Triggers of behavioral change: Longitudinal analysis of travel behavior, household composition and spatial characteristics of the residence

- Mixed Markov Latent Class identifies triggers of behavior change.
- Change in business diversity around residences triggers behavioral change.
- Increases in workers and adults in households lead to higher mobility.
- Changes of number of children in young households trigger behavioral change.

Longitudinal data have the potential to reveal the causal mechanism underlying changes in observed behavior, can protect us from finding spurious relationships, study time precedence, and offer strength in helping discover association among variables. In this paper, we expand our previous analysis of the only longitudinal travel behavior database in the US that has ten waves (observation time points). The analysis here is using data from the two-day Puget Sound Transportation Panel (PSTP) travel diary and 230 households participating in all ten waves of the panel. The main objective of this analysis is to identify what triggers behavioral change in activity and travel and to quantify and compare the size of these triggering effects using different methods. Our search for triggers includes the birth of a child, child leaving the household, and the entry or exit from the labor force of a household member. In the land use changes we identify moves from high accessibility and diverse environments to lower accessibility and less diverse places and find that diversity is key in changing behavior. The method used to analyze the data is a longitudinal Mixed Markov Latent Class analysis that allows to not only account for behavioral heterogeneity at each time of observation but to also test for the existence of multiple latent trajectories of change and the role played by triggers in shaping these trajectories.