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Empirical analysis of dependence between stations in Chinese railway network

Author

Listed:
  • Wang, Yong-Li
  • Zhou, Tao
  • Shi, Jian-Jun
  • Wang, Jian
  • He, Da-Ren

Abstract

A railway transportation system can be represented by a bipartite network consisting of trains and stations, where a train is connected to all stations where it stops. In this paper, motivated by the resource-allocation process taking place on networks, we design a method to project a Chinese train-station bipartite network into a weighted station network. A new metric is proposed to quantify the dependence between pairs of stations, which is shown to follow a shifted power-law distribution. In addition, we compare the resource-allocation method and the well-known multiple-edge method, and the results indicate that our proposed method is more reasonable.

Suggested Citation

  • Wang, Yong-Li & Zhou, Tao & Shi, Jian-Jun & Wang, Jian & He, Da-Ren, 2009. "Empirical analysis of dependence between stations in Chinese railway network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2949-2955.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:14:p:2949-2955
    DOI: 10.1016/j.physa.2009.03.026
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    Citations

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    Cited by:

    1. Yasir Tariq Mohmand & Aihu Wang, 2014. "Complex Network Analysis of Pakistan Railways," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-5, March.
    2. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    3. Zhang, Jianhua & Hu, Funian & Wang, Shuliang & Dai, Yang & Wang, Yixing, 2016. "Structural vulnerability and intervention of high speed railway networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 743-751.
    4. Liu, Chengliang & Duan, Dezhong, 2020. "Spatial inequality of bus transit dependence on urban streets and its relationships with socioeconomic intensities: A tale of two megacities in China," Journal of Transport Geography, Elsevier, vol. 86(C).
    5. Yang, Xu-Hua & Chen, Guang & Sun, Bao & Chen, Sheng-Yong & Wang, Wan-Liang, 2011. "Bus transport network model with ideal n-depth clique network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4660-4672.
    6. Huo, Jie & Wang, Xu-Ming & Zhao, Ning & Hao, Rui, 2016. "Statistical characteristics of dynamics for population migration driven by the economic interests," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 123-134.
    7. Zhang, Chuanzhe & Pang, Shaopeng & Yu, Hao & Han, Guozheng, 2021. "A fund-stock network projection model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    8. Wang, Ximeng & Liu, Yun & Xiong, Fei, 2016. "Improved personalized recommendation based on a similarity network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 271-280.
    9. Yan, Deng-Cheng & Li, Ming & Wang, Bing-Hong, 2017. "Dependence centrality similarity: Measuring the diversity of profession levels of interests," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 118-127.
    10. Feng, Ai-Xia & Fu, Chun-Hua & Xu, Xiu-Lian & Zhou, Yue-Ping & Chang, Hui & Wang, Jian & He, Da-Ren & Feng, Guo-Lin, 2012. "An extended clique degree distribution and its heterogeneity in cooperation–competition networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2454-2462.

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