Abstract

In order to minimize incident duration, reduce secondary crashes, and improve travel time reliability along interstates and primary roadways, the Virginia Department of Transportation (VDOT) employs a fleet of vehicles known as Safety Service Patrols (SSPs) to detect traffic incidents and disruptions, assist stranded motorists, and perform short-term traffic control and scene management. At the time of their origin in the 1960s, the SSP routes, loose schedules detailing which road segments to patrol during a shift, were developed independently in each of VDOT's five regions, using largely anecdotal evidence. Fifty years later, and these routes have largely remained the same, having never been formally analyzed to check for system efficiency. This has left the agency concerned that the current routes may not be maximizing the patrollers' potential effect on public safety and traffic management. This paper thus develops a route optimization model via a genetic algorithm to optimally position the SSPs to minimize their average incident response time. This model is informed from five years' worth of traffic incident data in Virginia, collected and analyzed to generate a probability distribution estimating the concentration of incidents along varying route segments across each day of the week and time of day. Once layered onto the deliverable, an interactive dashboard depicting the locations of all the incidents, VDOT personnel will be able to visualize where the SSPs are in relation to the incidents. The SSP route optimization and visualization tools have the potential to improve system performance by respectively reducing average response time and allowing VDOT to gain a better understanding of traffic incident hotspots.

Full Text
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