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With recent developments in ICT, the interest in using large amounts of accumulated data for traffic policy planning has increased significantly. In recent years, data polishing has been proposed as a new method of big data analysis. Data polishing is a graphical clustering method, which can be used to extract patterns that are similar or related to each other by identifying the cluster structures present in the data. The purpose of this study is to identify the travel patterns of railway passengers by applying data polishing to smart card data collected in the Kagawa Prefecture, Japan. To this end, we consider 9,008,709 data points collected over a period of 15 months, ranging from December 1st, 2013 to February 28th, 2015. This dataset includes various types of information, including trip histories and types of passengers. This study implements data polishing to cluster 4,667,520 combinations of information regarding individual rides in terms of the day of the week, the time of the day, passenger types, and origin and destination stations. Via the analysis, 127 characteristic travel patterns are identified in aggregate.

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 creates Origin-Destination network. Then, this study clarifies station groups and examines the usefulness of graph polishing to Origin-Destination network clustering.

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