Long-term and mid-term mobility decision processes in different life trajectories generate complex dynamics, in which consecutive life events are interrelated and time dependent. This study uses the Bayesian network approach to study the dynamic relationships among residential events, household structure events, employment/education events, and car ownership events. Using retrospective data obtained from a web-based survey in Beijing, China, first structure learning is used to discover the direct and indirect relationships between these mobility decisions. Parameter learning is then applied to describe the conditional probabilities and predict the direct and indirect effects of actions and policies in the resulting network. The results confirm the interdependencies between these long-term and mid-term mobility decisions, and evidence the reactive and proactive behavior of individuals and households in the context of various life events over the course of their lives. In this regard, it is important to note that an increase in household size has a contemporaneous effect on car acquisition in the future; while residential events have a synergic relationship with employment/education events. Moreover, if people’s residential location or workplace/study location moves from an urban district to a suburban or outer suburban district, this has both lagged and concurrent effects on car acquisition.
|Number of pages||12|
|Journal||Transportation Research Record|
|Publication status||Published - 1 Dec 2018|
|Event||97th Annual Meeting of the Transportation Research Board - Washington DC, United States|
Duration: 7 Jan 2018 → 11 Jan 2018
Conference number: 97th