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Empirical study on a directed and weighted bus transport network in China

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Listed:
  • Feng, Shumin
  • Hu, Baoyu
  • Nie, Cen
  • Shen, Xianghao

Abstract

Bus transport networks are directed complex networks that consist of routes, stations, and passenger flow. In this study, the concept of duplication factor is introduced to analyze the differences between uplinks and downlinks for the bus transport network of Harbin (BTN-H). Further, a new representation model for BTNs is proposed, named as directed-space P. Two empirical characteristics of BTN-H are reported in this paper. First, the cumulative distributions of weighted degree, degree, number of routes that connect to each station, and node weight (peak-hour trips at a station) uniformly follow the exponential law. Meanwhile, the node weight shows positive correlations with the corresponding weighted degree, degree, and number of routes that connect to a station. Second, a new richness parameter of a node is explored by its node weight and the connectivity, weighted connectivity, average shortest path length and efficiency between rich nodes can be fitted by composite exponential functions to demonstrate the rich-club phenomenon.

Suggested Citation

  • Feng, Shumin & Hu, Baoyu & Nie, Cen & Shen, Xianghao, 2016. "Empirical study on a directed and weighted bus transport network in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 85-92.
    • Handle: RePEc:eee:phsmap:v:441:y:2016:i:c:p:85-92
    DOI: 10.1016/j.physa.2015.08.030
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    1. Soh, Harold & Lim, Sonja & Zhang, Tianyou & Fu, Xiuju & Lee, Gary Kee Khoon & Hung, Terence Gih Guang & Di, Pan & Prakasam, Silvester & Wong, Limsoon, 2010. "Weighted complex network analysis of travel routes on the Singapore public transportation system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5852-5863.
    2. C. von Ferber & T. Holovatch & Yu. Holovatch & V. Palchykov, 2009. "Public transport networks: empirical analysis and modeling," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 68(2), pages 261-275, March.
    3. von Ferber, C. & Holovatch, T. & Holovatch, Yu. & Palchykov, V., 2007. "Network harness: Metropolis public transport," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 585-591.
    4. Angeloudis, Panagiotis & Fisk, David, 2006. "Large subway systems as complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 367(C), pages 553-558.
    5. Xu, Xinping & Hu, Junhui & Liu, Feng & Liu, Lianshou, 2007. "Scaling and correlations in three bus-transport networks of China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(1), pages 441-448.
    6. Chen, Yong-Zhou & Li, Nan & He, Da-Ren, 2007. "A study on some urban bus transport networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 747-754.
    7. Yang, Xu-Hua & Chen, Guang & Chen, Sheng-Yong & Wang, Wan-Liang & Wang, Lei, 2014. "Study on some bus transport networks in China with considering spatial characteristics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 1-10.
    8. Zhang, Jianhua & Zhao, Mingwei & Liu, Haikuan & Xu, Xiaoming, 2013. "Networked characteristics of the urban rail transit networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1538-1546.
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    3. Hu, Baoyu & Feng, Shumin & Li, Jinyang & Zhao, Hu, 2018. "Statistical analysis of passenger-crowding in bus transport network of Harbin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 426-438.
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    6. Lixin Tian & Huan Chen & Zaili Zhen, 2018. "Research on the forward-looking behavior judgment of heating oil price evolution based on complex networks," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-18, September.

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