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Resilient perimeter control of macroscopic fundamental diagram networks under cyberattacks

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  • Haddad, Jack
  • Mirkin, Boris

Abstract

The perimeter control concept based on macroscopic fundamental diagram (MFD) modeling is now well-established in various literature. Recent research efforts are devoted to develop perimeter control schemes, which can be eventually applied to urban traffic control systems. Modern urban traffic control systems increasingly rely on information technology infrastructure, combining various network and physical facilities and use of communication technologies, which increases the vulnerability for cyberattacks. As witnessed in recent real-life events, modern urban traffic control systems are vulnerable and not protected from internal and external, malicious and accidental threats. Hence, applicable perimeter control algorithms should be robust not only against dynamic uncertainties, but in addition, it should be resilient against cyberattack issues for real future implementations.

Suggested Citation

  • Haddad, Jack & Mirkin, Boris, 2020. "Resilient perimeter control of macroscopic fundamental diagram networks under cyberattacks," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 44-59.
    • Handle: RePEc:eee:transb:v:132:y:2020:i:c:p:44-59
    DOI: 10.1016/j.trb.2019.01.020
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    References listed on IDEAS

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    1. Laval, Jorge A. & Castrillón, Felipe, 2015. "Stochastic approximations for the macroscopic fundamental diagram of urban networks," Transportation Research Part B: Methodological, Elsevier, vol. 81(P3), pages 904-916.
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    Cited by:

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    2. Gao, Shengling & Li, Daqing & Zheng, Nan & Hu, Ruiqi & She, Zhikun, 2022. "Resilient perimeter control for hyper-congested two-region networks with MFD dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 156(C), pages 50-75.
    3. Zheng, Liang & Bao, Ji & Xu, Chengcheng & Tan, Zhen, 2022. "Biobjective robust simulation-based optimization for unconstrained problems," European Journal of Operational Research, Elsevier, vol. 299(1), pages 249-262.
    4. Su, Z.C. & Chow, Andy H.F. & Fang, C.L. & Liang, E.M. & Zhong, R.X., 2023. "Hierarchical control for stochastic network traffic with reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 196-216.
    5. Guo, Yajuan & Yang, Licai & Hao, Shenxue & Gu, Xinxin, 2021. "Perimeter traffic control for single urban congested region with macroscopic fundamental diagram and boundary conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).

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