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.

随着ICT的最新发展，使用大量累积数据进行交通政策规划的兴趣大大增加。近年来，数据抛光已被提出作为大数据分析的一种新方法。数据抛光是一种图形聚类方法，可用于通过识别数据中存在的聚类结构来提取彼此相似或相关的模式。这项研究的目的是通过对日本香川县收集的智能卡数据进行数据抛光来识别铁路乘客的出行方式。为此，我们考虑了从2013年12月1日到2015年2月28日的15个月内收集的9,008,709个数据点。该数据集包含各种信息，包括旅行历史和乘客类型。该研究对星期四，一天中的时间，乘客类型以及始发站和目的地站的各个游乐设施的信息组合进行聚类，从而对4,667,520个信息组合进行聚类。通过分析，总共确定了127种特征性旅行模式。