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Time-varying dependencies among mobility decisions and key life course events: An application of dynamic Bayesian decision networks

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  • Guo, Jia
  • Feng, Tao
  • Timmermans, Harry J.P.

Abstract

People’s long-term mobility decisions depend on their current situation, past history and/or future plans. Consequently, models of long-term mobility decisions should take lagged, concurrent and/or lead effects into account. Contributing to the literature on long-term mobility analysis, this study develops an integrated framework for modeling the temporally interdependent choices related to residential change, job change and car purchasing decisions. Using retrospective life trajectory data collected through a Web-based survey, a dynamic Bayesian network model is estimated. Results show that different life domains are highly interdependent. Concurrent, as well as lagged and lead effects are observed.

Suggested Citation

  • Guo, Jia & Feng, Tao & Timmermans, Harry J.P., 2019. "Time-varying dependencies among mobility decisions and key life course events: An application of dynamic Bayesian decision networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 82-92.
    • Handle: RePEc:eee:transa:v:130:y:2019:i:c:p:82-92
    DOI: 10.1016/j.tra.2019.09.008
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    References listed on IDEAS

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    Cited by:

    1. Scheiner, Joachim, 2020. "Changes in travel mode use over the life course with partner interactions in couple households," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 791-807.
    2. Gaofeng Gu & Tao Feng & Chixing Zhong & Xiaoxi Cai & Jiang Li, 2021. "The Effects of Life Course Events on Car Ownership and Sustainable Mobility Tools Adoption Decisions: Results of an Error Component Random Parameter Logit Model," Sustainability, MDPI, vol. 13(12), pages 1-21, June.

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