Evolving Control Laws for a Network of Traffic Signals

Optimally controlling the timings of traffic signals within a network of intersections is a difficult but important problem. Because the traffic signals need to coordinate their behavior to achieve the common goal of optimizing traffic flow through the network, this is a problem in collective intelligence. We apply a hybrid of a genetic algorithm and strongly typed genetic programming (STGP) to the problem of learning control laws which optimize aggregate performance. STGP learns the single basic decision tree to be executed by all the intersections when deciding whether to change the phase of the traffic signal. The genetic algorithm learns different constants to be used in these decision trees for different intersections, hence allowing specialization based on differences in geometry and traffic flow. Preliminary experimental work shows that our approach yields good performance on a variety of network configurations and that it can evolve control laws which induce cooperation, communication, and specialization among the traffic signals.