Artificial Neural Network–Based Heuristic Optimal Traffic Signal Timing

Abstract

A software called Optimal Traffic Signal Control System (OTSCS) was developed by us for testing the feasibility of dynamically controlling a traffic signal by finding optimal signal timing to minimize delay at signalized intersections. It also was designed as a research tool to study the learning behavior of artificial neural networks and the properties of heuristic search methods. It consists of a level‐of‐service evaluation model that is based on an artificial neural network and a heuristic optimization model that interacts with the level‐of‐service evaluation model. This article discusses the latter model, named the Optimal Traffic Signal Timing Model (OTSTM). The OTSTM was applied to determine optimal signal timing of two‐phase traffic signals to evaluate the model's performance. Two search methods were employed: a depth‐first search method (an enumeration method) and a direction‐search method that the authors developed. It was found that the OTSTM with the direction search resulted in “optimal” signal timings similar to the depth‐first search, which would always produce a global optimal timing. Yet the cost of the direction search, as measured by the CPU time of the computer used for analysis, was found to be much less than the cost of obtaining an optimal solution by the depth‐first search cases—more than 10 times less. The study showed that once the artificial neural network is properly trained, heuristic optimal signal timing combined with artificial networks can be used as a decision‐support tool for dynamic signal control. This article demonstrates how OTSTM can quickly find an optimal signal‐timing solution for two‐phase traffic signals.