城市流动的实时交通信号优化:一个强化学习增强框架,适用于科威特市
Abedalmuhdi Almomany1, Eedi Eedi1, Muhammed Sutcu2
1Department of Electrical and Computer Engineering, Gulf University for Science and Technology, Hawally, Kuwait.
Frontiers in robotics and AI
|October 10, 2025
概括
这项研究引入了一个智能交通控制系统,可以适应交通条件,减少拥堵和等待时间. 该系统使用现场可编程门阵列 (FPGA) 进行高效的实时城市移动管理.
科学领域:
- 智能运输系统 智能运输系统
- 计算机工程 计算机工程
- 环境科学 环境科学
背景情况:
- 城市流动性面临着日益增加的交通拥堵和相关环境影响带来的挑战.
- 智慧城市计划需要高效和适应性的交通管理解决方案.
- 现有的交通控制系统经常与动态条件和能源限制作斗争.
研究的目的:
- 开发一个智能和适应性的交通控制策略,以提高智慧城市的城市流动性.
- 尽量减少交通等待时间,减少拥堵,改善环境健康.
- 评估各种交通管理算法和硬件实现的有效性.
主要方法:
- 研究和评估基于规则,基于优化和机器学习 (强化学习) 的算法.
- 采用微观交通模拟和统计分析来评估性能.
- 在现场可编程门阵列 (FPGA) 平台上实现算法,用于实时处理.
主要成果:
- 拟议的系统显著改善了交通流量和减少拥堵.
- 与通用处理单元 (GPPU) 相比,FPGA的实施速度超过7倍.
- 该系统有效地减少了燃料消耗和二氧化碳排放.
结论:
- 智能,适应性强的交通控制策略提高了城市流动性和环境质量.
- 基于FPGA的实现为智能城市交通管理提供了高效,低延迟的处理.
- 该解决方案提供了一种可行的方法来应对交通挑战并改善空气质量,特别是在科威特等地区.
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