Simulating calls for service for an urban police department

Police departments in the United States strive to schedule officers so that a number of benchmarks are met. The police administration is often asked to justify to local governing bodies the size of the police force. To assess the effects of force size and scheduling strategies on the ability to meet the benchmark goals, we develop a discrete-event simulation for the calls for service (CFS). Using actual call data from an urban police department in the United States, we fit distributions for call rates and service times for input to the simulation. The output of the model includes statistics related to the response delay, cross-sector calls, and officer utilization. The simulation model verifies intuitive notions about policing and reveals interesting properties in the system.

[1]  Richard C. Larson Public Sector Operations Research: A Personal Journey , 2002, Oper. Res..

[2]  Patricio Donoso,et al.  Assessing an ambulance service with queuing theory , 2008, Comput. Oper. Res..

[3]  Raymond R. Hill,et al.  Discrete-Event Simulation: A First Course , 2007, J. Simulation.

[4]  N. C. Simpson,et al.  Fifty years of operational research and emergency response , 2009, J. Oper. Res. Soc..

[5]  John M. MacDonald,et al.  The Effectiveness of Community Policing in Reducing Urban Violence , 2002 .

[6]  A. Piehl,et al.  Problem-Oriented Policing, Deterrence, and Youth Violence: An Evaluation of Boston's Operation Ceasefire , 2001 .

[7]  Averill Law Simulation Modeling and Analysis with Expertfit Software , 2006 .

[8]  Peter J. Kolesar,et al.  ANNIVERSARY ARTICLE: Improving Emergency Responsiveness with Management Science , 2004, Manag. Sci..

[9]  George S. Fishman,et al.  Discrete-event simulation , 2001 .

[10]  Philip E. Taylor,et al.  A Break from Tradition for the San Francisco Police: Patrol Officer Scheduling Using an Optimization-Based Decision Support System , 1989 .

[11]  Bijan Fazlollahi,et al.  CATCH: Computer Assisted Tracking of Criminal Histories System , 1993 .

[12]  RICHARD C. LARSON,et al.  A hypercube queuing model for facility location and redistricting in urban emergency services , 1974, Comput. Oper. Res..

[13]  Richard C. Larson Public Sector Operations Research , 2002 .

[14]  Kevin M. Curtin,et al.  Determining Optimal Police Patrol Areas with Maximal Covering and Backup Covering Location Models , 2010 .

[15]  George C. Klein For The New Commander In Chief: A Violence Prevention Strategy , 2008 .

[16]  J. Chaiken,et al.  A Patrol Car Allocation Model: Background , 1978 .

[17]  P. Kolesar,et al.  Improving Emergency Responsiveness with Management Science , 2004 .

[18]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[19]  Pierre L'Ecuyer,et al.  Good Parameters and Implementations for Combined Multiple Recursive Random Number Generators , 1999, Oper. Res..

[20]  Nicholas Corsaro,et al.  Project Safe Neighborhoods and Violent Crime Trends in US Cities: Assessing Violent Crime Impact , 2010 .

[21]  Y. Pawitan In all likelihood : statistical modelling and inference using likelihood , 2002 .