使用门控图注意力网络解释短期交通预测模型中的空间信息流

中文摘要 AI 辅助

短期交通预测支持道路网络的实时监控与管理，图注意力网络（GAT）是这些模型中表示空间依赖性的标准手段。GAT层被广泛描述为捕获邻近位置的影响，但这很少得到验证，因为所提供的注意力权重无法与任何测量量进行比较。这留下了两个问题：模型应如何被解释，以及其哪些组成部分是必要的。我们通过在GAT层中添加一个门来应对这些问题，该门在每个传感器和每个时间步学习传感器更新状态中有多少份额来自其邻居而非其自身。对门进行正则化会逐步撤回邻居信息，从而提供一种分级形式的消融。我们将门控GAT应用于ST-MetaNet，其编码器和解码器各自在两个循环层之间放置一个GAT层，并在英格兰战略道路网络上498个环形检测器的一年日历记录上对其进行训练。该门将更大份额的邻居信息分配给承载更重交通的传感器，并遵循交通的日和周期循环，这与相邻位置在繁忙时耦合更强一致。轻度正则化略微提高了准确性，而在较高强度下，随着惩罚撤回模型所需的信息，准确性下降。编码器门先于解码器门关闭，但直接消融限定了该顺序。单独移除任一GAT层会使准确性至少与保留两者时一样好，而同时移除两者则会大幅降低准确性，因此这两层在很大程度上是冗余的，而非任一者不可或缺。因此，门控GAT带来了适度的准确性提升、对空间信息流在何处及何时流动的解释，以及关于架构需要哪些层的证据。

英文摘要

Short-term traffic forecasting supports real-time monitoring and control of road networks, and graph attention networks (GAT) are the standard means of representing spatial dependence in these models. GAT layers are widely described as capturing the influence of neighbouring locations, but this is seldom verified, because the attention weights offered in support cannot be compared against any measured quantity. That leaves two questions open, how the model should be explained and which of its components are necessary. We address this by adding a gate to the GAT layer which learns, at every sensor and every time step, what share of a sensor's updated state is drawn from its neighbours rather than from itself. Regularising the gate withdraws neighbour information progressively and thereby provides a graded form of ablation. We apply the gated GAT to ST-MetaNet, whose encoder and decoder each place one GAT layer between two recurrent layers, and train it on one calendar year of records from 498 loop detectors on the strategic road network of England. The gate assigns a larger share of neighbour information to sensors carrying heavier traffic and follows the daily and weekly cycle of travel, consistent with adjacent locations being more strongly coupled when busy. Mild regularisation improves accuracy slightly, and accuracy declines at higher strengths as the penalty withdraws information the model needs. The encoder gate closes before the decoder gate, but direct ablation qualifies that ordering. Removing either GAT layer alone leaves accuracy at least as good as keeping both, whereas removing both degrades it substantially, so the two layers are largely redundant rather than either being indispensable. The gated GAT therefore yields a modest accuracy gain, an explanation of where and when spatial information flows, and evidence on which layers the architecture requires.

发表机构

• Martin Centre for Architectural and Urban Studies, University of Cambridge（剑桥大学马丁建筑与城市研究中心）

机构由 AI 辅助整理，请以论文原文为准。