Location-allocation models for traffic police patrol vehicles on an interurban network

Nicole Adler, Alfred Shalom Hakkert, Jonathan Kornbluth, Tal Raviv, Mali Sher

Research output: Contribution to journalArticlepeer-review

Abstract

This research investigates the traffic police routine patrol vehicle (RPV) assignment problem on an interurban road network through a series of integer linear programs. The traffic police RPV’s main task, like other emergency services, is to handle calls-for-service. Emergency services allocation models are generally based on the shortest path algorithm however, the traffic police RPV also handles other roles, namely patrolling to create a presence that acts as a deterrence, and issuing tickets to offenders. The RPVs need to be located dynamically on both hazardous sections and on roads with heavy traffic in order to increase their presence and conspicuousness, in an attempt to prevent or reduce traffic offences, road accidents and traffic congestion. Due to the importance of the traffic patrol vehicle’s location with regard to their additional roles, allocation of the RPVs adheres to an exogenous, legal, time-to-arrival constraint. We develop location-allocation models and apply them to a case study of the road network in northern Israel. The results of the four models are compared to each other and in relation to the current chosen locations. The multiple formulations provide alternatives that jointly account for road safety and policing objectives which aid decision-makers in the selection of their preferred RPV assignments. The results of the models present a location-allocation configuration per RPV per shift with full call-for-service coverage whilst maximizing police presence and conspicuousness as a proxy for road safety.

Original languageEnglish
Pages (from-to)9-31
Number of pages23
JournalAnnals of Operations Research
Volume221
Issue number1
DOIs
StatePublished - 1 Sep 2014

Keywords

  • Allocation
  • Emergency services
  • Location
  • Network
  • Traffic police

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • Management Science and Operations Research

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