适应性交通信号控制系统的多目标深度强化学习方法,同时优化交叉路口的安全性,效率和脱碳
Gongquan Zhang1, Fangrong Chang2, Jieling Jin1
1School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China.
Accident; analysis and prevention
|February 17, 2024
概括
本研究使用多目标深度强化学习 (DRL) 进行自适应交通信号控制 (ATSC),提高安全性和减少排放. 基于DRL的ATSC系统提高了交通流量,同时平衡了效率,安全和脱碳目标.
科学领域:
- 智能运输系统 智能运输系统
- 在交通管理中的人工智能.
- 可持续的城市流动性
背景情况:
- 传统的自适应交通信号控制 (ATSC) 系统往往优先考虑交通效率,忽视了安全和环境影响等关键方面.
- 现有的ATSC方法很难动态地适应实时交通波动和复杂的城市环境.
- 需要先进的控制策略,可以全面优化交通流量,安全和脱碳.
研究的目的:
- 通过使用深度强化学习 (DRL) 引入一种新的多目标自适应交通信号控制 (ATSC) 方法.
- 开发一个基于DRL的ATSC算法,能够同时优化交通安全,效率和脱碳.
- 评估拟议的DRL-ATSC系统在模拟的城市环境中与传统方法相比的性能.
主要方法:
- 实现一个DRL算法,特别是对决双深Q网络 (D3QN) 框架,用于ATSC.
- 在中国长沙的交通交叉口的模拟,以测试拟议的DRL-ATSC算法.
- 对DRL-ATSC系统与传统的ATSC和以效率为中心的ATSC算法的比较分析.
主要成果:
- 与基线方法相比,DRL-ATSC算法实现了超过16%的交通冲突减少和碳排放减少4%.
- 与传统ATSC相比,观察到18%的等待时间显著减少,与专注于效率的DRL算法相比,至少增加了0.64%.
- 在高交通需求场景下,拟议的系统在所有三个目标 (安全,效率,脱碳) 中都表现出卓越的性能.
结论:
- 新型基于DRL的ATSC方法有效地平衡了多个交通控制目标,包括安全,效率和脱碳.
- D3QN框架为开发可适应交通信号控制系统提供了坚实的基础,这些系统的性能优于传统方法.
- 这项研究提供了一种实用和先进的解决方案,用于优化现实世界的交通信号控制,动态的交通条件,有助于更智能和更可持续的城市交通.
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