Spatiotemporal Decision Transformer for Traffic Coordination

• URL: http://arxiv.org/abs/2602.02903v1
• Date: Mon, 02 Feb 2026 23:19:13 GMT
• Title: Spatiotemporal Decision Transformer for Traffic Coordination
• Authors: Haoran Su, Yandong Sun, Hanxiao Deng,
• Abstract summary: MADT (Multi-Agent Decision Transformer) is a novel approach that reformulates multi-agent traffic signal control as a sequence modeling problem.<n>Our approach enables offline learning from historical traffic data, with architecture design that facilitates potential online fine-tuning.
• Score: 1.2099551931618155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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