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

Jia Guo; Tao Feng; Harry J.P. Timmermans

doi:10.1016/j.tra.2019.09.008

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

Urban Planning and Transportation

Research output: Contribution to journal › Article › Academic › peer-review

1 Citation (Scopus)

2 Downloads (Pure)

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.

Original language	English
Pages (from-to)	82-92
Number of pages	11
Journal	Transportation Research Part A: Policy and Practice
Volume	130
DOIs	https://doi.org/10.1016/j.tra.2019.09.008
Publication status	Published - 1 Dec 2019

Keywords

Concurrent
Dynamic Bayesian network
Lagged and lead effects
Life events

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10.1016/j.tra.2019.09.008

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Time-varying dependencies among mobility decisions and key life course events : an application of dynamic Bayesian decision networks. / Guo, Jia ; Feng, Tao (Corresponding author); Timmermans, Harry J.P.

In: Transportation Research Part A: Policy and Practice, Vol. 130, 01.12.2019, p. 82-92.

Research output: Contribution to journal › Article › Academic › peer-review

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