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

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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.

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Acknowledgements

Funding was provided by Department of Transport and Main Roads Queensland. Authors are grateful to Professor Carlo Giacomo Prato for his valuable comments and suggestions. Also, We want to thank anonymous referees who provided us with constructive comments.

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Authors and Affiliations

  1. The University of Queensland, Brisbane, Australia

    Bibhuti Sharma & Mark Hickman

  2. Massachusetts Institute of Technology, Cambridge, MA, USA

    Neema Nassir

Authors
  1. Bibhuti Sharma
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  2. Mark Hickman
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  3. Neema Nassir
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Contributions

BS: Literature search and review, data analysis, manuscript writing. MH: Overall research planning, supervision, advise on data analysis, manuscript editing. NN: A part of data analysis on finding transit travel times using BTDSP algorithm, manuscript editing.

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Correspondence to Bibhuti Sharma.

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All the authors declares that they have no conflict of interest.

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Sharma, B., Hickman, M. & Nassir, N. Park-and-ride lot choice model using random utility maximization and random regret minimization. Transportation 46, 217–232 (2019). https://doi.org/10.1007/s11116-017-9804-0

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