Journal
GEOCARTO INTERNATIONAL
Volume 37, Issue 4, Pages 1203-1223Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/10106049.2020.1768594
Keywords
Smart card data; public transport systems; network centrality; correlation analysis; passenger flow
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Funding
- Hong Kong Polytechnic University [1-ZE6P]
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This study utilizes real smart card data to dynamically explore and evaluate the structure of large-scale public transit networks in Beijing, China. The overall network structure of these networks is found to follow heavy-tailed distributions. The modified centrality measures outperform the traditional centrality measures in estimating passenger flow.
Most existing studies on public transit network (PTN) rely on either small-scale passenger flow data or small PTN, and only traditional network parameters are used to calculate the correlation coefficient. In this work, the real smart card data (SCD) (when passenger tap in and tap out a station) of over eight million users is used as a proxy of passenger flow to dynamically explore and evaluate the structure of large-scale PTNs with tens of thousands of stations in Beijing, China. Three types of large-scale PTNs are generated, and the overall network structure of PTNs are examined and found to follow heavy-tailed distributions (mostly power law). Further, three traditional centrality measures (i.e. degree, betweenness and closeness) are adopted and modified to dynamically explore the relationship between PTNs and passenger flow. Our findings show that, the modified centrality measures outperform the traditional centrality measures in estimating passenger flow.
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