用Transformer解决交通信号控制中的观测不全问题
Mitigating Partial Observability in Adaptive Traffic Signal Control with Transformers
- 引入Transformer架构提升交通信号控制器对历史数据的感知能力
- 在真实场景中实现更优的交通流协调，减少拥堵
- 适合城市交通优化与智能控制研究者参考
高效的交通信号控制对于管理城市交通、减少拥堵、提升安全与可持续性至关重要。强化学习（RL）已成为改进自适应交通信号控制（ATSC）系统的一种有前景的方法，使控制器可通过与环境交互学习最优策略。然而，由于交通网络中存在部分可观测性（PO），即代理只能获取有限信息，导致控制效果受限。本文提出将基于Transformer的控制器融入ATSC系统，以有效应对这一挑战。通过设计提升训练效率与效果的策略，实验证明该模型在真实场景中具备更强的协调能力。结果表明，基于Transformer的模型能从历史观测中捕捉关键信息，生成更优的控制策略，显著改善交通流状况。本研究展示了利用先进Transformer架构提升城市交通管理潜力的可能性。
原文摘要 · Abstract (English)
Efficient traffic signal control is essential for managing urban transportation, minimizing congestion, and improving safety and sustainability. Reinforcement Learning (RL) has emerged as a promising approach to enhancing adaptive traffic signal control (ATSC) systems, allowing controllers to learn optimal policies through interaction with the environment. However, challenges arise due to partial observability (PO) in traffic networks, where agents have limited visibility, hindering effectiveness. This paper presents the integration of Transformer-based controllers into ATSC systems to address PO effectively. We propose strategies to enhance training efficiency and effectiveness, demonstrating improved coordination capabilities in real-world scenarios. The results showcase the Transformer-based model's ability to capture significant information from historical observations, leading to better control policies and improved traffic flow. This study highlights the potential of leveraging the advanced Transformer architecture to enhance urban transportation management.
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