Traffic prediction is a fundamental and vital task in Intelligence Transportation System (ITS),
but it is very challenging to get high accuracy while containing low computational complexity …

Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …

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 …

Traffic prediction is a core problem in the intelligent transportation system and has broad
applications in the transportation management and planning, and the main challenge of this …

Traffic flow analysis, prediction and management are keystones for building smart cities in
the new era. With the help of deep neural networks and big traffic data, we can better …

Traffic prediction is an essential task in reducing traffic congestions and improving
transportation. However, this task is challenging due to the complex spatio-temporal …

Traffic prediction is a challenging topic in urban traffic construction and management due to
its complex dynamic spatial–temporal correlations. Currently, graph neural network …

Accurate urban traffic prediction is a critical issue in Intelligent Transportation Systems (ITS).
It is challenging since urban traffic usually indicates high dynamic spatio-temporal …

With well-defined graphs, graph convolution based spatiotemporal neural networks for traffic
prediction have achieved great performance in numerous tasks. Compared to other …

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