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

Jia Guo, Tao Feng (Corresponding author), Harry J.P. Timmermans

Research output: Contribution to journalArticleAcademicpeer-review

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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.

Original languageEnglish
Pages (from-to)82-92
Number of pages11
JournalTransportation Research Part A: Policy and Practice
Publication statusPublished - 1 Dec 2019



  • Concurrent
  • Dynamic Bayesian network
  • Lagged and lead effects
  • Life events

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