Abstract
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.
Highlights
The identification of urban structure is a topic that has long been studied by urban geographers and planners [1,2,3,4]
In order to obtain a better understanding of the urban spatial structure, researchers have been increasingly scrutinizing urban mobility dynamics and their impact on urban environments, since the pattern of how people move about a city is closely related to urban spatial structures [6,7,8,9,10]
This study aimed to identify the travel patterns from transit smart-card data and its association with local environmental characteristics
Summary
The identification of urban structure is a topic that has long been studied by urban geographers and planners [1,2,3,4]. It is important to measure urban structures and identify the underlying activity pattern for the sake of supporting an evidence-based urban planning policy. Identifying activity centers, clusters and their characteristics gives urban planners a better understanding of the current structure of a city and allows them to assess how their planning is being reflected [5]. In order to obtain a better understanding of the urban spatial structure, researchers have been increasingly scrutinizing urban mobility dynamics and their impact on urban environments, since the pattern of how people move about a city is closely related to urban spatial structures [6,7,8,9,10]. With the advances in technology, finer-resolution geospatial data have become available for modeling urban structures and dynamics [11,12]
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