Solving Traffic Signal Setting Problem Using Machine Learning

We present a method for optimizing traffic signal settings which can be used for offline planning and realtime adaptive traffic management. The method is based on metaheuristics efficiently exploring space of possible settings and evaluating candidate solutions using a microscopic traffic simulation or metamodels of simulations built using machine learning algorithms (e.g., neural networks, LightGBM). We present results of extensive experiments and compare different algorithms and their configurations in order to find the best approach in our use case. Experiments were carried out on a realistic road network of Warsaw (maps originated from the OpenStreetMap service) and showed that LightGBM may outperform neural networks in terms of accuracy of approximations, time efficiency and optimality of traffic signal settings, which is a new and important result. We also show that in terms of traffic optimization genetic algorithms give the best results comparing to other metaheuristics.