Artificial Neural Network–Based Heuristic Optimal Traffic Signal Timing

Optimal Traffic Signal Control System (OTSCS) software was developed to test the feasibility of dynamically controlling a traffic signal by finding optimal signal timing to minimize delay at signalized intersections. It was also designed as a research tool to study the learning behavior of artificial neural networks (ANNs) and the properties of heuristic search methods. It consists of a level-of-service evaluation model that is based on an ANN and a heuristic optimization model that interacts with the level-of-service evaluation model. This article discusses the latter model, called the Optimal Traffic Signal Timing Model (OTSTM). The OTSTM was applied to determine optimal signal timing of 2-phase traffic signals to evaluate the model's performance. Two search methods were employed: a depth-first search method and a direction-search method. 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 more than 10 times less than the cost of obtaining an optimal solution by the depth-first search cases. The study showed that once the ANN is properly trained, heuristic optimal signal timing combined with ANNs can be used as a decision-support tool for dynamic signal control. This paper demonstrates how OTSTM can quickly find an optimal signal-timing solution for 2-phase traffic signals.