Patrol Route Planning for Incident Response Vehicles under Dispatching Station Scenarios. (16th July 2018)

• Record Type: Journal Article
• Authors: Hajibabai, Leila; Saha, Debashis
• Is Part Of: Computer-aided civil and infrastructure engineering. Volume 34:Number 1(2019:Jan.)
• Page Start: 58
• Page End: 70
• Publication Date: 2018-07-16
• Subjects: Civil engineering -- Data processing -- Periodicals; Computer-aided engineering -- Periodicals; 624.0285
• DOI: 10.1111/mice.12384
• Languages: English
• ISSNs: 1093-9687

Abstract

Abstract: Traffic incidents often contribute to major safety concerns, impose additional congestion in the neighboring transportation networks, and induce indirect costs to economy. As roughly a third of traffic crashes are secondary accidents, effective incident management activities are critical, especially on roadways with high traffic volume, to detect, respond to, and clean up incidents in a timely fashion, which supports safety constraints and restores traffic capacity in the transportation network. Hence, it is beneficial to simultaneously plan for first respondents' dispatching station location and patrol route design to mitigate congestion. This article presents an optimal route planning for patrolling vehicles to facilitate quick response to potential accidents. A mixed‐integer nonlinear program is proposed that minimizes the respondents' patrolling travel cost based on the expected maximum response time from each arbitrary location to all incident locations (a.k.a. hotspots) with various incident occurrence probabilities. We have developed a column generation‐based solution technique to solve the route optimization model under different station design scenarios. To investigate the impact of dispatching station design on the routing cost, an integrated genetic algorithm framework with embedded continuous approximation approach is developed that reduces the complexity of the hybrid location design and route planning problem. Numerical experiments on hypothetical networks of various sizes are conducted to indicate the performance of the proposed algorithm and to draw managerial insights. The models and solution techniques, developed in this article, are applicable to a number of network problems that simultaneously involve routing and facility location choices.