June 6, 2024, 4:42 a.m. | Sanghyun Lee, Chanyoung Park

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02726v1 Announce Type: new
Abstract: Accurate traffic flow forecasting is a crucial research topic in transportation management. However, it is a challenging problem due to rapidly changing traffic conditions, high nonlinearity of traffic flow, and complex spatial and temporal correlations of road networks. Most existing studies either try to capture the spatial dependencies between roads using the same semantic graph over different time steps, or assume all sensors on the roads are equally likely to be connected regardless of the …

abstract arxiv correlations cs.lg flow forecasting graph graph learning however management network networks neural network problem recurrent neural network research spatial studies temporal traffic transportation type

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