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A simple contagion process describes spreading of traffic jams in urban networks

Author

Listed:
  • Meead Saberi

    (University of New South Wales (UNSW))

  • Homayoun Hamedmoghadam

    (Monash University)

  • Mudabber Ashfaq

    (University of New South Wales (UNSW))

  • Seyed Amir Hosseini

    (K.N. Toosi University of Technology)

  • Ziyuan Gu

    (University of New South Wales (UNSW))

  • Sajjad Shafiei

    (CSIRO)

  • Divya J. Nair

    (University of New South Wales (UNSW))

  • Vinayak Dixit

    (University of New South Wales (UNSW))

  • Lauren Gardner

    (Johns Hopkins University)

  • S. Travis Waller

    (University of New South Wales (UNSW))

  • Marta C. González

    (University of California)

Abstract

The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. In this study, we present a framework to describe the dynamics of congestion propagation and dissipation of traffic in cities using a simple contagion process, inspired by those used to model infectious disease spread in a population. We introduce two macroscopic characteristics for network traffic dynamics, namely congestion propagation rate β and congestion dissipation rate μ. We describe the dynamics of congestion spread using these new parameters embedded within a system of ordinary differential equations, similar to the well-known susceptible-infected-recovered (SIR) model. The proposed contagion-based dynamics are verified through an empirical multi-city analysis, and can be used to monitor, predict and control the fraction of congested links in the network over time.

Suggested Citation

  • Meead Saberi & Homayoun Hamedmoghadam & Mudabber Ashfaq & Seyed Amir Hosseini & Ziyuan Gu & Sajjad Shafiei & Divya J. Nair & Vinayak Dixit & Lauren Gardner & S. Travis Waller & Marta C. González, 2020. "A simple contagion process describes spreading of traffic jams in urban networks," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15353-2
    DOI: 10.1038/s41467-020-15353-2
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    Cited by:

    1. Wang, Jiawen & Zou, Linzhi & Zhao, Jing & Wang, Xinwei, 2024. "Dynamic capacity drop propagation in incident-affected networks: Traffic state modeling with SIS-CTM," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    2. Zeng, Jie & Xiong, Yong & Liu, Feiyang & Ye, Junqing & Tang, Jinjun, 2022. "Uncovering the spatiotemporal patterns of traffic congestion from large-scale trajectory data: A complex network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    3. Amitrajeet A. Batabyal & Hamid Beladi, 2022. "Commuting to work in cities: Bus, car, or train?," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(3), pages 599-609, June.
    4. Zhang, Xin & Huang, Ning & Sun, Lina & Zheng, Xiangyu & Guo, Ziyue, 2022. "Modeling congestion considering sequential coupling applications: A network-cell-based method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    5. Zheng, Xiangyu & Huang, Ning & Bai, Ya-nan & Zhang, Xin, 2023. "A traffic-fractal-element-based congestion model considering the uneven distribution of road traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    6. Jinxiao Duan & Guanwen Zeng & Nimrod Serok & Daqing Li & Efrat Blumenfeld Lieberthal & Hai-Jun Huang & Shlomo Havlin, 2023. "Spatiotemporal dynamics of traffic bottlenecks yields an early signal of heavy congestions," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    7. 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).
    8. Xu, Zhihao & Lv, Zhiqiang & Chu, Benjia & Li, Jianbo, 2024. "A Fast Spatial-temporal Information Compression algorithm for online real-time forecasting of traffic flow with complex nonlinear patterns," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    9. Wang, Shanshan & Schreckenberg, Michael & Guhr, Thomas, 2023. "Response functions as a new concept to study local dynamics in traffic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    10. 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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