Artificial Intelligence Approach for Optimizing Traffic Signal Timing on an Urban Road Network

Abstract

Using artificial intelligence techniques, we developed a stepwise method to optimize signal timing parameters, such as splits and offsets, on an urban street. The method is separated into two processes, a training process and an optimization process. In the training process, we used two neural network models ; a multilayer model and Kohonen Feature Map model. The former model builds an input-output relationship between the signal timing parameters and the objective variable. The latter model improves the computational efficiency and the estimation precision. In the optimization process, to avoid the entrapment into a local minimum, we used two artificial intelligence methods ; the Cauchy machine and a genetic algorithm. We adjusted the timing parameters so as to minimize the total weighted sum of delay time and stop frequencies. We compared the solutions by both artificial intelligence methods with those by a conventional method and confirmed that the proposed methods are useful for establishing advanced traffic control systems in the future.