Abstract
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.
Highlights
With recent developments in ICT, various types of data are being generated and accumulated in real time
The procedure for extracting travel patterns proposed in this study comprises five steps—(1) construction of the co-occurrence graph, (2) construction of the similarity graph, (3) application of data polishing to the similarity graph, (4) enumeration of cliques, and (5) extraction of combinations of origin and destination stations related to each clique
We conclude that a combination comprising “Saturday × 23:00–23:59 hrs × Child” and “Public holiday × 24:00–24:59 hrs × Adult” cannot be extracted by any method other than the proposed one, which is capable of considering multiple attributes simultaneously
Summary
With recent developments in ICT, various types of data are being generated and accumulated in real time. In the field of transportation research, big data such as GPS data and probe vehicle data have been analyzed to understand behaviors of travelers [1–3]. Several researchers have analyzed smart card data to draw conclusions regarding the behavior of transit users [4–6]. Smart card data contain information about particular ticket gates at particular stations that were passed by passengers at particular times of the day as well as at their destination stations. They allow analysts to understand the temporal and spatial travel behaviors of smart card users
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
