Web: http://arxiv.org/abs/2206.09112

June 23, 2022, 1:11 a.m. | Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, Christian S. Jensen

cs.LG updates on arXiv.org arxiv.org

We all depend on mobility, and vehicular transportation affects the daily
lives of most of us. Thus, the ability to forecast the state of traffic in a
road network is an important functionality and a challenging task. Traffic data
is often obtained from sensors deployed in a road network. Recent proposals on
spatial-temporal graph neural networks have achieved great progress at modeling
complex spatial-temporal correlations in traffic data, by modeling traffic data
as a diffusion process. However, intuitively, traffic data …

arxiv forecasting graph lg network neural neural network temporal traffic

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