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Urban Transit Network Properties Evaluation and Optimization Based on Complex Network Theory

Author

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  • Guo-Ling Jia

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Rong-Guo Ma

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Zhi-Hua Hu

    (Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China)

Abstract

Urban public transportation contributes greatly to sustainable urban development. An urban public transportation network is a complex system. It is meaningful for theory and practice to analyze the topological structure of an urban public transportation network and explore the spatial structure of an urban transportation network so as to mitigate and prevent traffic congestion and achieve sustainability. By examining the Xi’an bus network, the degree distribution, average path length, aggregation coefficient, and betweenness centrality of the bus station network were computed using models in complex network theory. The results show that the node degrees of the Xi’an bus network are unevenly distributed and present a polarization diagram with long average path length and high aggregation. A model based on betweenness and its solution method was developed to improve the public transportation network’s sustainability and discuss the possibility of optimizing the sustainability by network analyzing methods.

Suggested Citation

  • Guo-Ling Jia & Rong-Guo Ma & Zhi-Hua Hu, 2019. "Urban Transit Network Properties Evaluation and Optimization Based on Complex Network Theory," Sustainability, MDPI, vol. 11(7), pages 1-16, April.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:7:p:2007-:d:219961
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    References listed on IDEAS

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    1. Farahani, Reza Zanjirani & Miandoabchi, Elnaz & Szeto, W.Y. & Rashidi, Hannaneh, 2013. "A review of urban transportation network design problems," European Journal of Operational Research, Elsevier, vol. 229(2), pages 281-302.
    2. Latora, Vito & Marchiori, Massimo, 2002. "Is the Boston subway a small-world network?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 314(1), pages 109-113.
    3. Pternea, Moschoula & Kepaptsoglou, Konstantinos & Karlaftis, Matthew G., 2015. "Sustainable urban transit network design," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 276-291.
    4. Seaton, Katherine A. & Hackett, Lisa M., 2004. "Stations, trains and small-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 339(3), pages 635-644.
    5. Fan Xiao & Zhi-Hua Hu & Ke-Xin Wang & Pei-Hua Fu, 2015. "Spatial Distribution of Energy Consumption and Carbon Emission of Regional Logistics," Sustainability, MDPI, vol. 7(7), pages 1-20, July.
    6. Dou, Yijie & Zhu, Qinghua & Sarkis, Joseph, 2014. "Evaluating green supplier development programs with a grey-analytical network process-based methodology," European Journal of Operational Research, Elsevier, vol. 233(2), pages 420-431.
    7. Ibarra-Rojas, O.J. & Delgado, F. & Giesen, R. & Muñoz, J.C., 2015. "Planning, operation, and control of bus transport systems: A literature review," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 38-75.
    8. Bagler, Ganesh & Sinha, Somdatta, 2005. "Network properties of protein structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(1), pages 27-33.
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    Cited by:

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