Space–Time Sequential Similarity for Identifying Factors of Activity-Travel Pattern Segmentation: A Measure of Sequence Alignment and Path Similarity

The article develops a new method that compares activity-travel patterns in both terms of the sequential order of activities and the shape of activity-travel trajectory in time and space. The similarity of the list of activities and their order between activity-travel patterns are computed by a sequence alignment method. The shape of activity-travel trajectory is compared between the patterns using a path similarity technique that captures the direction and speed of a movement from the current location and the duration of staying at each location. The comparison results, therefore capture how people move around in three-dimensional space–time choreography that indicates how people conduct which activities in what order. A total of 1,000 individuals are sampled from the data of 2016 Household Travel Survey, South Korea. The data provide the information of individual activity-travel behavior and personal characteristics. The suggested method computes the pairwise distance matrix, and Ward clustering algorithm segments the pattern groups of similar activity sequences and space–time trajectories. A CHAID analysis then associates personal and household characteristics with the pattern groups to identify important factors for the segmentation. The analysis provides a significant implication in both terms of research and practice in transportation.