IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v144y2021icp170-188.html
   My bibliography  Save this article

Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships

Author

Listed:
  • Xu, Yiming
  • Yan, Xiang
  • Liu, Xinyu
  • Zhao, Xilei

Abstract

Ridesharing is critical for promoting transportation sustainability. As a new form of ridesharing services, ridesplitting has rarely been studied. Based on the Chicago ridesourcing trip data, this study explores how ridesplitting adoption rate (i.e., the proportion of ridesourcing trips with ridesharing authorization) varies across space and what factors are associated with these variations. We find large variations in ridesplitting adoption rates across neighborhoods (Census Tracts) and across origin–destination (Census-Tract-to-Census-Tract) pairs. Particularly, the ridesplitting adoption rate is low for airport rides. We further apply a random forest model to explore which factors are key predictors of ridesplitting adoption rate across O-D pairs and to explore their nonlinear associations. The results suggest that the socioeconomic and demographic variables collectively contribute to 68.60% of the predictive power of the model, but travel-cost variables and built-environment-related factors are also important. The most important variables associated with ridesplitting adoption are ethnic composition, median household income, education level, trip distance, and neighborhood density. We further examine the nonlinear association between neighborhood ridesplitting adoption rate and several key variables such as the percentage of white population, median household income, and neighborhood Walk Score. The revealed nonlinear patterns can help transportation professionals identify neighborhoods with the greatest potential to promote ridesplitting.

