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arXiv Machine Learning By Yue Li, Shujuan Chen, Ying Jin

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

Read the original on arXiv Machine Learning →

The paper introduces a gated graph attention network (GAT) to better explain spatial information flow in short‑term traffic forecasting models. By adding a gate that learns how much of a sensor’s updated state comes from its neighbors versus itself, the authors can progressively withdraw neighbor information and observe its impact on accuracy. Applied to the ST‑MetaNet architecture on data from 498 loop detectors in England, the gated GAT shows that neighbor influence is higher for busy sensors, improves accuracy slightly with mild regularisation, and reveals that the two GAT layers are largely redundant.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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