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Dynamic Discrete Choice Models for Transportation

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  • Cinzia Cirillo
  • Renting Xu

Abstract

Discrete choice models have received widespread acceptance in transport research over the past three decades, being used in travel demand modelling and behavioural analysis; however, their applications have been mainly developed in a static context. There have been several dynamic models in transportation; but these formulations are not based on dynamic optimization principles and do not allow for changes in external factors. With the continuous and rapid changes in modern societies (i.e. introduction of advanced technologies, aggressive marketing strategies and innovative policies) it is more and more recognized by researchers in various disciplines from economics to social science that choice situations take place in a dynamic environment and that strong interdependencies exist among decisions made at different points in time. Dynamic discrete choice models (DDCMs) describe the behaviour of a forward-looking economic agent who chooses between several alternatives repeatedly over time. DDCMs are usually specified as an optimal stopping problem, where agents decide when to make a change in ownership of durable goods or in their behaviour. In this paper, we present the application of the dynamic formulation to short- to medium-term vehicle-holding decisions.

Suggested Citation

  • Cinzia Cirillo & Renting Xu, 2011. "Dynamic Discrete Choice Models for Transportation," Transport Reviews, Taylor & Francis Journals, vol. 31(4), pages 473-494.
    • Handle: RePEc:taf:transr:v:31:y:2011:i:4:p:473-494
    DOI: 10.1080/01441647.2010.533393
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    Cited by:

    1. Arkoudi, Ioanna & Krueger, Rico & Azevedo, Carlos Lima & Pereira, Francisco C., 2023. "Combining discrete choice models and neural networks through embeddings: Formulation, interpretability and performance," Transportation Research Part B: Methodological, Elsevier, vol. 175(C).
    2. Haoying Wang & Guohui Wu, 2022. "Modeling discrete choices with large fine-scale spatial data: opportunities and challenges," Journal of Geographical Systems, Springer, vol. 24(3), pages 325-351, July.
    3. Ioanna Arkoudi & Carlos Lima Azevedo & Francisco C. Pereira, 2021. "Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance," Papers 2109.12042, arXiv.org, revised Sep 2021.
    4. Chung, Yi-Shih & Ku, Ya-Han, 2023. "Effect of time stress and store visibility on the dynamics of passenger activity choices at airport terminals based on indoor trajectory data," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    5. Chenfeng Xiong & Di Yang & Lei Zhang, 2018. "A High-Order Hidden Markov Model and Its Applications for Dynamic Car Ownership Analysis," Service Science, INFORMS, vol. 52(6), pages 1365-1375, December.
    6. Mattioli, Giulio & Anable, Jillian & Vrotsou, Katerina, 2016. "Car dependent practices: Findings from a sequence pattern mining study of UK time use data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 89(C), pages 56-72.
    7. Ferrari, Paolo, 2014. "The dynamics of modal split for freight transport," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 70(C), pages 163-176.
    8. Sabouri, Sadegh & Tian, Guang & Ewing, Reid & Park, Keunhyun & Greene, William, 2021. "The built environment and vehicle ownership modeling: Evidence from 32 diverse regions in the U.S," Journal of Transport Geography, Elsevier, vol. 93(C).
    9. Frances Ifeoma Ukonze & Maxwell Umunna Nwachukwu & Harold Chike Mba & Donald Chiuba Okeke & Uloma Jiburum, 2020. "Determinants of Vehicle Ownership in Nigeria," SAGE Open, , vol. 10(2), pages 21582440209, May.
    10. Liu, Yan & Cirillo, Cinzia, 2018. "A generalized dynamic discrete choice model for green vehicle adoption," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PB), pages 288-302.
    11. Hasnine, Md Sami & Habib, Khandker Nurul, 2018. "What about the dynamics in daily travel mode choices? A dynamic discrete choice approach for tour-based mode choice modelling," Transport Policy, Elsevier, vol. 71(C), pages 70-80.
    12. Stathopoulos, Amanda & Cirillo, Cinzia & Cherchi, Elisabetta & Ben-Elia, Eran & Li, Yeun-Touh & Schmöcker, Jan-Dirk, 2017. "Innovation adoption modeling in transportation: New models and data," Journal of choice modelling, Elsevier, vol. 25(C), pages 61-68.
    13. Zhao, Jianfeng & Liu, Henry J. & Love, Peter E.D. & Greenwood, David J. & Sing, Michael C.P., 2022. "Public-private partnerships: A dynamic discrete choice model for road projects," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    14. Chenfeng Xiong & Lei Zhang, 2017. "Dynamic travel mode searching and switching analysis considering hidden model preference and behavioral decision processes," Transportation, Springer, vol. 44(3), pages 511-532, May.

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