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A model of deadheading trips and pick-up locations for ride-hailing service vehicles

Author

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  • Nair, Gopindra S.
  • Bhat, Chandra R.
  • Batur, Irfan
  • Pendyala, Ram M.
  • Lam, William H.K.
Abstract
The mode share of app-based ride-hailing services has been growing steadily in recent years and this trend is expected to continue. Ride-hailing services generate two types of trips – passenger hauling trips and deadheading trips. Passenger hauling trips are the trips made while transporting passengers between places. Virtually all other trips made by a ride-hailing vehicle when there are no passengers in the vehicle are called deadheading trips or empty trips. Trips between the drop-off location of one passenger and the pick-up location of the next passenger could comprise a substantial share of total travel by ride-hailing vehicles, both in terms of number of trips and miles of travel. This paper aims to model the deadheading trips produced by app-based ride-hailing services at the disaggregate level of individual trips. Passenger trip data published by the app-based ride-hailing company Ride Austin is used to impute deadheading trips. The pick-up locations of passengers are then modeled using a nonlinear-in-parameters multinomial logit framework, essentially capturing the deadheading that takes place from the drop-off of one passenger to the pick-up of the next passenger. The model is sensitive to socio-demographic characteristics, as well as employment opportunities and built environment characteristics of the study area. The model results shed light on the characteristics of deadheading trips at different locations and at different time periods in a day. The paper concludes with a discussion of how transportation planners and app-based ride-hailing companies may utilize knowledge about deadheading to enact policies and pricing schemes that reduce deadheading.

Suggested Citation

  • Nair, Gopindra S. & Bhat, Chandra R. & Batur, Irfan & Pendyala, Ram M. & Lam, William H.K., 2020. "A model of deadheading trips and pick-up locations for ride-hailing service vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 289-308.
    • Handle: RePEc:eee:transa:v:135:y:2020:i:c:p:289-308
    DOI: 10.1016/j.tra.2020.03.015
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    References listed on IDEAS

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    2. Yan, Pengyu & Lee, Chung-Yee & Chu, Chengbin & Chen, Cynthia & Luo, Zhiqin, 2021. "Matching and pricing in ride-sharing: Optimality, stability, and financial sustainability," Omega, Elsevier, vol. 102(C).
    3. Sumitkumar, Rathor & Al-Sumaiti, Ameena Saad, 2024. "Shared autonomous electric vehicle: Towards social economy of energy and mobility from power-transportation nexus perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    4. Jason Soria & Shelly Etzioni & Yoram Shiftan & Amanda Stathopoulos & Eran Ben-Elia, 2022. "Microtransit adoption in the wake of the COVID-19 pandemic: evidence from a choice experiment with transit and car commuters," Papers 2204.01974, arXiv.org.
    5. Peng, Zixuan & Shan, Wenxuan & Zhu, Xiaoning & Yu, Bin, 2022. "Many-to-one stable matching for taxi-sharing service with selfish players," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 255-279.
    6. Sun, Luoyi & Teunter, Ruud H. & Hua, Guowei & Wu, Tian, 2020. "Taxi-hailing platforms: Inform or Assign drivers?," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 197-212.
    7. Xiong, Ziyue & Jian Li, & Wu, Hangbin, 2021. "Understanding operation patterns of urban online ride-hailing services: A case study of Xiamen," Transport Policy, Elsevier, vol. 101(C), pages 100-118.

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