用序列建模解决多路口信号灯协同，降低平均通勤时间5-6%。
Spatiotemporal Decision Transformer for Traffic Coordination
- 将交通信号控制转为序列建模，结合图注意力与时间变换器捕捉空间和动态特征。
- 在真实与模拟场景中，平均通勤时间减少5-6%，相邻路口协调性显著提升。
- 支持离线训练与在线微调，适合智能交通系统部署与实际城市应用。
交通信号控制是城市交通的关键挑战，需协调多个交叉口以优化全局交通流。尽管强化学习在自适应信号控制中展现出潜力，现有方法在多智能体协同和样本效率方面仍存在不足。本文提出MADT（多智能体决策变换器），将多智能体交通信号控制重构为序列建模问题。MADT通过引入：(1) 图注意力机制建模交叉口间空间依赖，(2) 时间变换器编码器捕捉交通动态，(3) 未来回报条件化以指定性能目标，扩展了决策变换器范式至多智能体场景。该方法可基于历史交通数据进行离线学习，架构设计支持潜在在线微调。在合成网格网络和真实交通场景中的实验表明，MADT达到当前最优性能，相比最强基线平均通勤时间降低5-6%，且相邻交叉口间的协同效果更优。
原文摘要 · Abstract (English)
Traffic signal control is a critical challenge in urban transportation, requiring coordination among multiple intersections to optimize network-wide traffic flow. While reinforcement learning has shown promise for adaptive signal control, existing methods struggle with multi-agent coordination and sample efficiency. We introduce MADT (Multi-Agent Decision Transformer), a novel approach that reformulates multi-agent traffic signal control as a sequence modeling problem. MADT extends the Decision Transformer paradigm to multi-agent settings by incorporating: (1) a graph attention mechanism for modeling spatial dependencies between intersections, (2) a|temporal transformer encoder for capturing traffic dynamics, and (3) return-to-go conditioning for target performance specification. Our approach enables offline learning from historical traffic data, with architecture design that facilitates potential online fine-tuning. Experiments on synthetic grid networks and real-world traffic scenarios demonstrate that MADT achieves state-of-the-art performance, reducing average travel time by 5-6% compared to the strongest baseline while exhibiting superior coordination among adjacent intersections.
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