Explaining spatial information flow in short-term traffic forecasting models using a gated graph attention network

Abstract: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.

Machine Learning (cs.LG)

arXiv:2609.20217 [cs.LG]

(or arXiv:2609.20217v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.20217 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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