Optimization of Traffic Lights Timing Based on Multiple Neural Networks

Abstract

This paper presents a neural networks based traffic light controller for urban traffic road intersection called EOM-MNN Controller (Environment Observation Method based on Multiple Neural Networks Controller). Traffic congestion leads to problems like delays and higher fuel consumption. Consequently, alleviating congested situation is not only good to economy but also to environment. The problem of traffic light control is very challenging. Traditional mathematical methods have some limitations when they are applied in traffic control. Thus, modern artificial intelligent ways have gained more and more attentions. In this work, EOM is a very interesting mathematical method for determining traffic lights timing that was developed by Ejzenberg [4]. However, this method has some implications in which multiple neural networks were proposed to improve such problems. The solution was compared with the conventional method through scenario of simulation in microscopic traffic simulation software.