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Study on the topology and dynamics of the rail transit network based on automatic fare collection data

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  • Lin, Pengfei
  • Weng, Jiancheng
  • Fu, Yu
  • Alivanistos, Dimitrios
  • Yin, Baocai

Abstract

Studying the topology of a rail transit network based on complex network theory is of great significance for identifying the weak links of the network and improving the network’s accessibility and connectivity. Transfer stations play important roles in the network. This study investigates the dynamic properties of the transfer network of the Beijing rail transit system considering the passenger flow assignment. The P-space representation and automatic fare collection data were employed to build a directed and weighted network. The improved degree centrality, closeness centrality, betweenness centrality and the PageRank index were used to distinguish the key stations and sections of the network. A community detection method, the Infomap algorithm, was applied to partition the network into some subnetworks according to the dynamics of passenger flow. The results showed that a large majority of the important stations are in large residential areas or commercial office areas, located at the intersections of the radial lines with the loop lines. According to the travel demand of passengers, the network was divided into 8 communities. The majority of the trips start and end in the same community. This study could help effectively identify the important stations and sections as well as the community structures generated by passenger mobility.

Suggested Citation

  • Lin, Pengfei & Weng, Jiancheng & Fu, Yu & Alivanistos, Dimitrios & Yin, Baocai, 2020. "Study on the topology and dynamics of the rail transit network based on automatic fare collection data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    • Handle: RePEc:eee:phsmap:v:545:y:2020:i:c:s0378437119319727
    DOI: 10.1016/j.physa.2019.123538
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    1. Zhang, Jianhua & Wang, Shuliang & Zhang, Zhaojun & Zou, Kuansheng & Shu, Zhan, 2016. "Characteristics on hub networks of urban rail transit networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 502-507.
    2. Sun, Daniel (Jian) & Guan, Shituo, 2016. "Measuring vulnerability of urban metro network from line operation perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 348-359.
    3. Sun, Li & Ling, Ximan & He, Kun & Tan, Qian, 2016. "Community structure in traffic zones based on travel demand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 356-363.
    4. Akbarzadeh, Meisam & Salehi Reihani, Sayed Farzin & Samani, Keivan Aghababaei, 2019. "Detecting critical links of urban networks using cluster detection methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 288-298.
    5. Oded Cats & Erik Jenelius, 2014. "Dynamic Vulnerability Analysis of Public Transport Networks: Mitigation Effects of Real-Time Information," Networks and Spatial Economics, Springer, vol. 14(3), pages 435-463, December.
    6. Sybil Derrible & Christopher Kennedy, 2010. "Characterizing metro networks: state, form, and structure," Transportation, Springer, vol. 37(2), pages 275-297, March.
    7. Sun, Lishan & Huang, Yuchen & Chen, Yanyan & Yao, Liya, 2018. "Vulnerability assessment of urban rail transit based on multi-static weighted method in Beijing, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 108(C), pages 12-24.
    8. Li, W. & Cai, X., 2007. "Empirical analysis of a scale-free railway network in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(2), pages 693-703.
    9. Raveau, Sebastián & Muñoz, Juan Carlos & de Grange, Louis, 2011. "A topological route choice model for metro," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 138-147, February.
    10. Feng, Jia & Li, Xiamiao & Mao, Baohua & Xu, Qi & Bai, Yun, 2017. "Weighted complex network analysis of the Beijing subway system: Train and passenger flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 213-223.
    11. Liu, Xi & Gong, Li & Gong, Yongxi & Liu, Yu, 2015. "Revealing travel patterns and city structure with taxi trip data," Journal of Transport Geography, Elsevier, vol. 43(C), pages 78-90.
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