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Using machine learning for direct demand modeling of ridesourcing services in Chicago

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  • Yan, Xiang
  • Liu, Xinyu
  • Zhao, Xilei

Abstract

The exponential growth of ridesourcing services has been disrupting the transportation sector and changing how people travel. As ridesourcing continues to grow in popularity, being able to accurately predict the demand for it is essential for effective land-use and transportation planning and policymaking. Using recently released trip-level ridesourcing data in Chicago along with a range of variables obtained from publicly available data sources, we applied random forest, a widely-applied machine learning technique, to estimate a zone-to-zone (census tract) direct demand model for ridesourcing services. Compared to the traditional multiplicative models, the random forest model had a better model fit and achieved much higher predictive accuracy. We found that socioeconomic and demographic variables collectively contributed the most (about 50%) to the predictive power of the random forest model. Travel impedance, the built-environment characteristics, and the transit-supply-related variables are also indispensable in ridesourcing demand prediction.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:jotrge:v:83:y:2020:i:c:s0966692320300053
    DOI: 10.1016/j.jtrangeo.2020.102661
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    as
    1. Felipe F. Dias & Patrícia S. Lavieri & Venu M. Garikapati & Sebastian Astroza & Ram M. Pendyala & Chandra R. Bhat, 2017. "A behavioral choice model of the use of car-sharing and ride-sourcing services," Transportation, Springer, vol. 44(6), pages 1307-1323, November.
    2. Yu, Haitao & Peng, Zhong-Ren, 2019. "Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson regression," Journal of Transport Geography, Elsevier, vol. 75(C), pages 147-163.
    3. M. Keith Chen & Judith A. Chevalier & Peter E. Rossi & Emily Oehlsen, 2019. "The Value of Flexible Work: Evidence from Uber Drivers," Journal of Political Economy, University of Chicago Press, vol. 127(6), pages 2735-2794.
    4. Dagenais, M.G. & Gaudry, M.J.I., 1986. "Can Aggregate Direct Travel Demand Models Work?," Cahiers de recherche 8603, Universite de Montreal, Departement de sciences economiques.
    5. 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.
    6. Anne Brown, 2019. "Redefining Car Access," Journal of the American Planning Association, Taylor & Francis Journals, vol. 85(2), pages 83-95, April.
    7. Young, Mischa & Allen, Jeff & Farber, Steven, 2020. "Measuring when Uber behaves as a substitute or supplement to transit: An examination of travel-time differences in Toronto," Journal of Transport Geography, Elsevier, vol. 82(C).
    8. Jinkyung Choi & Yong Lee & Taewan Kim & Keemin Sohn, 2012. "An analysis of Metro ridership at the station-to-station level in Seoul," Transportation, Springer, vol. 39(3), pages 705-722, May.
    9. 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.
    10. Reid Ewing & Robert Cervero, 2010. "Travel and the Built Environment," Journal of the American Planning Association, Taylor & Francis Journals, vol. 76(3), pages 265-294.
    11. 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.
    12. 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.
    13. Hall, Jonathan D. & Palsson, Craig & Price, Joseph, 2018. "Is Uber a substitute or complement for public transit?," Journal of Urban Economics, Elsevier, vol. 108(C), pages 36-50.
    14. 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.
    15. Kepaptsoglou, Konstantinos & Stathopoulos, Antony & Karlaftis, Matthew G., 2017. "Ridership estimation of a new LRT system: Direct demand model approach," Journal of Transport Geography, Elsevier, vol. 58(C), pages 146-156.
    16. Jinbao Zhao & Wei Deng & Yan Song & Yueran Zhu, 2014. "Analysis of Metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models," Transportation, Springer, vol. 41(1), pages 133-155, January.
    17. Iseki, Hiroyuki & Liu, Chao & Knaap, Gerrit, 2018. "The determinants of travel demand between rail stations: A direct transit demand model using multilevel analysis for the Washington D.C. Metrorail system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 635-649.
    Full references (including those not matched with items on IDEAS)

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    7. Liu, Jixiang & Xiao, Longzhu, 2023. "Non-linear relationships between built environment and commuting duration of migrants and locals," Journal of Transport Geography, Elsevier, vol. 106(C).
    8. 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).
    9. 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.
    10. Yang, Jiawen & Cao, Jason & Zhou, Yufei, 2021. "Elaborating non-linear associations and synergies of subway access and land uses with urban vitality in Shenzhen," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 74-88.
    11. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
    12. 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.
    13. Alsaleh, Nael & Farooq, Bilal, 2021. "Interpretable data-driven demand modelling for on-demand transit services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 1-22.
    14. Peikun Li & Quantao Yang & Wenbo Lu, 2024. "Nonlinear Relationship of Multi-Source Land Use Features with Temporal Travel Distances at Subway Station Level: Empirical Study from Xi’an City," Land, MDPI, vol. 13(7), pages 1-16, July.
    15. 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.
    16. Morteza Taiebat & Elham Amini & Ming Xu, 2022. "Sharing Behavior in Ride-hailing Trips: A Machine Learning Inference Approach," Papers 2201.12696, arXiv.org.
    17. Lin Zhang & Suhong Zhou & Lanlan Qi & Yue Deng, 2022. "Nonlinear Effects of the Neighborhood Environments on Residents’ Mental Health," IJERPH, MDPI, vol. 19(24), pages 1-17, December.
    18. Zhang, Xiaojian & Zhao, Xilei, 2022. "Machine learning approach for spatial modeling of ridesourcing demand," Journal of Transport Geography, Elsevier, vol. 100(C).
    19. 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.
    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.
    21. Wang, Sicheng & Du, Rui & Lee, Annie S., 2024. "Ridesourcing regulation and traffic speeds: A New York case," Journal of Transport Geography, Elsevier, vol. 116(C).
    22. Wang, Sicheng & Noland, Robert B., 2021. "What is the elasticity of sharing a ridesourcing trip?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 284-305.
    23. Tulio Silveira-Santos & Thais Rangel & Juan Gomez & Jose Manuel Vassallo, 2024. "Forecasting Moped Scooter-Sharing Travel Demand Using a Machine Learning Approach," Sustainability, MDPI, vol. 16(13), pages 1-20, June.
    24. 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).
    25. Wang, Sicheng & Huang, Xiao & Shen, Qing, 2024. "Disparities in resilience and recovery of ridesourcing usage during COVID-19," Journal of Transport Geography, Elsevier, vol. 114(C).

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