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Park-and-ride lot choice model using random utility maximization and random regret minimization

Author

Listed:
  • Bibhuti Sharma

    (The University of Queensland)

  • Mark Hickman

    (The University of Queensland)

  • Neema Nassir

    (Massachusetts Institute of Technology)

Abstract

This research aims to understand the park-and-ride (PNR) lot choice behaviour of users i.e., why PNR user choose one PNR lot versus another. Multinomial logit models are developed, the first based on the random utility maximization (RUM) concept where users are assumed to choose alternatives that have maximum utility, and the second based on the random regret minimization (RRM) concept where users are assumed to make decisions such that they minimize the regret in comparison to other foregone alternatives. A PNR trip is completed in two networks, the auto network and the transit network. The travel time of users for both the auto network and the transit network are used to create variables in the model. For the auto network, travel time is obtained using information from the strategic transport network using EMME/4 software, whereas travel time for the transit network is calculated using Google’s general transit feed specification data using a backward time-dependent shortest path algorithm. The involvement of two different networks in a PNR trip causes a trade-off relation within the PNR lot choice mechanism, and it is anticipated that an RRM model that captures this compromise effect may outperform typical RUM models. We use two forms of RRM models; the classical RRM and µRRM. Our results not only confirm a decade-old understanding that the RRM model may be an alternative concept to model transport choices, but also strengthen this understanding by exploring differences between two models in terms of model fit and out-of-sample predictive abilities. Further, our work is one of the few that estimates an RRM model on revealed preference data.

Suggested Citation

  • Bibhuti Sharma & Mark Hickman & Neema Nassir, 2019. "Park-and-ride lot choice model using random utility maximization and random regret minimization," Transportation, Springer, vol. 46(1), pages 217-232, February.
    • Handle: RePEc:kap:transp:v:46:y:2019:i:1:d:10.1007_s11116-017-9804-0
    DOI: 10.1007/s11116-017-9804-0
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    References listed on IDEAS

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    1. Guevara, C. Angelo & Fukushi, Mitsuyoshi, 2016. "Modeling the decoy effect with context-RUM Models: Diagrammatic analysis and empirical evidence from route choice SP and mode choice RP case studies," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 318-337.
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    Cited by:

    1. Wong, Stephen D & Chorus, Caspar G & Shaheen, Susan A & Walker, Joan L, 2020. "A Revealed Preference Methodology to Evaluate Regret Minimization with Challenging Choice Sets: A Wildfire Evacuation Case Study," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2k12q9ph, Institute of Transportation Studies, UC Berkeley.
    2. Jiawei Gui & Qunqi Wu, 2020. "Multiple Utility Analyses for Sustainable Public Transport Planning and Management: Evidence from GPS-Equipped Taxi Data in Haikou," Sustainability, MDPI, vol. 12(19), pages 1-46, September.
    3. Elżbieta Macioszek & Agata Kurek, 2020. "The Use of a Park and Ride System—A Case Study Based on the City of Cracow (Poland)," Energies, MDPI, vol. 13(13), pages 1-26, July.

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