Simulative Workload Analysis of Police Forces






Author(s): Cors, Tobias; Hoth, Kai; Tschöke, Martin; Fliedner, Malte; Haase, Knut; Honekamp, Wilfried
Title of the compilation: Mobility in a Globalised World 2019
Editors: Werner, Jan; Biethahn, Niels; Kolke, Reinhard; Sucky, Eric  ; Honekamp, Wilfried
Conference: 9. Mobility in a Globalised World-Konferenz, September 2019, Hamburg
Publisher Information: Bamberg : University of Bamberg Press
Year of publication: 2020
Pages: 7-15
ISBN: 978-3-86309-731-8
Language(s): English
DOI: 10.20378/irb-58522
Licence: Creative Commons - CC BY - Attribution 4.0 International 
Abstract: 
This chapter discusses a simulation model for conducting workload analyses of police forces. Due to the high operational heterogeneity and variability, determining reliable profiles for resource utilization and establishing their relationship to response times is a challenging task in and of itself that requires an adequate consideration of several sources of stochastic influence. Prior approaches from police practice mainly consider static ratios (e.g. resources per number of inhabitants or calls for service) in order to estimate capacity demand. Based on an extensive dataset comprising more than two million data points, we derive stochastic process models for all relevant police operations in a major metropolitan area and use a discreteevent simulation to analyse the effects on workloads and capacity utilization of a given fleet of police cars. The simulation model predicts the spatial and temporal occurrence of police operations and dispatches available vehicles from different districts, in order to model resource sharing in emergency response. This provides key insights into the required capacity over time and constitutes a crucial first step for an adequate capacity planning.
GND Keywords: Polizei; Arbeitsbelastung; Kapazitätsplanung; Simulation; Stochastik
Keywords: Police force planning, stochastic processes, simulation, capacity planning
DDC Classification: 650 Management & public relations  
RVK Classification: QP 413   
Type: Conferenceobject
URI: https://fis.uni-bamberg.de/handle/uniba/58522
Release Date: 10. May 2023

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