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Urban rail patrolling: a game theoretic approach

Author

Listed:
  • Abdolmajid Yolmeh

    (Rutgers University)

  • Melike Baykal-Gürsoy

    (Rutgers University)

Abstract

Patrol scheduling is a critical operational decision in protecting urban rail networks against terrorist activities. Designing patrols to protect such systems poses many challenges that have not been comprehensively addressed in the literature of patrol scheduling so far. These challenges include strategic attackers, dynamically changing station occupancy levels and human resource related limitations. In this paper, we develop a game theoretic model for the problem of scheduling security teams to patrol an urban mass transit rail network. Our main objective is to minimize the expected potential damage caused by terrorist activities while observing scheduling constraints. We model this problem as a non-cooperative simultaneous move game between a defender and an attacker. We then develop column generation based algorithms to find a Nash equilibrium for this game. We also present a lower bound for the value of the game which can be used to terminate the column generation algorithm when a desired solution quality is reached. We then run computational experiments to investigate the efficiency of the proposed algorithms and to gain insight about the value of the patrolling game. Our results show the efficiency of the proposed algorithms. Finally, we present results for the case of a real urban rail network.

Suggested Citation

  • Abdolmajid Yolmeh & Melike Baykal-Gürsoy, 2018. "Urban rail patrolling: a game theoretic approach," Journal of Transportation Security, Springer, vol. 11(1), pages 23-40, June.
    • Handle: RePEc:spr:jtrsec:v:11:y:2018:i:1:d:10.1007_s12198-018-0187-z
    DOI: 10.1007/s12198-018-0187-z
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    References listed on IDEAS

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    1. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions - 1," LIDAM Reprints CORE 334, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Jan M. Chaiken & Peter Dormont, 1978. "A Patrol Car Allocation Model: Background," Management Science, INFORMS, vol. 24(12), pages 1280-1290, August.
    3. Hoong Chuin Lau & Zhi Yuan & Aldy Gunawan, 2016. "Patrol scheduling in urban rail network," Annals of Operations Research, Springer, vol. 239(1), pages 317-342, April.
    4. Kenneth Chelst, 1978. "An Algorithm for Deploying a Crime Directed (Tactical) Patrol Force," Management Science, INFORMS, vol. 24(12), pages 1314-1327, August.
    5. Jan M. Chaiken & Peter Dormont, 1978. "A Patrol Car Allocation Model: Capabilities and Algorithms," Management Science, INFORMS, vol. 24(12), pages 1291-1300, August.
    6. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions," LIDAM Reprints CORE 341, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Alex Shvetsov & Svetlana Shvetsova & Valentin Aleksandrovich Kozyrev & Victor Aleksandrovich Spharov & Nikolay Mikhaylovich Sheremet, 2017. "The “car-bomb” as a terrorist tool at metro stations, railway terminals and airports," Journal of Transportation Security, Springer, vol. 10(1), pages 31-43, June.
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    Cited by:

    1. Abigail Luxton & Marin Marinov, 2020. "Terrorist Threat Mitigation Strategies for the Railways," Sustainability, MDPI, vol. 12(8), pages 1-21, April.
    2. Bui, Thuy & Lidbetter, Thomas, 2023. "Optimal patrolling strategies for trees and complete networks," European Journal of Operational Research, Elsevier, vol. 311(2), pages 769-776.
    3. Abdolmajid Yolmeh & Melike Baykal-Gürsoy, 2019. "Two-Stage Invest–Defend Game: Balancing Strategic and Operational Decisions," Decision Analysis, INFORMS, vol. 16(1), pages 46-66, March.

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