  Over the last few years, traffic data has been exploding and the
transportation discipline has entered the era of big data. It brings out new
opportunities for doing data-driven analysis, but it also challenges
traditional analytic methods. This paper proposes a new Divide and Combine
based approach to do K means clustering on activity-travel behavior time series
using features that are derived using tools in Time Series Analysis and
Topological Data Analysis. Clustering data from five waves of the National
Household Travel Survey ranging from 1990 to 2017 suggests that activity-travel
patterns of individuals over the last three decades can be grouped into three
clusters. Results also provide evidence in support of recent claims about
differences in activity-travel patterns of different survey cohorts. The
proposed method is generally applicable and is not limited only to
activity-travel behavior analysis in transportation studies. Driving behavior,
travel mode choice, household vehicle ownership, when being characterized as
categorical time series, can all be analyzed using the proposed method.
