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

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

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 ar...

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Bibliographic Details
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
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.

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