Temporal traffic prediction is critical for ITS yet remains challenging in handling complex
spatio-temporal dynamics of traffic systems. The continuous traffic data (eg, traffic flow, and …

Graph learning-based algorithms are becoming the prevalent traffic prediction solutions due
to their capability of exploiting non-Euclidean spatial-temporal traffic data correlation …

Traffic networks exhibit complex spatial-temporal dependencies, and accurately capturing
such dependencies is critical to improving prediction accuracy. Recently, many deep …

Traffic prediction is an important and yet highly challenging problem due to the complexity
and constantly changing nature of traffic systems. To address the challenges, we propose a …

Accurate and efficient traffic prediction is the key to the realization of intelligent transportation
system (ITS), which helps to alleviate traffic congestion and reduce traffic accidents. Due to …

Accurate and reliable traffic flow prediction is critical to the safe and stable deployment of
intelligent transportation systems. However, it is very challenging since the complex spatial …

Recent spatial-temporal graph-based deep learning methods for Traffic Flow Prediction
(TFP) problems have shown superior performance in modeling higher-level spatial …

Traffic prediction is a popular research topic in the field of Intelligent Transportation System
(ITS), as it can allocate resources more reasonably, relieve traffic congestion, and improve …

Accurate traffic prediction plays an important role in Intelligent Transportation System. This
problem is very challenging due to the heterogeneity and dynamic spatio-temporal …

Spatiotemporal graph neural networks (GNNs) have been used successfully in traffic
prediction in recent years, primarily owing to their ability to model complex spatiotemporal …