Suggested Citation

  • Xu, Yiming & Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2021. "Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 170-188.
    • Handle: RePEc:eee:transa:v:144:y:2021:i:c:p:170-188
    DOI: 10.1016/j.tra.2020.12.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856420307989
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2020.12.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tao, Tao & Wang, Jueyu & Cao, Xinyu, 2020. "Exploring the non-linear associations between spatial attributes and walking distance to transit," Journal of Transport Geography, Elsevier, vol. 82(C).
    2. Ding, Chuan & Cao, Xinyu & Wang, Yunpeng, 2018. "Synergistic effects of the built environment and commuting programs on commute mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 104-118.
    3. Khandker Nurul Habib & Yuan Tian & Hamid Zaman, 2011. "Modelling commuting mode choice with explicit consideration of carpool in the choice set formation," Transportation, Springer, vol. 38(4), pages 587-604, July.
    4. 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.
    5. Shaheen, Susan PhD & Chan, Nelson & Gaynor, Theresa, 2016. "Casual Carpooling in the San Francisco Bay Area: Understanding User Characteristics, Behaviors, and Motivations," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt4dh2h0rf, Institute of Transportation Studies, UC Berkeley.
    6. Rayle, Lisa & Dai, Danielle & Chan, Nelson & Cervero, Robert & Shaheen, Susan, 2016. "Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco," Transport Policy, Elsevier, vol. 45(C), pages 168-178.
    7. Rayle, Lisa & Dai, Danielle & Chan, Nelson & Cervero, Robert & Shaheen, Susan PhD, 2016. "Just A Better Taxi? A Survey-Based Comparison of Taxis, Transit, and Ridesourcing Services in San Francisco," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt60v8r346, Institute of Transportation Studies, UC Berkeley.
    8. Shaheen, Susan & Cohen, Adam & Zohdy, Ismail & Kock, Beaudry, 2016. "Smartphone Applications to Influence Travel Choices: Practices and Policies," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt8dq801g7, Institute of Transportation Studies, UC Berkeley.
    9. Lhéritier, Alix & Bocamazo, Michael & Delahaye, Thierry & Acuna-Agost, Rodrigo, 2019. "Airline itinerary choice modeling using machine learning," Journal of choice modelling, Elsevier, vol. 31(C), pages 198-209.
    10. Lavieri, Patrícia S. & Bhat, Chandra R., 2019. "Modeling individuals’ willingness to share trips with strangers in an autonomous vehicle future," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 242-261.
    11. Catherine Morency, 2007. "The ambivalence of ridesharing," Transportation, Springer, vol. 34(2), pages 239-253, March.
    12. Brown, Anne E., 2020. "Who and where rideshares? Rideshare travel and use in Los Angeles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 120-134.
    13. Ding, Chuan & Cao, Xinyu (Jason) & Næss, Petter, 2018. "Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 107-117.
    14. Haitao Yu & Zhong-Ren Peng, 2020. "The impacts of built environment on ridesourcing demand: A neighbourhood level analysis in Austin, Texas," Urban Studies, Urban Studies Journal Limited, vol. 57(1), pages 152-175, January.
    15. Anne Brown, 2019. "Redefining Car Access," Journal of the American Planning Association, Taylor & Francis Journals, vol. 85(2), pages 83-95, April.
    16. Susan Shaheen & Adam Cohen, 2019. "Shared ride services in North America: definitions, impacts, and the future of pooling," Transport Reviews, Taylor & Francis Journals, vol. 39(4), pages 427-442, July.
    17. Shaheen, Susan A. & Chan, Nelson D. & Gaynor, Teresa, 2016. "Casual carpooling in the San Francisco Bay Area: Understanding user characteristics, behaviors, and motivations," Transport Policy, Elsevier, vol. 51(C), pages 165-173.
    18. Ding, Chuan & Cao, Xinyu & Liu, Chao, 2019. "How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds," Journal of Transport Geography, Elsevier, vol. 77(C), pages 70-78.
    19. Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2020. "Using machine learning for direct demand modeling of ridesourcing services in Chicago," Journal of Transport Geography, Elsevier, vol. 83(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2020. "Using machine learning for direct demand modeling of ridesourcing services in Chicago," Journal of Transport Geography, Elsevier, vol. 83(C).
    3. Soria, Jason & Stathopoulos, Amanda, 2021. "Investigating socio-spatial differences between solo ridehailing and pooled rides in diverse communities," Journal of Transport Geography, Elsevier, vol. 95(C).
    4. Yang, Hongtai & Zheng, Rong & Li, Xuan & Huo, Jinghai & Yang, Linchuan & Zhu, Tong, 2022. "Nonlinear and threshold effects of the built environment on e-scooter sharing ridership," Journal of Transport Geography, Elsevier, vol. 104(C).
    5. Li, Shengxiao(Alex) & Zhai, Wei & Jiao, Junfeng & Wang, Chao (Kenneth), 2022. "Who loses and who wins in the ride-hailing era? A case study of Austin, Texas," Transport Policy, Elsevier, vol. 120(C), pages 130-138.
    6. 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).
    7. Du, Qiang & Zhou, Yuqing & Huang, Youdan & Wang, Yalei & Bai, Libiao, 2022. "Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership," Journal of Transport Geography, Elsevier, vol. 102(C).
    8. Schaller, Bruce, 2021. "Can sharing a ride make for less traffic? Evidence from Uber and Lyft and implications for cities," Transport Policy, Elsevier, vol. 102(C), pages 1-10.
    9. Gehrke, Steven R., 2020. "Uber service area expansion in three major American cities," Journal of Transport Geography, Elsevier, vol. 86(C).
    10. Wang, Hai & Yang, Hai, 2019. "Ridesourcing systems: A framework and review," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 122-155.
    11. 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).
    12. 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.
    13. Loa, Patrick & Nurul Habib, Khandker, 2021. "Examining the influence of attitudinal factors on the use of ride-hailing services in Toronto," Transportation Research Part A: Policy and Practice, Elsevier, vol. 146(C), pages 13-28.
    14. Zou, Zhenpeng & Cirillo, Cinzia, 2021. "Does ridesourcing impact driving decisions: A survey weighted regression analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 146(C), pages 1-12.
    15. 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.
    16. Shaheen, Susan & Cohen, Adam, 2020. "Chapter 3 - Mobility on demand (MOD) and mobility as a service (MaaS): early understanding of shared mobility impacts and public transit partnerships," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt5030f0cd, Institute of Transportation Studies, UC Berkeley.
    17. Punel, Aymeric & Stathopoulos, Amanda, 2017. "Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 18-38.
    18. Yin, Chun & Cao, Jason & Sun, Bindong & Liu, Jiahang, 2023. "Exploring built environment correlates of walking for different purposes: Evidence for substitution," Journal of Transport Geography, Elsevier, vol. 106(C).
    19. He, Zhengbing, 2021. "Portraying ride-hailing mobility using multi-day trip order data: A case study of Beijing, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 146(C), pages 152-169.
    20. Du, Mingyang & Cheng, Lin & Li, Xuefeng & Liu, Qiyang & Yang, Jingzong, 2022. "Spatial variation of ridesplitting adoption rate in Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 13-37.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transa:v:144:y:2021:i:c:p:170-188. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.