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Empirical analysis of urban road traffic network: A case study in Hangzhou city, China

Author

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  • Ruan, Zhongyuan
  • Song, Congcong
  • Yang, Xu-hua
  • Shen, Guojiang
  • Liu, Zhi

Abstract

Urban road traffic system is a time-evolving, directed weighted network in which both the topological structure and traffic flow should be considered. In this work, we collect the real-time traffic data of Xiaoshan district of Hangzhou city in China, to empirically study the properties of the traffic network. We show that the traffic patterns at different times during a day vary significantly. Specifically, at rush hours, more roads with low average velocity would emerge. Correspondingly, the average weight density at rush hours becomes smaller, while the variance increases, meaning that the traffic becomes more heterogeneous. By introducing a null model in which link weights are randomly shuffled, we find that the connected links are correlated, implying that the congested roads do not emerge at random in the network. Finally, we apply the percolation theory to study the influence of weather on the traffic network. We show that, on a rainy weekday, the traffic is more congested than that on a sunny weekday; while the result is opposite for weekends.

Suggested Citation

  • Ruan, Zhongyuan & Song, Congcong & Yang, Xu-hua & Shen, Guojiang & Liu, Zhi, 2019. "Empirical analysis of urban road traffic network: A case study in Hangzhou city, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119307575
    DOI: 10.1016/j.physa.2019.121287
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    Citations

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    Cited by:

    1. Yamada, Takashi, 2022. "Generalizing the probability of reaching a destination in case of route blockage," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    2. Meng, Yangyang & Zhao, Xiaofei & Liu, Jianzhong & Qi, Qingjie & Zhou, Wei, 2023. "Data-driven complexity analysis of weighted Shenzhen Metro network based on urban massive mobility in the rush hours," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    3. Chen, Yuting & Mao, Jiannan & Zhang, Zhao & Huang, Hao & Lu, Weike & Yan, Qipeng & Liu, Lan, 2022. "A quasi-contagion process modeling and characteristic analysis for real-world urban traffic network congestion patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    4. Meng, Yangyang & Tian, Xiangliang & Li, Zhongwen & Zhou, Wei & Zhou, Zhijie & Zhong, Maohua, 2020. "Exploring node importance evolution of weighted complex networks in urban rail transit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    5. 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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