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Game theoretic resource allocation model for designing effective traffic safety solution against drunk driving

Author

Listed:
  • Jie, Yingmo
  • Liu, Charles Zhechao
  • Li, Mingchu
  • Choo, Kim-Kwang Raymond
  • Chen, Ling
  • Guo, Cheng

Abstract

To reduce the number of deaths and injuries due to drunk driving (also referred to as drink driving, driving while intoxicated, and driving under the influence of alcohol in the literature), many countries have deployed public safety resources to inspect traffic network. However, challenges remain in allocating limited public safety resources to the significantly large traffic networks. In this paper, we propose an optimal public safety resource allocation scheme to inspect drunk driving. To highlight the utilization of limited public safety resources, first, we model the issue of drunk driving as a defender-attacker Stackelberg game. In the game, the law enforcement agency (the defender) allocates public safety resources in a traffic network to arrest drunk drivers (the attackers), and the attacker seeks to choose a feasible route given the defender's strategy to maximize the escape probability. Second, we develop an effective approach to compute the optimal defender strategy based on a double oracle framework. Third, we analyze the complexity of the defender oracle problem. Then, we conduct simulations on directed graphs, which are abstracted from the city traffic network in Dalian, China, to demonstrate that our scheme achieves a robust solution and higher utility, and is capable of scaling up to handle realistic-sized drunk-driving problems.

Suggested Citation

  • Jie, Yingmo & Liu, Charles Zhechao & Li, Mingchu & Choo, Kim-Kwang Raymond & Chen, Ling & Guo, Cheng, 2020. "Game theoretic resource allocation model for designing effective traffic safety solution against drunk driving," Applied Mathematics and Computation, Elsevier, vol. 376(C).
  • Handle: RePEc:eee:apmaco:v:376:y:2020:i:c:s0096300320301119
    DOI: 10.1016/j.amc.2020.125142
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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. Gary S. Becker & William M. Landes, 1974. "Essays in the Economics of Crime and Punishment," NBER Books, National Bureau of Economic Research, Inc, number beck74-1.
    3. Tanimoto, Jun & Nakamura, Kousuke, 2016. "Social dilemma structure hidden behind traffic flow with route selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 92-99.
    4. 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).
    5. Fang, He & Xu, Li & Choo, Kim-Kwang Raymond, 2017. "Stackelberg game based relay selection for physical layer security and energy efficiency enhancement in cognitive radio networks," Applied Mathematics and Computation, Elsevier, vol. 296(C), pages 153-167.
    6. Tanimoto, Jun & An, Xie, 2019. "Improvement of traffic flux with introduction of a new lane-change protocol supported by Intelligent Traffic System," Chaos, Solitons & Fractals, Elsevier, vol. 122(C), pages 1-5.
    7. Askar, S.S., 2018. "Tripoly Stackelberg game model: One leader versus two followers," Applied Mathematics and Computation, Elsevier, vol. 328(C), pages 301-311.
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    Cited by:

    1. Liu, Chunxia & Lu, Kaihong & Chen, Xiaojie & Szolnoki, Attila, 2023. "Game-theoretical approach for task allocation problems with constraints," Applied Mathematics and Computation, Elsevier, vol. 458(C).
    2. Shen, Ziwen & Dong, Tao & Huang, Tingwen, 2025. "Data-driven bipartite synchronization control of multi-agent systems with asymmetric input saturation over switching networks," Applied Mathematics and Computation, Elsevier, vol. 494(C).
    3. Pablo Escalona & Luce Brotcorne & Bernard Fortz & Nathalia Wolf, 2026. "Spot-fare inspection in urban bus transportation systems: strategy and unpredictability under a Stackelberg game approach," Public Transport, Springer, vol. 18(1), pages 29-64, March.

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