Development of efficient metaheuristics-based optimizer for traffic light scheduling problem: a comparative study

• Main Author: Chua, Angie
• Other Authors: Su Rong
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Engineering; Metaheuristic algorithms; Genetic algorithms; Particle swarm optimization; Differential evolution; Traffic light scheduling problem
• Online Access: https://hdl.handle.net/10356/176876
• Institution: Nanyang Technological University

Traffic congestion is a pressing issue in urban areas, affecting both economic productivity and quality of life. This report explores the use of metaheuristic algorithms to optimize traffic light schedules, aiming to minimize delays and improve traffic flow. Various metaheuristic algorithms are implemented and evaluated, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). The results demonstrate the effectiveness of metaheuristic algorithms in addressing the Traffic Light Scheduling Problem (TLSP) and offer insights into their practical application in real-world traffic management scenarios.