Citation

Guo J, Feng T, Timmermans HJP. Transp. Res. A Policy Pract. 2019; 130: 82-92.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.tra.2019.09.008

PMID

unavailable

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.

Language: en

Keywords

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