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Optimising police dispatch for incident response in real time

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  • Sarah Dunnett
  • Johanna Leigh
  • Lisa Jackson

Abstract

It is crucial that police forces operate in a cost efficient manner and, in the case of incident response, that the most efficient resources are allocated. The current procedure is that police response units are allocated manually by a dispatcher using a resource list and mapping software. The efficiency of this process can be improved by the use of integrated mathematical approaches embedded within an automatic framework, yielding the optimal selection framework developed in this paper. The framework combines mapping and routing algorithms, and a decision process to facilitate optimal officer selection for incident response. The decision process considers information such as quickest response time, predicted traffic conditions, driving qualifications, response unit availability and demand coverage. The selection framework has been tested and validated through simulation and has shown to increase the efficiency of response units through reduced response times, increased response unit availability, and greater demand coverage.

Suggested Citation

  • Sarah Dunnett & Johanna Leigh & Lisa Jackson, 2019. "Optimising police dispatch for incident response in real time," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(2), pages 269-279, February.
    • Handle: RePEc:taf:tjorxx:v:70:y:2019:i:2:p:269-279
    DOI: 10.1080/01605682.2018.1434401
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    Citations

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    Cited by:

    1. Zhen, Lu & Wu, Jingwen & Chen, Fengli & Wang, Shuaian, 2024. "Traffic emergency vehicle deployment and dispatch under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    2. LaBerge, Alyssa & Mason, Makayla & Sanders, Kaelyn, 2022. "Police dispatch times: The effects of neighborhood structural disadvantage," Journal of Criminal Justice, Elsevier, vol. 79(C).
    3. David Payares-Garcia & Javier Platero & Jorge Mateu, 2023. "A Dynamic Spatio-Temporal Stochastic Modeling Approach of Emergency Calls in an Urban Context," Mathematics, MDPI, vol. 11(4), pages 1-28, February.
    4. 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.
    5. Soumendra Nath Sanyal & Izabela Nielsen & Subrata Saha, 2020. "Multi-Objective Human Resource Allocation Approach for Sustainable Traffic Management," IJERPH, MDPI, vol. 17(7), pages 1-16, April.

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