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[67] Railway Station Clustering based on Origin-Destination Patterns using Graph Polishing

○Mio Hosoe¹, Masashi Kuwano¹, Taku Moriyama¹ (1. Tottori University)

Keywords:Traffic Big Data, Smart Card Data, Origin-Destination Table, Similarity

With the development of ICT, interest in traffic policy planning by utilizing large varieties of accumulated big data has been increasing. In recent years, graph polishing has been proposed as a new methodology for graph clustering. Graph polishing is one of the graph clustering methods. This method can be used to extract groups that are similar or related to each other by clarifying the cluster structures in the data. This study classifies railway stations by applying the graph polishing to smart card data that has been introduced in Kagawa Prefecture, Japan. This study uses 9,008,709 data collected during the 15 months from December 1st, 2013 to February 28th, 2015, and prepares Origin-Destination network. Then, this study clarifies station groups and examines the usefulness of graph polishing to Origin-Destination network clustering.