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Modeling demand for ridesourcing as feeder for high capacity mass transit systems with an application to the planned Beirut BRT

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  • Zgheib, Najib
  • Abou-Zeid, Maya
  • Kaysi, Isam

Abstract

Ridesourcing (Uber, Careem, Lyft, …) is emerging as a main player in the transportation industry. However, its relation to mass transit remains ambiguous, with divided opinions on its complementarity or substitutive effect towards high capacity public transportation systems. This study examines the integration of ridesourcing and transit, particularly focusing on modeling the demand for mass transit when ridesourcing is used as an access or egress mode to mass transit. It extends the existing literature on the integration of transit and new mobility concepts by providing a modeling framework that incorporates all stages of multi-modal trips such as those that involve using mass transit. A mixed logit with error component structure is presented to capture correlations in unobserved factors across multi-modal alternatives sharing similar modes at certain stages. The framework incorporates uni-modal and multi-modal travel alternatives and distinguishes between access, main mode, and egress stages without applying constraints on possible combinations. An application to Beirut’s planned Bus Rapid Transit (BRT) system, performed on a data set of 392 respondents, reveals that ridesourcing as a feeder mode is mostly popular with young commuters while also being perceived as more reliable than feeder buses and jitneys. Awareness and familiarity are major drivers for the service implying higher potential in the future. A complementarity effect with transit is found as the introduction of ridesourcing at the feeders’ level is expected to drive an additional 2% of commuters to use the BRT. Decreasing ridesourcing fare is effective for its integration with transit, as a fare decrease of 50% increases BRT market share from 33.53% to 36.89% of all motorized trips, implying possible synergies between the two modes. Forecasting results further reveal that additional taxes on parking used by car commuters and increasing park and ride capacity at BRT stations are effective policies to augment BRT ridership.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:transa:v:138:y:2020:i:c:p:70-91
    DOI: 10.1016/j.tra.2020.05.019
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    References listed on IDEAS

    as
    1. Young, Mischa & Farber, Steven, 2019. "The who, why, and when of Uber and other ride-hailing trips: An examination of a large sample household travel survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 383-392.
    2. Cohen, Adam & Shaheen, Susan PhD, 2018. "Planning for Shared Mobility," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt0dk3h89p, Institute of Transportation Studies, UC Berkeley.
    3. Walker, Joan & Ben-Akiva, Moshe, 2002. "Generalized random utility model," Mathematical Social Sciences, Elsevier, vol. 43(3), pages 303-343, July.
    4. 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.
    5. Debrezion, Ghebreegziabiher & Pels, Eric & Rietveld, Piet, 2009. "Modelling the joint access mode and railway station choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(1), pages 270-283, January.
    6. David A Hensher & William H Greene & John M Rose, 2006. "Deriving Willingness-to-Pay Estimates of Travel-Time Savings from Individual-Based Parameters," Environment and Planning A, , vol. 38(12), pages 2365-2376, December.
    7. Alemi, Farzad & Circella, Giovanni & Mokhtarian, Patricia & Handy, Susan, 2018. "Exploring the latent constructs behind the use of ridehailing in California," Journal of choice modelling, Elsevier, vol. 29(C), pages 47-62.
    8. Cherchi, Elisabetta & Ortúzar, Juan de Dios, 2006. "On fitting mode specific constants in the presence of new options in RP/SP models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(1), pages 1-18, January.
    9. Young, Mischa & Farber, Steven, 2019. "The Who, Why, and When of Uber and other Ride-hailing Trips: An Examination of a Large Sample Household Travel Survey," OSF Preprints x7ryj, Center for Open Science.
    10. Contreras, Seth D. & Paz, Alexander, 2018. "The effects of ride-hailing companies on the taxicab industry in Las Vegas, Nevada," Transportation Research Part A: Policy and Practice, Elsevier, vol. 115(C), pages 63-70.
    11. Krygsman, Stephan & Dijst, Martin & Arentze, Theo, 2004. "Multimodal public transport: an analysis of travel time elements and the interconnectivity ratio," Transport Policy, Elsevier, vol. 11(3), pages 265-275, July.
    12. Habib, Khandker Nurul, 2019. "Mode choice modelling for hailable rides: An investigation of the competition of Uber with other modes by using an integrated non-compensatory choice model with probabilistic choice set formation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 205-216.
    13. Wen, Chieh-Hua & Wang, Wei-Chung & Fu, Chiang, 2012. "Latent class nested logit model for analyzing high-speed rail access mode choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(2), pages 545-554.
    14. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, June.
    15. Yap, Menno D. & Correia, Gonçalo & van Arem, Bart, 2016. "Preferences of travellers for using automated vehicles as last mile public transport of multimodal train trips," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 1-16.
    16. 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.
    17. Aurélie Glerum & Lidija Stankovikj & Michaël Thémans & Michel Bierlaire, 2014. "Forecasting the Demand for Electric Vehicles: Accounting for Attitudes and Perceptions," Transportation Science, INFORMS, vol. 48(4), pages 483-499, November.
    18. Arentze, Theo A. & Molin, Eric J.E., 2013. "Travelers’ preferences in multimodal networks: Design and results of a comprehensive series of choice experiments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 58(C), pages 15-28.
    19. Joan L. Walker & Yanqiao Wang & Mikkel Thorhauge & Moshe Ben-Akiva, 2018. "D-efficient or deficient? A robustness analysis of stated choice experimental designs," Theory and Decision, Springer, vol. 84(2), pages 215-238, March.
    20. Hensher, David A. & Rose, John M., 2007. "Development of commuter and non-commuter mode choice models for the assessment of new public transport infrastructure projects: A case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(5), pages 428-443, June.
    21. 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.
    22. Danaf, Mazen & Atasoy, Bilge & de Azevedo, Carlos Lima & Ding-Mastera, Jing & Abou-Zeid, Maya & Cox, Nathaniel & Zhao, Fang & Ben-Akiva, Moshe, 2019. "Context-aware stated preferences with smartphone-based travel surveys," Journal of choice modelling, Elsevier, vol. 31(C), pages 35-50.
    23. 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.
    24. Ben-Akiva, Moshe & McFadden, Daniel & Train, Kenneth, 2019. "Foundations of Stated Preference Elicitation: Consumer Behavior and Choice-based Conjoint Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 10(1-2), pages 1-144, January.
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