用图神经网络为复杂路网设计可迁移的信号控制接口
A Graph-Based Control Interface for Traffic Signals on Heterogeneous Road Networks
- 用图神经网络给每个车流路径打分，生成自适应信号方案
- 同一策略在5个真实城市路网中运行，表现各异但稳定有效
- 首次实现跨不同路网结构的统一信号控制接口，适合交通智能调度研究者
我们提出一种基于图神经网络的交通信号控制接口，共享的图神经网络为每个交通流分配评分。每个交叉口利用确定性关联矩阵将这些评分转化为自身变量大小的合法信号相位集合。定向走廊节点提供交通上下文，移动节点代表路口中的输入到输出路径。类型化均值聚合为每个移动产生一个标量；相位定义和信号时序不在学习网络内。这使得图的大小和路口特定动作数量与学习参数形状无关。使用PPO在未见过的合成网格几何、信号覆盖变化及五个异构城市图上评估该接口。策略在合成网格家族的未见几何中保持性能，而信号覆盖变化暴露了对信号覆盖率分布偏移的敏感性。单一训练的城市策略实例在所有五个城市图上执行，结果具有异质性。这些结果提供了可行性证据，而非对任意道路网络迁移能力的一般估计。
原文摘要 · Abstract (English)
We present a traffic-signal control interface in which a shared graph neural network assigns scores to individual traffic movements. Each junction converts these scores into its own variable-sized set of legal signal phases using a deterministic incidence matrix. Directed corridor nodes provide traffic context, while movement nodes represent controlled input-to-output paths through junctions. Typed mean aggregation produces one scalar per movement; phase definitions and signal timing remain outside the learned network. This makes graph size and junction-specific action count independent of the learned parameter shapes. PPO experiments evaluate the interface on unseen synthetic grid geometries, altered signal coverage, and five heterogeneous city graphs. The policies retained performance across unseen geometries within the synthetic grid family, while changes in signal coverage exposed sensitivity to a signal-coverage distribution shift. A single trained city-policy instance executed across all five city graphs, with heterogeneous outcomes. These results provide feasibility evidence rather than a general estimate of transfer to arbitrary road networks.
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