Hidden Markov Approach to Dynamically Modeling Car Ownership Behavior

It has become apparent to researchers in various domains that choice behavior occurs in a dynamic context and decision making involves strong temporal dependency, especially when it comes to car ownership decisions, because of consumers' forward-looking behavior. However, a substantial portion of the literature focuses on static model formulations, and limitations exist, particularly in long-term travel demand forecasting. This study proposed a hidden Markov modeling (HMM) framework to analyze car ownership behavior dynamically. The dynamic model framework was applied to the 10-wave Puget Sound (Washington) Transportation Panel data. Two hidden states were identified in this study: State 1 tended to be land use entropy sensitive and vice versa for State 2. Empirical results reveal that households with preschool-age children are more likely to live in urbanized areas where they have easy access to various facilities. Also, one more licensed driver would lead to a 13.33% increase in owning two cars for State 1 households and a 28.45% increase in owning three or more cars for State 2 households. The comparison with both the multinomial logit model and the latent class model favors the study's dynamic model framework with respect to model performance. The HMM approach offers insights on policy development for a target population and provides more accurate forecasting for long-term planning and policy analysis.