Due to prediction on the traffic flow is influenced by the real environment and historical data,
the produced traffic graph may include significant uncertainty. The graph convolution …

Accurate traffic flow prediction is crucial for the development of intelligent transportation. It
can not only effectively avoid traffic congestion and other traffic problems, but also provide a …

Traffic flow prediction is one of the essential technologies in the intelligent transportation
system. Graph convolution is naturally suitable for graph structure data mining and is widely …

Traffic flow prediction remains an ongoing hot topic in the field of Intelligent Transportation
System. The state-of-the-art traffic flow prediction models can effectively extract both spatial …

The spatial–temporal prediction of traffic flow is very important for traffic management and
planning. The most difficult challenges of traffic flow prediction are the temporal feature …

Short-term traffic flow prediction is a core branch of intelligent traffic systems (ITS) and plays
an important role in traffic management. The graph convolution network (GCN) is widely …

Exploiting deep learning techniques for traffic flow prediction has become increasingly
widespread. Most existing studies combine CNN or GCN with recurrent neural network to …

Traffic prediction is significant for transportation management and travel route planning, and
it is challenging as the spatial dependencies are complex and temporal patterns are …

The complex traffic network spatial correlation and the characteristic of high nonlinear and
dynamic traffic conditions in the time are the challenges to accurate traffic flow forecasting …

In the field of traffic forecasting, methods based on Graph Convolutional Network (GCN) are
emerging. But existing methods still have limitations due to insufficient sharing patterns …