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Congestion And Centrality In Traffic Flow On Complex Networks

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  • PETTER HOLME

    (Department of Physics, Umeå University, 901 87 Umeå, Sweden)

Abstract

The central points of communication network flow have often been identified using graph theoretical centrality measures. In real networks, the state of traffic density arises from an interplay between the dynamics of the flow and the underlying network structure. In this work we investigate the relationship between centrality measures and the density of traffic for some simple particle hopping models on networks with emerging scale-free degree distributions. We also study how the speed of the dynamics are affected by the underlying network structure. Among other conclusions, we find that, even at low traffic densities, the dynamical measure of traffic density (the occupation ratio) has a non-trivial dependence on the static centrality (quantified by "betweenness centrality"), where non-central vertices get a comparatively large portion of the traffic.

Suggested Citation

  • Petter Holme, 2003. "Congestion And Centrality In Traffic Flow On Complex Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 6(02), pages 163-176.
    • Handle: RePEc:wsi:acsxxx:v:06:y:2003:i:02:n:s0219525903000803
    DOI: 10.1142/S0219525903000803
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    Cited by:

    1. Batac, Rene C. & Cirunay, Michelle T., 2022. "Shortest paths along urban road network peripheries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    2. Wang, Weiping & Yang, Saini & Hu, Fuyu & Stanley, H. Eugene & He, Shuai & Shi, Mimi, 2018. "An approach for cascading effects within critical infrastructure systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 164-177.
    3. Wang, Yuhong & Cullinane, Kevin, 2016. "Determinants of port centrality in maritime container transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 95(C), pages 326-340.
    4. Rui Ding & Norsidah Ujang & Hussain Bin Hamid & Mohd Shahrudin Abd Manan & Rong Li & Safwan Subhi Mousa Albadareen & Ashkan Nochian & Jianjun Wu, 2019. "Application of Complex Networks Theory in Urban Traffic Network Researches," Networks and Spatial Economics, Springer, vol. 19(4), pages 1281-1317, December.
    5. Wang, Zi-Yi & Han, Jing-Ti & Zhao, Jun, 2017. "Identifying node spreading influence for tunable clustering coefficient networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 242-250.
    6. Ribas, Lucas C. & Bruno, Odemir M., 2020. "Dynamic texture analysis using networks generated by deterministic partially self-avoiding walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    7. Cirunay, Michelle T. & Batac, Rene C., 2023. "Evolution of the periphery of a self-organized road network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
    8. Wang, Duo & Sipahi, Rifat, 2024. "Betweenness centrality can inform stability and delay margin in a large-scale connected vehicle system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 636(C).
    9. Bilong Shen & Weimin Zheng & Kathleen M. Carley, 2018. "Urban Activity Mining Framework for Ride Sharing Systems Based on Vehicular Social Networks," Networks and Spatial Economics, Springer, vol. 18(3), pages 705-734, September.
    10. Angelo Furno & Nour-Eddin El Faouzi & Rajesh Sharma & Eugenio Zimeo, 2021. "Graph-based ahead monitoring of vulnerabilities in large dynamic transportation networks," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-35, March.
    11. Rui Ding & Jian Yin & Peng Dai & Lu Jiao & Rong Li & Tongfei Li & Jianjun Wu, 2019. "Optimal Topology of Multilayer Urban Traffic Networks," Complexity, Hindawi, vol. 2019, pages 1-19, October.
    12. Wang, Shao-Ping & Pei, Wen-Jiang, 2008. "First passage time of multiple Brownian particles on networks with applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(18), pages 4699-4708.
    13. Wen, Tzai-Hung & Chin, Wei-Chien-Benny & Lai, Pei-Chun, 2017. "Understanding the topological characteristics and flow complexity of urban traffic congestion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 166-177.
    14. Tam, Wai M. & Lau, Francis C.M. & Tse, Chi K. & Xia, Yongxiang & Shan, Xiuming, 2006. "Effect of clustering in a complex user network on the telephone traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 745-753.
    15. Ghosh, Saptarshi & Banerjee, Avishek & Ganguly, Niloy, 2012. "Some insights on the recent spate of accidents in Indian Railways," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(9), pages 2917-2929.
    16. Xiangyang Cao & Bingzhong Zhou & Qiang Tang & Jiaqi Li & Donghui Shi, 2018. "Urban Wasteful Transport and Its Estimation Methods," Sustainability, MDPI, vol. 10(12), pages 1-15, December.
    17. Perez, Yuri & Pereira, Fabio Henrique, 2021. "Simulation of traffic light disruptions in street networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).

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