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Evolving Adaptive Traffic Signal Controllers for a Real Scenario Using Genetic Programming with an Epigenetic Mechanism

An important challenge for traffic signal control is adapting to irregular changes in traffic. In recent years, different heuristics have been developed to address this issue. However, most of them are tested in artificial scenarios under controlled circumstances. In this paper, we present the first implementation of Genetic Programming in the evolution of traffic signal controllers for a real-world scenario. The evolved controllers are compared with a static control and an actuated control. The results indicate a significant improvement over traditional methods. Moreover, additional experiments indicate that the evolved controllers have the ability to adapt to unplanned changes in traffic conditions.