Nature

Integrated spatio-temporal modeling with hybrid graph convolutions and the graph fourier neural operator for traffic prediction

The rapid growth of smart cities and intelligent transportation systems (ITS) has intensified the demand for accurate and scalable traffic forecasting models, particularly for long-term prediction ...
Nature

Spatial–temporal combination and multi-head flow-attention network for traffic flow prediction

Intelligent Transportation System (ITS) 1 plays an important role in road traffic management. It combines some advanced science and technology such as information technology and sensor technology 2, ...
EurekAlert!

Heterogeneous spatio-temporal graph contrastive learning for point-of-interest recommendation

As one of the most crucial topics in the recommendation system field, Point-of-Interest (POI) recommendation aims to recommending potential interesting POIs to users. Recently, graph neural networks ...