Inferring temporal motifs for travel pattern analysis using large scale smart card data

In this paper, we proposed a new method to extract travel patterns for transit riders from different public transportation systems based on temporal motif, which is an emerging notion in network science literature. We then developed a scalable algorithm to recognize temporal motifs from daily trip sub-sequences extracted from two smart card datasets. Our method shows its benefits in uncovering the potential correlation between varying topologies of trip combinations and specific activity chains. Commuting, different types of transfer, and other travel behaviors have been identified. Besides, varying travel-activity chains like "Home -> Work -> Post-work activity (for dining or shopping)-> Back home" and the corresponding travel motifs have been distinguished by incorporating the land use information in the GIS data. The analysis results contribute to our understanding of transit riders' travel behavior. We also present application examples of the travel motif to demonstrate the practicality of the proposed approach. Our methodology can be adapted to travel pattern analysis using different data sources and lay the foundation for other travel-pattern related studies.