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A Multiple Dispatch Queueing Model of Police Patrol Operations


  • Linda Green

    (Graduate School of Business, Columbia University, New York, New York 10027)


One of the primary concerns of urban police departments is the effective use of patrol cars. In large cities, police assigned to patrol cars typically account for more than 50% of total police manpower and their allocation has become particularly crucial in light of recent fiscal cutbacks. The police patrol system is a complex multi-server queueing system, and recently many urban police departments have been using queueing models to estimate delays in responding to calls for police assistance. The magnitude of these delays is usually one basis for measuring system efficiency as well as for determining allocations of patrol cars among precincts. A major limitation of these models is that they assume that only a single unit is dispatched to each call. In general, this is not the case, particularly in police departments with one-officer patrol cars. This paper describes a model that has been developed to represent patrol car operations more accurately. It is a multi-priority queueing model that explicitly reflects multiple car dispatches. Its purpose is not only to provide a better basis for the efficient allocation of patrol cars, but to enable police officials to gauge the effects of policies, such as one-officer patrol cars, which affect the number of cars dispatched to various types of incidents.

Suggested Citation

  • Linda Green, 1984. "A Multiple Dispatch Queueing Model of Police Patrol Operations," Management Science, INFORMS, vol. 30(6), pages 653-664, June.
  • Handle: RePEc:inm:ormnsc:v:30:y:1984:i:6:p:653-664

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    Cited by:

    1. Capar, Ismail & Kuby, Michael & Leon, V. Jorge & Tsai, Yu-Jiun, 2013. "An arc cover–path-cover formulation and strategic analysis of alternative-fuel station locations," European Journal of Operational Research, Elsevier, vol. 227(1), pages 142-151.
    2. 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.
    3. Diwas S. Kc & Christian Terwiesch, 2009. "Impact of Workload on Service Time and Patient Safety: An Econometric Analysis of Hospital Operations," Management Science, INFORMS, vol. 55(9), pages 1486-1498, September.
    4. Hall, Randolph, 2000. "Incident Dispatching, Clearance and Delay," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2pp689vn, Institute of Transportation Studies, UC Berkeley.
    5. Keskin, Burcu B. & Li, Shirley (Rong) & Steil, Dana & Spiller, Sarah, 2012. "Analysis of an integrated maximum covering and patrol routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 215-232.
    6. Hall, Randolph W., 2002. "Incident dispatching, clearance and delay," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(1), pages 1-16, January.
    7. Nilay Tan{i}k Argon & Serhan Ziya, 2009. "Priority Assignment Under Imperfect Information on Customer Type Identities," Manufacturing & Service Operations Management, INFORMS, vol. 11(4), pages 674-693, June.
    8. repec:eee:transe:v:103:y:2017:i:c:p:143-157 is not listed on IDEAS
    9. repec:pal:jorsoc:v:60:y:2009:i:1:d:10.1057_jors.2009.3 is not listed on IDEAS
    10. Hall, Randolph W., 2001. "Incident Management: Process Analysis and Improvement," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt1jf6j37t, Institute of Transportation Studies, UC Berkeley.
    11. William P. Millhiser & Charu Sinha & Matthew J. Sobel, 2016. "Optimality of the fastest available server policy," Queueing Systems: Theory and Applications, Springer, vol. 84(3), pages 237-263, December.


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