Stochastic optimization of traffic control and transit priority settings in VISSIM
operations - traffic, infrastructure - bus/tram priority, infrastructure - traffic signals
VISSIM (Computer model), Traffic signal timing, Traffic signal settings, Traffic signal priority systems, Traffic signal preemption, Traffic control, Stochastic processes, Settings (Traffic signals), Random processes, Preemption (Traffic signals), Optimization, Optimisation, Microsimulation, Genetic algorithms
Genetic algorithms have been shown to be effective tools for optimizations of traffic signal timings. However, only one tool that combines genetic algorithms and traffic microsimulation has matured to a commercial deployment: Direct CORSIM optimization, a feature of TRANSYT-7F. This paper presents a genetic algorithm formulation that builds on the best of the recorded methods, by extending their capabilities. It optimizes four basic signal timing parameters and transit priority settings using VISSIM microsimulation as the evaluation environment. The program is the first optimization tool that optimizes traffic control transit priority settings on roads with both private and transit traffic. These settings are optimized either simultaneously with the basic signal timings or separately, thus improving overall traffic operations without changing existing basic signal timings. The optimization has been tested on two VISSIM models: a suburban network of 12 signalized intersections in Park City, UT, and an urban corridor with transit operations in Albany, NY. The results show that timing plans optimized by the genetic algorithm outperformed the timing plans from the field and SYNCHRO. Optimization of the transit priority settings shows that adjustment of these settings has significant impact on travelers delay on the corridors with mixed traffic and transit operations.
Stevanovic, Jelka, Stevanovic, Aleksandar, Martin, Peter, Bauer, Thomas, (2008). Stochastic optimization of traffic control and transit priority settings in VISSIM. Transportation Research Part C: Emerging Technologies, Volume 16, Issue 3, pp 332-349.