Traffic Signal Optimization Program for Oversaturated Conditions: Genetic Algorithm Approach

Abstract

Traffic signal optimization programs have been used widely among transportation professionals. However, none of the existing computer programs can optimize all four traffic control parameters (i.e., cycle length, green split, offset, and phase sequence) simultaneously, even for undersaturated conditions. In this paper, a genetic algorithm-based signal optimization program that can handle oversaturated signalized intersections is presented. The program consists of a genetic algorithm (GA) optimizer and a mesoscopic traffic simulator. The GA optimizer is designed to search for a near-optimal traffic signal timing plan on the basis of a fitness value obtained from the mesoscopic simulator. The proposed program is compared with the newly released TRANSYT-7F version 8.1 on the basis of CORSIM simulation program. Three different demand volume levels—low, medium, and high demand—are tested. For the low-demand and high-demand volume cases, the GA-based program produced statistically better signal timing plans than did TRANSYT-7F in terms of queue time. In the case of medium-demand volume level, the signal timing plan obtained from the GA-based program produced statistically equivalent queue time compared with TRANSYT-7F. Both programs are judged to provide superior capability for oversaturated conditions due to their queue blockage model when compared with previously available signal timing optimization software.