适应性交通信号控制的序列决策变压器
Rui Zhao1, Haofeng Hu1, Yun Li2
1College of Automotive Engineering, Jilin University, Changchun 130025, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
一个新的序列决策变压器 (SDT) 使用深度强化学习 (DRL) 来优化自适应交通信号控制 (ATSC). 这种先进的方法显著改善了交通流量,并减少了城市环境中的拥堵.
科学领域:
- 智能运输系统 智能运输系统
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 城市交通拥堵在全球范围内带来了重大的经济和环境挑战.
- 适应性交通信号控制 (ATSC) 提供了一个潜在的解决方案,深度强化学习 (DRL) 显示了最近的进展.
- 现有的ATSC方法在复杂的交通动态和大型观测空间方面扎.
研究的目的:
- 引入一种新的基于DRL的ATSC方法,即序列决策变压器 (SDT),旨在解决城市交通拥堵问题.
- 将ATSC问题建模为适合DRL的马尔科夫决策过程 (MDP).
- 通过利用序列决策模型和变压器架构来增强交通管理.
主要方法:
- 序列决策变压器 (SDT) 模型采用基于变压器的架构,在演员-关键框架内采用编码器-解码器结构.
- 编码器处理观察并提供价值估计,而解码器作为政策网络,输出行动.
- 邻近政策优化 (PPO) 用于政策网络更新,从历史流量数据中学习.
主要成果:
- 采用SDT方法可以显著提高交通吞吐量,缩短训练时间和更好地管理大型观测空间.
- 评估显示,SDT在车辆数量,平均速度和队列长度等关键指标上表现优于传统的ATSC算法和最先进的FRAP方法.
- 具体的改进包括在某些指标上比传统的ATSC提高26.8%,比FRAP提高18%.
结论:
- 序列决策变压器 (SDT) 为自适应交通信号控制提供了一个高效的基于DRL的解决方案.
- 将LLM启发的序列决策模型与DRL集成为应对复杂的城市交通管理挑战提供了一个有希望的途径.
- 这项研究强调了先进的人工智能技术在缓解城市拥堵和提高交通效率方面的潜力.
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