Abstract
Public transportation in big cities is a crucial part of urban transportation infrastructures. Exploring the spatiotemporal patterns of public trips can help us to understand dynamic transportation patterns and the complex urban systems thus supporting better urban planning and design. The availability of large-scale smart card data (SCD) offers new opportunities to study intra-urban structure and spatial interaction dynamics. In this research, we applied the novel community detection methods from the study of complex networks to examine the dynamic spatial interaction structures of public transportation communities in the Beijing Metropolitan Area. It can help to find the ground-truth community structure of strongly connected traffic analysis zones by public transportation, which may yield insights for urban planners on land use patterns or for transportation engineers on traffic congestion. We also found that the daily community detection results using SCD are different from that using household travel surveys. The SCD results match better with the planned urban area boundary, which means that the actual operation data of publication transportation might be a good source to validate the urban planning and development.
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Long, Y., Shen, Z. (2015). Finding Public Transportation Community Structure Based on Large-Scale Smart Card Records in Beijing. In: Geospatial Analysis to Support Urban Planning in Beijing. GeoJournal Library, vol 116. Springer, Cham. https://doi.org/10.1007/978-3-319-19342-7_8
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DOI: https://doi.org/10.1007/978-3-319-19342-7_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19341-0
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