Assessment of Stochastic Signal Optimization Method Using Microsimulation

Abstract

A stochastic signal optimization method based on a genetic algorithm (GA-SOM) that interfaces with the microscopic simulation program CORSIM is assessed. A network in Chicago consisting of nine signalized intersections is used as an evaluation test bed. Taking CORSIM as the best representation of reality, the performance of the GA-SOM plan sets a ceiling on how good any (fixed) signal plan can be. An important aspect of this approach is its accommodations of variability. Also discussed is the robustness of an optimal plan under changes in demand. This benchmark is used to assess the best signal plan generated by TRANSYT-7F (T7F), Version 8.1, from among 12 reasonable strategies. The performance of the best T7F plan falls short of the benchmark on several counts, reflecting the need to account for variability in the highly stochastic system of traffic operations, which is not possible under the deterministic conditions intrinsic to T7F. As a sidelight, the performance of the GA-SOM plan within T7F is also computed and it is found to perform nearly as well as the optimum T7F plan.

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