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 …

Traffic flow prediction is an important part of the intelligent transportation system. Accurate
traffic flow prediction is of great significance for strengthening urban management and …

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

Graph convolutional networks (GCN) have been applied in the traffic flow forecasting tasks
with the graph capability in describing the irregular topology structures of road networks …

Traffic forecasting is an important prerequisite for the application of intelligent transportation
systems in urban traffic networks. The existing works adopted RNN and CNN/GCN, among …

In most recent research, the traffic forecasting task is typically formulated as a spatio-
temporal graph modeling problem. For spatial correlation, they typically learn the shared …

In traffic systems, traffic forecasting is a critical issue, which has attracted much interest from
researchers. It is a challenging task due to the complex spatial‐temporal patterns of traffic …

Traffic speed prediction, as one of the most important topics in Intelligent Transport Systems
(ITS), has been investigated thoroughly in the literature. Nonetheless, traditional methods …

Traffic forecasting has been regarded as the basis for many intelligent transportation system
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …

Traffic flow forecasting is crucial for making appropriate route guidance and vehicle
scheduling schemes in intelligent transportation systems. However, recent graph-based …