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Speed Trap Optimal Patrolling: STOP Playing Stackelberg Security Games

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Abstract

During 2015, 35,092 people died in motor vehicle crashes on the U.S. roadways, an increase from 32,744 in 2014. The 7.2% increase is the largest percentage increase in nearly 50 years. To reduce reckless driving and the resulting accidents, law enforcement agencies deploy speed traps. However, limited resources prevent full coverage at all times, which leaves many roads uncovered. Law enforcement agencies cannot rely on deterministic coverage as it allows drivers to observe and anticipate covered areas. Therefore, randomized speed trap deployment is vital for active road security. This paper provides random and optimal speed traps deployment based on our innovative STOP framework. STOP utilizes game theory to model drivers’ and law enforcers’ behaviors. In particular, we provide distinct weights to different actions based on the accidents probability, derive the Nash Equilibrium and Stackelberg Security Equilibrium, and determine the best strategies to deploy. The optimal game solution maximizes law enforcer utility, consequently minimizing the cost paid by the society in terms of reducing vehicle accidents.

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References

  1. USDOT-NHTSA. (2015). Traffic safety facts-compilation of motor vehicle crash data from the fatality analysis reporting system and the general estimates system.

  2. U.-N.-D. 8. 354. (2011). General estimates (GES) coding and editing.

  3. Naja R. (2013). Wireless vehicle networks: Applications, architecture and standards. In Wireless vehicular networks for car collision avoidance. New York: Springer.

  4. Shieh, E., Jiang, A. X., Yadav, A., Varakantham, P., & Tambe, M. Unleashing dec-mdps in security games: Enabling effective defender teamwork. In The European conference on artificial intelligence (ECAI).

  5. Korzhyk, D. (2013). Security games: Solution concepts and algorithms. Ph.D. thesis, Graduate School of Duke University.

  6. Bosanský, B., Lisý, V., Jakob, M., & Pechoucek, M. (2011). Computing time-dependent policies for patrolling games with mobile targets. In AAMAS 46.

  7. Jain, M., Korzhyk, D., Vanek, O., Conitzer, V., Pechoucek, M., & Tambe, M. (2011). A double oracle algorithm for zero-sum security games on graphs. In AAMAS.

  8. Brown, M., Saisubramanian, S., Varakantham, P., & Tambe, M. (2014). STREETS: Game-theoretic traffic patrolling with exploration and exploitation. In C. E. Brodley & P. Stone (Eds.), Proceedings of the twenty-eighth AAAI conference on artificial intelligence, Québec City, Québec, Canada.

  9. Jain, M., Kardes, E., Kiekintveld, C., Tambe, M., & Ordóñez, F. (2010). Security games with arbitrary schedules: A branch and price approach. In AAAI.

  10. Turocy, T. L., & Stengel, B. (2001). Game theory—CDAM Research Report LSE-CDAM-2001-09, Texas A&M University & London School of Economics, Technical report.

  11. Prisner, E. (2014). Game theory through examples. Washington, DC: Franklin University Switzerland. MAA, Published and Distributed by The Mathematical Association of America.

    Book  Google Scholar 

  12. Brown, M. (2015). Balancing tradeoffs in security games: Handling defenders and adversaries with multiple objectives. Ph.D. thesis, University of Southern California.

  13. Korzhyk, D., Yin, Z., Kiekintveld, C., Conitzer, V., & Tambe, M. (2011). Stackelberg vs. nash in security games: An extended investigation of interchangeability, equivalence, and uniqueness. Journal of Artificial Intelligence Research, 41, 297–327.

    MathSciNet  MATH  Google Scholar 

  14. Adler, N., Hakkert, A. S., Kornbluth, J., Raviv, T., & Sher, M. (2014). Location-allocation models for traffic police patrolvehicles on an interurban network. Annals of Operations Research, 221, 1–23.

    Article  MATH  Google Scholar 

  15. Curtin, K. M., Qiu, F., Hayslett-McCall, K., & Bray, T. M. (2005). Integrating GIS and maximal covering models to determine optimal police patrol areas. In Geographic Information Systems and Crime Analysis Hershey: IDEA Group Publishing (pp. 214–235).

  16. Sharma, D. K., Ghosh, D., & Gaur, A. (2007). Lexicographic goal programming model for police patrol cars deployment in metropolitan cities. Information and Management Sciences, 18, 173–188.

    MathSciNet  MATH  Google Scholar 

  17. Paruchuri, P. (2007). Keep the adversary guessing: Agent security by policy randomization. California: University of Southern California.

    Google Scholar 

  18. Pita, M. J. J. Deployed armor protection: The application of a game theoretic model for security at the Los Angeles International Airport. In AAMAS (pp. 125–132).

  19. Tsai, J., Rathi, S., Kiekintveld, C., Ordonez, F., & Tambe, M. (2009). IRIS- a tool for strategic security allocation in transportation networks. In AAMAS.

  20. Eric Shieh, B. A. (2013). PROTECT in the Ports of Boston, New York and beyond: Experiences in deploying Stackelberg security games with quantal response. In V. S. Subrahmanian (Ed.), Handbook of computational approaches to counterterrorism (pp. 441–463). New York: Springer.

    Chapter  Google Scholar 

  21. Pita, J., Tambe, M., Kiekintveld, C., Cullen, S., & Steigerwald, E. (2011) Guards—Game theoretic security allocation on a national scale. In 10th International conference on autonomous agents and multiagent systems. Richland: International foundation for autonomous agents and multiagent systems.

  22. Yin, Z., Jiang, A., Johnson, M., Tambe, M., Kiekintveld, C., & Leyton-Brown, K. (2012). Trusts: Scheduling randomized patrols for fare inspection in transit systems. In IAAI.

  23. Inappropriate Speed—(January 2016). Available at www.rospa.com.

  24. New York City Police Department Highway Patrol. Available at www.wikipedia.com.

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Acknowledgements

The work was funded by the Lebanese University and the AUF “Projet de cooperation scientifique interuniversitaire” (PCSI) project and supported by the National Council of Scientific Research (CNRS).

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Authors and Affiliations

  1. Doctoral School of Science and Technology, Lebanese University, Beirut, Lebanon

    Rola Naja & Nadia Mouawad

  2. Li-Parad Laboratory, University of Versailles Saint Quentin, Versailles, France

    Rola Naja & Nadia Mouawad

  3. National Council for Scientific Research CNRS, Beirut, Lebanon

    Ali J. Ghandour

  4. Electrical and Computer Engineering Department, University of Wisconsin, Madison, USA

    Kassem Fawaz

Authors
  1. Rola Naja
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  2. Nadia Mouawad
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  3. Ali J. Ghandour
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  4. Kassem Fawaz
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Correspondence to Rola Naja.

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Naja, R., Mouawad, N., Ghandour, A.J. et al. Speed Trap Optimal Patrolling: STOP Playing Stackelberg Security Games. Wireless Pers Commun 98, 3563–3582 (2018). https://doi.org/10.1007/s11277-017-5029-y

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  • DOI: https://doi.org/10.1007/s11277-017-5029-y

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