Scholarlink

This paper introduces two polling models with distinct vacation service policies. Aimed at optimizing traffic light management at intersections. The objective is to minimize vehicle waiting times by dynamically adjusting the duration of green lights according to real-time traffic conditions. A genetic algorithm was employed to determine the optimal timing plans for each phase. The models were tested using the SUMO (Simulation of Urban MObility) simulator. The results determine the model that improves system performance, significantly reduces congestion and vehicle dwell times.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Traffic Signal Timing Optimization with Genetic Algorithms: Modeling Techniques and Simulation Results

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Sabiha Larbi,
  • Fazia Rahmoune,
  • Mohammed Said Radjef,
  • Zohra Aoudia

摘要

This paper introduces two polling models with distinct vacation service policies. Aimed at optimizing traffic light management at intersections. The objective is to minimize vehicle waiting times by dynamically adjusting the duration of green lights according to real-time traffic conditions. A genetic algorithm was employed to determine the optimal timing plans for each phase. The models were tested using the SUMO (Simulation of Urban MObility) simulator. The results determine the model that improves system performance, significantly reduces congestion and vehicle dwell times.