With the incorporation of an automated fare-collection system into the management of public transportation, not only can the quality of transportation services be improved but also that of the data collected from users when coupled with smart-card technology. The data collected from smart cards provide opportunities for researchers to analyze big data sets and draw meaningful information out of them. This study aims to identify the relationship between travel patterns derived from smart-card data and urban characteristics. Using seven-day transit smart-card data from the public-transportation system in Seoul, the capital city of the Republic of Korea, we investigated the temporal and spatial boarding and alighting patterns of the users. The major travel patterns, classified into five clusters, were identified by utilizing the K-Spectral Centroid clustering method. We found that the temporal pattern of urban mobility reflects daily activities in the urban area and that the spatial pattern of the five clusters classified by travel patterns was closely related to urban structure and urban function; that is, local environmental characteristics extracted from land-use and census data. This study confirmed that the travel patterns at the citywide level can be used to understand the dynamics of the urban population and the urban spatial structure. We believe that this study will provide valuable information about general patterns, which represent the possibility of finding travel patterns from individuals and urban spatial traits.
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Acknowledgments: We would like to thank the Transport Policy Division of the Seoul Metropolitan Government and related organizations for their help in making available various data including smart-card records and other information. This work was partially supported by a grant [MOIS-DP-2015-10] through the Disaster and Safety Management Institute funded by Ministry of the Interior and Safety of Korean government. The authors would also like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.
All Science Journal Classification (ASJC) codes
- Geography, Planning and Development
- Renewable Energy, Sustainability and the Environment
- Management, Monitoring, Policy and Law