Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …

With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …

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

Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical
Systems) technologies, big spatiotemporal data are being generated from mobile phones …

Traffic flow forecasting is indispensable in today's society and regarded as a key problem for
Intelligent Transportation Systems (ITS), as emergency delays in vehicles can cause serious …

Accurate spatial-temporal prediction is a fundamental building block of many real-world
applications such as traffic scheduling and management, environment policy making, and …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

Traffic prediction plays an essential role in many real-world applications ranging from route
planning to vehicular communications. The goal of making accurate prediction is …

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

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 …