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Does the decision rule matter for large-scale transport models?

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  • van Cranenburgh, Sander
  • Chorus, Caspar G.

Abstract

This paper is the first to study to what extent decision rules, embedded in disaggregate discrete choice models, matter for large-scale aggregate level mobility forecasts. Such large-scale forecasts are a crucial underpinning for many transport infrastructure investment decisions. We show, in the particular context of (linear-additive) utility maximization (RUM) and regret minimization (RRM) rules, that the decision rule matters for aggregate level mobility forecasts. We find non-trivial differences between the RUM-based and RRM-based transport model in terms of aggregate forecasts of passenger kilometers, demand elasticities, and monetary benefits of transport policies. This opens up new opportunities for policy analysts to enrich their sensitivity analysis toolbox.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transa:v:114:y:2018:i:pb:p:338-353
    DOI: 10.1016/j.tra.2018.01.035
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    Cited by:

    1. Collins, Mor & Etzioni, Shelly & Ben-Elia, Eran, 2024. "Travel behavior and system dynamics in a simple gamified automated multimodal network," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    2. 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.
    3. Haghani, Milad & Sarvi, Majid, 2019. "Laboratory experimentation and simulation of discrete direction choices: Investigating hypothetical bias, decision-rule effect and external validity based on aggregate prediction measures," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 134-157.
    4. S. Van Cranenburgh & S. Wang & A. Vij & F. Pereira & J. Walker, 2021. "Choice modelling in the age of machine learning -- discussion paper," Papers 2101.11948, arXiv.org, revised Nov 2021.

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