Traffic Signal Optimization Based on Fuzzy Control and Differential Evolution Algorithm

Abstract

Urban crossroads are frequently the sites of concentrated urban traffic congestion. An urban road traffic signal management system is required to avoid issues like fuel waste from prolonged idling periods, exhaust pollutants from frequent vehicle starts and stops, and driving delays brought on by traffic congestion on trunk lines. For traffic control studies, maximizing an intersection's traffic capacity and lowering vehicle delay rates have never been easy. Urban traffic signal coordination is thought of as a multi-objective optimization issue. This study examines a mathematical model for metropolitan trunk traffic. To get an optimization, models for average delay, average queue length, total delay calculation for cars at junctions, and vehicle exhaust pollution are developed. model for a new coordinated control system for traffic trunks. Furthermore, the adaptive sequencing mutation multi-objective differential evolution algorithm (FASM-MDEA) and fuzzy control theory are combined in this work. In order to address the issue of traffic signal coordination and control of urban trunk lines, a novel optimization technique for traffic signal management at urban junctions is put forth as a fix for the traffic flow optimization model. The efficiency of the model optimization approach suggested in this study is shown by the simulation results.