Traffic control optimization on isolated intersection using fuzzy neural network and modified particle swarm optimization

Traffic density in big cities due to congestion problems in various points of the city. This problem will occur worse at crucial times such as when rush hours and active working hours. The existence of a traffic light system as a traffic signalling device is a solution to overcome traffic congestion. Appropriate traffic light settings can minimize vehicle waiting times at intersections. The aim of this study is to optimize an adaptive traffic control that can adjust the conditions of traffic flow on certain road segments at isolated intersections. In this study optimization uses methods of Fuzzy Neural Network (FNN) and Modified Particle Swarm Optimization (MPSO). The optimization results will be compared with a regular method of Adaptive Neural Fuzzy Inference System without using MPSO. The simulation results show that the efficiency and adaptability of the combination method of FNN and MPSO are better than the Neural Fuzzy Controller without MPSO. A better result is also indicated by the value of Mean Squared Error (MSE) that decreased from 6.3299 becomes 2.065.