As one of the most significant components of Intelligent Transportation Systems (ITS), traffic
prediction has gained much popularity given its enormous application value in vehicular …

Recently, Graph Convolution Network (GCN) and Temporal Convolution Network (TCN) are
introduced into traffic prediction and achieve state-of-the-art performance due to their good …

Traffic prediction methods on a single-source data have achieved excellent results in recent
years, especially the Graph Convolutional Networks (GCN) based models with spatio …

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 …

Accurately obtaining information of road traffic conditions is of great significance to people's
travel planning and arrangement of social shared resources, and has become a major …

With the rapid development of urbanization, increasingly more data are being acquired by
intelligent transportation systems (ITSs), which is of great significance for traffic flow …

Robust and accurate traffic forecasting is a key issue in intelligent transportation systems.
Existing studies usually employ pre-defined spatial graph or learned fixed adjacency graph …

Traffic prediction is an important part of building a smart city. Reasonable traffic prediction
can help the relevant departments to make important decisions and help people to plan their …

Traffic prediction facilitates various applications in the fields of smart vehicles and vehicular
communications, and the key of successfully and accurately forecasting urban traffic state is …