零样本适配任意道路网络的交通信号控制框架。
TransferLight: Zero-Shot Traffic Signal Control on any Road-Network
- 基于对数距离奖励函数实现空间感知的信号优先级调度。
- 单个模型零样本迁移至未知路网，无需重新训练。
- 适合城市交通系统部署与跨场景智能控制研究者。
交通信号控制对城市出行至关重要。现有方法常难以泛化到训练环境之外的未见场景，尤其在交通动态变化、道路布局多样时表现不佳。我们提出 TransferLight，一个面向任意路网、复杂交通条件和交叉口几何结构的鲁棒泛化框架。核心是提出对数距离奖励函数，在保持对车道配置适应性的同时，实现空间感知的信号优先调度，克服传统压力型奖励的局限。采用分层、异构、有向图神经网络架构，精准捕捉细粒度交通动态，支持任意交叉口布局的迁移能力。结合去中心化多智能体机制、全局奖励设计及新颖的状态转移先验，构建单一权重共享策略，实现零样本扩展至任意道路网络而无需再训练。训练中引入领域随机化进一步提升泛化性能。实验验证其在未见场景中的优越表现，推动可实际部署的通用智能交通系统发展，以应对不断变化的城市交通需求。
原文摘要 · Abstract (English)
Traffic signal control plays a crucial role in urban mobility. However, existing methods often struggle to generalize beyond their training environments to unseen scenarios with varying traffic dynamics. We present TransferLight, a novel framework designed for robust generalization across road-networks, diverse traffic conditions and intersection geometries. At its core, we propose a log-distance reward function, offering spatially-aware signal prioritization while remaining adaptable to varied lane configurations - overcoming the limitations of traditional pressure-based rewards. Our hierarchical, heterogeneous, and directed graph neural network architecture effectively captures granular traffic dynamics, enabling transferability to arbitrary intersection layouts. Using a decentralized multi-agent approach, global rewards, and novel state transition priors, we develop a single, weight-tied policy that scales zero-shot to any road network without re-training. Through domain randomization during training, we additionally enhance generalization capabilities. Experimental results validate TransferLight's superior performance in unseen scenarios, advancing practical, generalizable intelligent transportation systems to meet evolving urban traffic demands.
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