Orchestrating Reasoning and Reaction: An Asynchronous Hierarchical Framework for LLM-driven Traffic Signal Control

Abstract

Network-wide coordinated Traffic Signal Control (TSC) is critical for enhancing urban mobility. However, existing approaches face a fundamental trade-off: traditional Multi-Agent Reinforcement Learning (MARL) is often hindered by a myopic observational scope, while Large Language Model (LLM) agents are constrained by high inference costs and spatial-topological hallucinations. To address these limitations, we present Astra, an Asynchronous Synergistic Traffic Regulation Architecture that decouples high-level strategic reasoning from reactive execution through a three-layer hierarchy. Specifically, the Macro layer performs low-frequency strategic inference to identify global bottlenecks, which are then partitioned into regional congestion subgraphs. The Meso layer employs a topological semantic causal logic mechanism to ground LLM-based coordination in physical reality via semantic primitives. Simultaneously, the Micro layer governs the broader network using decentralized MARL agents optimized with spatial attention and auxiliary prediction for robust, high-frequency execution. To bridge the gap between reasoning depth and real-time constraints, Astra incorporates an asynchronous synergetic protocol featuring strategic locking for stability and proactive feedback for adaptive re-planning. Extensive evaluations on real-world datasets demonstrate that Astra consistently outperforms state-of-the-art baseline methods, offering superior efficiency, robustness, and generalization across diverse urban scenarios.