Longitudinal analysis of activity and travel pattern dynamics using generalized mixed Markov latent class models

Understanding the dynamics of time allocation by households and their household members is becoming increasingly important for travel demand forecasting. A unique opportunity to understand day-to-day and year-to-year behavioral change, is provided by data from multi-day travel diaries combined with yearly observation of the same individuals over time (panel surveys). In fact, the "repeated" nature of the data allows to distinguish units that over time change their behavior from those that are not and to uncover the underlying stochastic behavior generating the data. In this paper data from the Puget Sound Transportation Panel (PSTP) are analyzed to identify change in the patterns of time allocation by the panel participants (i.e., patterns of activity participation and travel). The data analyzed are sequences of states in categorical data from reported individuals' daily activity participation and travel indicators. This is done separately for activity participation and trip making using probabilistic models that generalize the restrictive Markov chain models by incorporating unobserved variables of change. The PSTP data analysis here suggests the more likely presence of multiple paths of change for time allocation to activities, non-stationary switching of activity participation from one year to the next, and day-to-day stationarity in activity participation pattern switching. Travel pattern change is best explained by a single path of change with stationary day-to-day pattern transition probabilities that are different from their year-to-year counterparts.