User characteristics and spatial correlates of ride-pooling demand – Evidence from Berlin and Munich," Journal of Transport Geography, Elsevier, vol. 109(C).
    10. Yuan Liang & Bingjie Yu & Xiaojian Zhang & Yi Lu & Linchuan Yang, 2022. "The Short-term Impact of Congestion Taxes on Ridesourcing Demand and Traffic Congestion: Evidence from Chicago," Papers 2207.01793, arXiv.org, revised Feb 2023.
    11. Rico Krueger & Michel Bierlaire & Prateek Bansal, 2022. "A Data Fusion Approach for Ride-sourcing Demand Estimation: A Discrete Choice Model with Sampling and Endogeneity Corrections," Papers 2212.02178, arXiv.org.
    12. Liu, Hao & Devunuri, Saipraneeth & Lehe, Lewis & Gayah, Vikash V., 2023. "Scale effects in ridesplitting: A case study of the City of Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    13. Morteza Taiebat & Elham Amini & Ming Xu, 2022. "Sharing Behavior in Ride-hailing Trips: A Machine Learning Inference Approach," Papers 2201.12696, arXiv.org.
    14. Wali, Behram & Santi, Paolo & Ratti, Carlo, 2023. "Are californians willing to use shared automated vehicles (SAV) & renounce existing vehicles? An empirical analysis of factors determining SAV use & household vehicle ownership," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    15. Liang, Yuan & Yu, Bingjie & Zhang, Xiaojian & Lu, Yi & Yang, Linchuan, 2023. "The short-term impact of congestion taxes on ridesourcing demand and traffic congestion: Evidence from Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).
    16. Wang, Zhiqi & Zhang, Yufeng & Jia, Bin & Gao, Ziyou, 2024. "Comparative Analysis of Usage Patterns and Underlying Determinants for Ride-hailing and Traditional Taxi Services: A Chicago Case Study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    17. Loa, Patrick & Hossain, Sanjana & Liu, Yicong & Nurul Habib, Khandker, 2022. "How has the COVID-19 pandemic affected the use of ride-sourcing services? An empirical evidence-based investigation for the Greater Toronto Area," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 46-62.
    18. Li, Ziqi, 2022. "Understanding Factors Influencing Willingness to Ridesharing Using Big Trip Data and Interpretable Machine Learning," OSF Preprints chy4p, Center for Open Science.

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