Over the last few years, as with many other fields, the transportation discipline has been swept by the big data revolution. This revolution has not only brought about tremendous opportunities for conducting interesting data-driven analysis, it has also highlighted challenges associated with using traditional analytical methods to analyze these large datasets. To this end, this paper proposes a new Divide and Combine-based approach to estimating Mixture Markov models for analyzing large categorical time series data. The validity of this approach is demonstrated using a simulation study. Further, the feasibility and applicability is highlighted by conducting a clustering analysis of large activity–travel sequences using multiyear travel survey datasets. In the case study, each individual’s daily activity–travel behavior is characterized as a categorical time series that attempts to capture multiple aspects of travel and activity engagement simultaneously while also incorporating the timing and the schedule of different episodes. The proposed Divide and Combine-based Mixture Markov models are then used to cluster the large data. Subsequently, cluster compositions are explored to understand within and between-cluster differences and their associations with generational cohort factors, socioeconomic attributes, and demographic variables. As a preliminary exploration, the results suggest that travel patterns of individuals over the last three decades can be categorized into three types of travel patterns. Results also provide evidence in support of recent claims about different generational cohorts and their activity–travel behaviors.
Nested-error regression models and small area estimation combining cross-sectional and time series data.