Technical Papers
May 20, 2024

Community Structure Division and Ridership Characteristics Analysis of Rail Transit Stations Based on the Louvain Algorithm

Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 8

Abstract

Expansion of the rail transit network has increased public transportation accessibility, and the spatial and temporal distribution characteristics of ridership have become more complex. Herein, the characteristics of rail transit origin-destination (OD) ridership distribution were analyzed using a drift power law distribution model, and the robustness of ridership distribution in three periods (weekdays, weekends, and holidays) was investigated. A Louvain algorithm-based community detection method divided the rail transit network into different community structures. This division was generally consistent with the established rail transit line layout. However, central city communities were more dispersed, and the rail transit stations of different lines may have belonged to the same community, revealing the spatial correlation between different stations from the perspective of OD ridership. The outcomes of this division were stable for the different periods. This study contributes to the precise configuration of transportation facilities around rail transit stations and the formulation of rail transit operation management strategies.

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Data Availability Statement

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

This research was funded by the Scientific Research Foundation for Advanced Talents of Nanjing Forestry University (Grant No. 163106041), the General Project of Philosophy and Social Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 2020SJA0125), and the General Program of Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 20KJB580013).

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Go to Journal of Transportation Engineering, Part A: Systems
Journal of Transportation Engineering, Part A: Systems
Volume 150Issue 8August 2024

History

Received: Jul 5, 2023
Accepted: Dec 5, 2023
Published online: May 20, 2024
Published in print: Aug 1, 2024
Discussion open until: Oct 20, 2024

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Zhenjun Zhu [email protected]
Associate Professor, College of Automobile and Traffic Engineering, Nanjing Forestry Univ., Nanjing 210037, China. Email: [email protected]
Graduate Student, College of Automobile and Traffic Engineering, Nanjing Forestry Univ., Nanjing 210037, China. Email: [email protected]
Jingrui Sun [email protected]
Graduate Student, College of Automobile and Traffic Engineering, Nanjing Forestry Univ., Nanjing 210037, China. Email: [email protected]
Shiyu Zhang [email protected]
Graduate Student, College Student, School of Artificial Intelligence, Nanjing Univ. of Information Science and Technology, Nanjing 210044, China. Email: [email protected]
Associate Professor, College of Automobile and Traffic Engineering, Nanjing Forestry Univ., Nanjing 210037, China (corresponding author). Email: [email protected]
Yunpeng Zhao [email protected]
Graduate Student, College of Automobile and Traffic Engineering, Nanjing Forestry Univ., Nanjing 210037, China. Email: [email protected]

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