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A Patrol Car Allocation Model: Background

Author

Listed:
  • Jan M. Chaiken

    (The Rand Corporation, Santa Monica, California)

  • Peter Dormont

    (Mathematica, Inc., Princeton, New Jersey)

Abstract

Before designing a computer program for allocating police patrol cars by time and geography, a review was undertaken of previously existing programs of this type. Nearly all of the programs calculated queuing statistics for the collection of patrol cars by assuming a steady-state system with calls for service arriving within priority levels according to Poisson processes and having independent, identical, exponentially distributed service times. Unavailabilities of patrol cars for reasons other than calls for service were handled in the models either by artificially increasing the arrival rate of calls or by assuming that the number of servers is smaller than the number of patrol cars. Some programs calculated additional performance measures such as travel times and preventive patrol frequencies. All the programs had the capabilities to describe performance statistics for an allocation proposed by the user, but they differed in their capabilities to prescribe desirable allocations. None of the programs had achieved general acceptance because each had virtues and inadequacies not present in the others.

Suggested Citation

  • Jan M. Chaiken & Peter Dormont, 1978. "A Patrol Car Allocation Model: Background," Management Science, INFORMS, vol. 24(12), pages 1280-1290, August.
    • Handle: RePEc:inm:ormnsc:v:24:y:1978:i:12:p:1280-1290
    DOI: 10.1287/mnsc.24.12.1280
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    Citations

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    Cited by:

    1. 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.
    2. Nicole Adler & Alfred Hakkert & Jonathan Kornbluth & Tal Raviv & Mali Sher, 2014. "Location-allocation models for traffic police patrol vehicles on an interurban network," Annals of Operations Research, Springer, vol. 221(1), pages 9-31, October.
    3. Linda V. Green & Peter J. Kolesar, 2004. "ANNIVERSARY ARTICLE: Improving Emergency Responsiveness with Management Science," Management Science, INFORMS, vol. 50(8), pages 1001-1014, August.
    4. Sukanya Samanta & Goutam Sen & Soumya Kanti Ghosh, 2022. "A literature review on police patrolling problems," Annals of Operations Research, Springer, vol. 316(2), pages 1063-1106, September.
    5. Verma, Arvind, 1998. "The fractal dimension of policing," Journal of Criminal Justice, Elsevier, vol. 26(5), pages 425-435, September.
    6. P. Daniel Wright & Matthew J. Liberatore & Robert L. Nydick, 2006. "A Survey of Operations Research Models and Applications in Homeland Security," Interfaces, INFORMS, vol. 36(6), pages 514-529, December.
    7. Camacho-Collados, M. & Liberatore, F. & Angulo, J.M., 2015. "A multi-criteria Police Districting Problem for the efficient and effective design of patrol sector," European Journal of Operational Research, Elsevier, vol. 246(2), pages 674-684.
    8. Schlicher, Loe & Lurkin, Virginie, 2024. "Fighting pickpocketing using a choice-based resource allocation model," European Journal of Operational Research, Elsevier, vol. 315(2), pages 580-595.
    9. Kyle Y. Lin & Michael P. Atkinson & Kevin D. Glazebrook, 2014. "Optimal patrol to uncover threats in time when detection is imperfect," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(8), pages 557-576, December.
    10. N C Simpson & P G Hancock, 2009. "Fifty years of operational research and emergency response," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 126-139, May.
    11. Lei, Chao & Zhang, Qian & Ouyang, Yanfeng, 2017. "Planning of parking enforcement patrol considering drivers’ parking payment behavior," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 375-392.

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