IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v46y2019i1d10.1007_s11116-017-9804-0.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11116-017-9804-0
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11116-017-9804-0?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. 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.
    2. Ilona D M Bos & Rob E C M Van der Heijden & Eric J E Molin & Harry J P Timmermans, 2004. "The Choice of Park and Ride Facilities: An Analysis Using a Context-Dependent Hierarchical Choice Experiment," Environment and Planning A, , vol. 36(9), pages 1673-1686, September.
    3. Caspar Chorus & Michel Bierlaire, 2013. "An empirical comparison of travel choice models that capture preferences for compromise alternatives," Transportation, Springer, vol. 40(3), pages 549-562, May.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, November.
    5. C. Angelo Guevara & Caspar G. Chorus & Moshe E. Ben-Akiva, 2016. "Sampling of Alternatives in Random Regret Minimization Models," Transportation Science, INFORMS, vol. 50(1), pages 306-321, February.
    6. Moshe Ben-Akiva & Joffre Swait, 1986. "The Akaike Likelihood Ratio Index," Transportation Science, INFORMS, vol. 20(2), pages 133-136, May.
    7. van Cranenburgh, Sander & Guevara, Cristian Angelo & Chorus, Caspar G., 2015. "New insights on random regret minimization models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 74(C), pages 91-109.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.
    4. Amirreza Talebi, 2024. "Simulation in discrete choice models evaluation: SDCM, a simulation tool for performance evaluation of DCMs," Papers 2407.17014, arXiv.org, revised Jul 2024.
    5. Juan Palaguachi & Monserrath Padilla & Martin Ortega & Marco Romero Solorzano & Ruffo Villa Uvidia & Jairo Ortega & Diego Veloz-Cherrez, 2024. "Evaluating the Location of the Park-and-Ride System Using Multi-Criteria Methods: A Systematic Review," Sustainability, MDPI, vol. 16(23), pages 1-20, November.
    6. Leong, Joseph & Nassir, Neema & Mohri, Seyed Sina & Sarvi, Majid, 2024. "A dynamic discrete choice modelling approach for forward-looking travel mode choices," Transportation Research Part A: Policy and Practice, Elsevier, vol. 190(C).

    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. Geržinič, Nejc & van Cranenburgh, Sander & Cats, Oded & Lancsar, Emily & Chorus, Caspar, 2021. "Estimating decision rule differences between ‘best’ and ‘worst’ choices in a sequential best worst discrete choice experiment," Journal of choice modelling, Elsevier, vol. 41(C).
    2. van Cranenburgh, Sander & Chorus, Caspar G., 2018. "Does the decision rule matter for large-scale transport models?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PB), pages 338-353.
    3. John Buckell & Vrinda Vasavada & Sarah Wordsworth & Dean A. Regier & Matthew Quaife, 2022. "Utility maximization versus regret minimization in health choice behavior: Evidence from four datasets," Health Economics, John Wiley & Sons, Ltd., vol. 31(2), pages 363-381, February.
    4. Chorus, Caspar & van Cranenburgh, Sander & Daniel, Aemiro Melkamu & Sandorf, Erlend Dancke & Sobhani, Anae & Szép, Teodóra, 2021. "Obfuscation maximization-based decision-making: Theory, methodology and first empirical evidence," Mathematical Social Sciences, Elsevier, vol. 109(C), pages 28-44.
    5. Chorus, Caspar & van Cranenburgh, Sander & Dekker, Thijs, 2014. "Random regret minimization for consumer choice modeling: Assessment of empirical evidence," Journal of Business Research, Elsevier, vol. 67(11), pages 2428-2436.
    6. Stephane Hess & Andrew Daly & Richard Batley, 2018. "Revisiting consistency with random utility maximisation: theory and implications for practical work," Theory and Decision, Springer, vol. 84(2), pages 181-204, March.
    7. van Cranenburgh, Sander & Collins, Andrew T., 2019. "New software tools for creating stated choice experimental designs efficient for regret minimisation and utility maximisation decision rules," Journal of choice modelling, Elsevier, vol. 31(C), pages 104-123.
    8. van Cranenburgh, Sander & Guevara, Cristian Angelo & Chorus, Caspar G., 2015. "New insights on random regret minimization models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 74(C), pages 91-109.
    9. Alwosheel, Ahmad & van Cranenburgh, Sander & Chorus, Caspar G., 2018. "Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis," Journal of choice modelling, Elsevier, vol. 28(C), pages 167-182.
    10. van Cranenburgh, Sander & Rose, John M. & Chorus, Caspar G., 2018. "On the robustness of efficient experimental designs towards the underlying decision rule," Transportation Research Part A: Policy and Practice, Elsevier, vol. 109(C), pages 50-64.
    11. Hancock, Thomas O. & Broekaert, Jan & Hess, Stephane & Choudhury, Charisma F., 2020. "Quantum probability: A new method for modelling travel behaviour," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 165-198.
    12. Gevrek, Z.Eylem & Uyduranoglu, Ayse, 2015. "Public preferences for carbon tax attributes," Ecological Economics, Elsevier, vol. 118(C), pages 186-197.
    13. Chorus, Caspar G. & Koetse, Mark J. & Hoen, Anco, 2013. "Consumer preferences for alternative fuel vehicles: Comparing a utility maximization and a regret minimization model," Energy Policy, Elsevier, vol. 61(C), pages 901-908.
    14. Peng Jing & Mengxuan Zhao & Meiling He & Long Chen, 2018. "Travel Mode and Travel Route Choice Behavior Based on Random Regret Minimization: A Systematic Review," Sustainability, MDPI, vol. 10(4), pages 1-20, April.
    15. Richartz, P. Christoph & Abdulai, Awudu & Kornher, Lukas, 2020. "Attribute Non Attendance and Consumer Preferences for Online Food Products in Germany," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 69(01), March.
    16. 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.
    17. Bechler, Georg & Steinhardt, Claudius & Mackert, Jochen & Klein, Robert, 2021. "Product line optimization in the presence of preferences for compromise alternatives," European Journal of Operational Research, Elsevier, vol. 288(3), pages 902-917.
    18. Caspar G. Chorus & Sander Cranenburgh, 2018. "Specification of regret-based models of choice behaviour: formal analyses and experimental design based evidence—commentary," Transportation, Springer, vol. 45(1), pages 247-256, January.
    19. Sergio Colombo & Nick Hanley & Jordan Louviere, 2009. "Modeling preference heterogeneity in stated choice data: an analysis for public goods generated by agriculture," Agricultural Economics, International Association of Agricultural Economists, vol. 40(3), pages 307-322, May.
    20. Hancock, Thomas O. & Hess, Stephane & Choudhury, Charisma F., 2018. "Decision field theory: Improvements to current methodology and comparisons with standard choice modelling techniques," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 18-40.

    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:kap:transp:v:46:y:2019:i:1:d:10.1007_s11116-017-9804-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.