An empirical analysis of public transit networks using smart card data in Beijing, China

Abstract

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.

Keywords:

• Smart card data
• public transport systems
• network centrality
• correlation analysis
• passenger flow

Acknowledgements

The authors thank Dr. LAI, Jianhui for processing the public transit big data used in this study, and the anonymous reviewers, who helped to improve this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Author 2 acknowledges funding support from a start-up project (1-ZE6P) funded by The Hong Kong Polytechnic University.