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Community structure in traffic zones based on travel demand

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  • Sun, Li
  • Ling, Ximan
  • He, Kun
  • Tan, Qian

Abstract

Large structure in complex networks can be studied by dividing it into communities or modules. Urban traffic system is one of the most critical infrastructures. It can be abstracted into a complex network composed of tightly connected groups. Here, we analyze community structure in urban traffic zones based on the community detection method in network science. Spectral algorithm using the eigenvectors of matrices is employed. Our empirical results indicate that the traffic communities are variant with the travel demand distribution, since in the morning the majority of the passengers are traveling from home to work and in the evening they are traveling a contrary direction. Meanwhile, the origin–destination pairs with large number of trips play a significant role in urban traffic network’s community division. The layout of traffic community in a city also depends on the residents’ trajectories.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:457:y:2016:i:c:p:356-363
    DOI: 10.1016/j.physa.2016.03.036
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    References listed on IDEAS

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    Cited by:

    1. He, Xijun & Dong, Yanbo & Wu, Yuying & Wei, Guodan & Xing, Lizhi & Yan, Jia, 2017. "Structure analysis and core community detection of embodied resources networks among regional industries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 137-150.
    2. 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).
    3. Lu, Feng & Liu, Kang & Duan, Yingying & Cheng, Shifen & Du, Fei, 2018. "Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 227-237.
    4. Zhang, Mengyao & Huang, Tao & Guo, Zhaoxia & He, Zhenggang, 2022. "Complex-network-based traffic network analysis and dynamics: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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