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A Dynamic Formulation for Car Ownership Modeling

Author

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  • Cinzia Cirillo

    (Department of Civil and Environmental Engineering, University of Maryland, College Park, Maryland 20742; and Centre interuniversitaire de recherche sur les reseaux d’entreprise, la logistique et le transport (CIRRELT), Montréal, Québec H3C 3J7, Canada)

  • Renting Xu

    (Nielsen Audio, Columbia, Maryland 21046)

  • Fabian Bastin

    (Centre interuniversitaire de recherche sur les reseaux d’entreprise, la logistique et le transport (CIRRELT), Montréal, Québec H3C 3J7, Canada; and Department of Computer Science and Operations Research, University of Montréal, Montréal, Québec H3T 1J4, Canada)

Abstract

Discrete choice models are commonly used in transportation planning and modeling, but their theoretical basis and applications have been mainly developed in a static context. In this paper, we propose an estimation technique for analyzing the impact of technological changes on the dynamic of consumer demand. The proposed research presents a dynamic formulation that explicitly models market evolution and accounts for consumers’ expectations of future product characteristics. The timing of consumers’ decisions is formulated as a regenerative optimal stopping problem where the agent must decide on the optimal time of purchase. This model frame will be further improved by modeling the choice from a set of differentiated products whose characteristics randomly change over time. The framework proposed is developed and applied in the context of car ownership.

Suggested Citation

  • Cinzia Cirillo & Renting Xu & Fabian Bastin, 2016. "A Dynamic Formulation for Car Ownership Modeling," Transportation Science, INFORMS, vol. 50(1), pages 322-335, February.
    • Handle: RePEc:inm:ortrsc:v:50:y:2016:i:1:p:322-335
    DOI: 10.1287/trsc.2015.0597
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    References listed on IDEAS

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    Cited by:

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    2. Dong, Han & Cirillo, Cinzia, 2020. "Space-time dynamics: A modeling approach for commuting departure time on linked datasets," Journal of Transport Geography, Elsevier, vol. 82(C).
    3. Song, Yuchen & Li, Dawei & Liu, Dongjie & Cao, Qi & Chen, Junlan & Ren, Gang & Tang, Xiaoyong, 2022. "Modeling activity-travel behavior under a dynamic discrete choice framework with unobserved heterogeneity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    4. Ranjit R. Desai & Eric Hittinger & Eric Williams, 2022. "Interaction of Consumer Heterogeneity and Technological Progress in the US Electric Vehicle Market," Energies, MDPI, vol. 15(13), pages 1-25, June.
    5. Stanislav S. Borysov & Jeppe Rich, 2021. "Introducing synthetic pseudo panels: application to transport behaviour dynamics," Transportation, Springer, vol. 48(5), pages 2493-2520, October.
    6. Jungwoo Shin & Taehoon Lim & Moo Yeon Kim & Jae Young Choi, 2018. "Can Next-Generation Vehicles Sustainably Survive in the Automobile Market? Evidence from Ex-Ante Market Simulation and Segmentation," Sustainability, MDPI, vol. 10(3), pages 1-16, February.
    7. Yang, Hongtai & Zhai, Guocong & Liu, Xiaohan & Yang, Linchuan & Liu, Yugang & Yuan, Quan, 2022. "Determinants of city-level private car ownership: Effect of vehicle regulation policies and the relative price," Transport Policy, Elsevier, vol. 115(C), pages 40-48.
    8. Cirillo, Cinzia & Bastin, Fabian & Hetrakul, Pratt, 2018. "Dynamic discrete choice model for railway ticket cancellation and exchange decisions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 110(C), pages 137-146.
    9. Urena Serulle, Nayel & Cirillo, Cinzia, 2017. "The optimal time to evacuate: A behavioral dynamic model on Louisiana resident data," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 447-463.

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