2020
Guo, J.; Feng, T.; Zhang, J.; Timmermans, H. J. P.
Temporal interdependencies in mobility decisions over the life course: A household-level analysis using dynamic Bayesian networks Journal Article
In: Journal of Transport Geography, vol. 82, 2020, ISSN: 09666923.
Abstract | Links | BibTeX | Tags: Car ownership change, Household decision making mechanisms, Job change, Life course decisions, Residential change, Temporal dependencies
@article{Guo2020b,
title = {Temporal interdependencies in mobility decisions over the life course: A household-level analysis using dynamic Bayesian networks},
author = {J. Guo and T. Feng and J. Zhang and H. J. P. Timmermans},
doi = {10.1016/j.jtrangeo.2019.102589},
issn = {09666923},
year = {2020},
date = {2020-01-01},
journal = {Journal of Transport Geography},
volume = {82},
abstract = {Life trajectory analysis has been shown a powerful approach to understand the interdependencies between key life events, critical incidents and long-term mobility decisions such as residential move, job change and change in vehicle possession, which in turn constitute the context of daily activity-travel decisions. Because people in multi-earner households share resources, some of these long-term decisions affect them equally, while job change affects them differently because their job location likely differs. Current life course models in transportation research, however, have typically considered individuals' trajectories. To contribute to the further development of the relatively thin line of research in transportation studies, a dynamic Bayesian network approach is proposed to investigate the temporal interdependencies between life course events from a household perspective. Results show that the effects of child birth are much larger on residential and car ownership change than on job change for both household heads in dual-earner households. Moreover, the probability of residential and car ownership change increases when both spouses have relatively long commuting times. In case only the husband faces an excessive commuting time, households have a larger probability of moving house or purchasing an additional car. By contrast, in case only the wife faces an excessive commuting time, she is more likely to change job rather than the household taking particular actions to adjust to the problematic situation.},
keywords = {Car ownership change, Household decision making mechanisms, Job change, Life course decisions, Residential change, Temporal dependencies},
pubstate = {published},
tppubtype = {article}
}
Life trajectory analysis has been shown a powerful approach to understand the interdependencies between key life events, critical incidents and long-term mobility decisions such as residential move, job change and change in vehicle possession, which in turn constitute the context of daily activity-travel decisions. Because people in multi-earner households share resources, some of these long-term decisions affect them equally, while job change affects them differently because their job location likely differs. Current life course models in transportation research, however, have typically considered individuals’ trajectories. To contribute to the further development of the relatively thin line of research in transportation studies, a dynamic Bayesian network approach is proposed to investigate the temporal interdependencies between life course events from a household perspective. Results show that the effects of child birth are much larger on residential and car ownership change than on job change for both household heads in dual-earner households. Moreover, the probability of residential and car ownership change increases when both spouses have relatively long commuting times. In case only the husband faces an excessive commuting time, households have a larger probability of moving house or purchasing an additional car. By contrast, in case only the wife faces an excessive commuting time, she is more likely to change job rather than the household taking particular actions to adjust to the problematic situation.