CCGN:集中式协作图形变压器多代理强化学习,用于多交叉点信号自由走廊
Hamza Mukhtar1, Adil Afzal1, Sultan Alahmari2
1XeroAI, G.T. Road, Lahore, 54890, Punjab, Pakistan; University of Engineering and Technology (UET), Lahore, GT, Road, Lahore, 54890, Punjab, Pakistan.
概括
本研究介绍了一个集中式协作图形网络 (CCGN),用于创建无信号交通走廊. 通过使十字路口能够共享信息和协作,CCGN可以减少交通拥堵并提高旅行效率.
科学领域:
- 人工智能的人工智能
- 交通工程是交通工程.
- 网络科学 网络科学
背景情况:
- 目前用于交通信号控制的多代理增强学习模型遭受了局部优化,导致交通流量效率低下和旅行时间增加.
- 这些模型缺乏交叉路口之间的通信和适应动态交通条件的适应性,因此需要采用更综合的方法.
研究的目的:
- 为交通信号控制开发一个集中的协作图形网络 (CCGN).
- 通过实现十字路口之间的无交通流动来实现无信号走廊.
主要方法:
- 拟议的CCGN模型整合了地方政策网络 (LPN) 和全球政策网络 (GPN).
- LPN使用变压器和图形卷积网络 (GCN) 进行交叉级别的动作预测.
- GPN使用GCN和Q-network来管理交叉点,并促进无信号走廊,其特定的实现称为深度图形卷积Q-Network (DGCQ).
主要成果:
- CCGN模型,特别是DGCQ,在创建无信号交通走廊方面表现出卓越的性能.
- 对现实世界交通网络的评估表明,拟议的模型优于现有的最先进的方法.
- 该系统有效地利用GCN进行交叉协作和DQN进行信息聚合.
结论:
- 集中的协作图形网络 (CCGN) 在交通信号控制方面取得了重大进展.
- 这种方法有效地减少了交通拥堵,并通过创建无信号走廊来提高旅行效率.
- 该模型的适应和跨十字路口协作能力为复杂的交通场景提供了强大的解决方案.
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