Applying sequential alignment methods to large activity-travel data sets: exploration of a heuristic approach

Sequence alignment methods have recently found increasing application in transportation research for segmentation and classification analysis. Because these methods account for the sequential nature of activity-travel diary data, they are valuable for classifying activity-travel diary data. However, computation time requirements are overwhelming, implying that large-scale applications of these methods have not yet been possible. The performance and applicability of a heuristic approach was explored by using smart card data from Seoul, South Korea. Results were promising and suggest that the proposed heuristic approach could be used to perform segmentation analysis of large activity-travel data sets. Avenues for further improvement of the suggested method are discussed.