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Measuring uncertainty in discrete choice travel demand forecasting models

Author

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  • Olga Petrik
  • Filipe Moura
  • João de Abreu e Silva

Abstract

In transportation projects, uncertainty related to the difference between forecast and actual demand is of major interest for the decision-maker, as it can have a substantial influence on the viability of a project. This paper identifies and quantifies discrete choice model uncertainty, which is present in the model parameters and attributes, and determines its impact on risk taking for decision-making applied to a case study of the High-Speed Rail project in Portugal. The methodology includes bootstrapping for the parameter variation, a postulated triangular distribution for the mode-specific input and a probabilistic graphical model for the socio-economic input variation. In comparison to point estimates, the findings for mode shift results in a wider swing in the system, which constitutes valuable information for decision-makers. The methodology, findings and conclusions presented in this study can be generalized to projects involving similar models.

Suggested Citation

  • Olga Petrik & Filipe Moura & João de Abreu e Silva, 2016. "Measuring uncertainty in discrete choice travel demand forecasting models," Transportation Planning and Technology, Taylor & Francis Journals, vol. 39(2), pages 218-237, March.
    • Handle: RePEc:taf:transp:v:39:y:2016:i:2:p:218-237
    DOI: 10.1080/03081060.2015.1127542
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    Cited by:

    1. Huihui Wang & Weihua Zeng, 2019. "Revealing Urban Carbon Dioxide (CO 2 ) Emission Characteristics and Influencing Mechanisms from the Perspective of Commuting," Sustainability, MDPI, vol. 11(2), pages 1-22, January.
    2. Aguas, Oriana & Bachmann, Chris, 2022. "Assessing the effects of input uncertainties on the outputs of a freight demand model," Research in Transportation Economics, Elsevier, vol. 95(C).

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