arXiv Machine Learning June 9, 2026 By Soban Nasir Lone, Mohamed Abouelela, Taeyoung Yu, Jiwon Kim, Constantinos Antoniou

Efficient Traffic Prediction at Scale: A Systematic Study of STGCN Architectural Depth

Read the original on arXiv Machine Learning →

arXiv:2606. 09539v1 Announce Type: new Abstract: Spatio-temporal graph neural networks (STGNNs) have become the dominant approach for traffic prediction, yet their computational requirements pose challenges for practical deployment in intelligent transportation systems (ITS).

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