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

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …

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 …

Dynamic Graph Neural Networks (DGNNs) have become one of the most promising
methods for traffic speed forecasting. However, when adapting DGNNs for traffic speed …

With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …

Region-level demand forecasting is an essential task in ridehailing services. Accurate ride-
hailing demand forecasting can guide vehicle dispatching, improve vehicle utilization …

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 plays an essential role in intelligent transportation system. Accurate traffic
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …

With the development of modern Intelligent Transportation System (ITS), reliable and
efficient transportation information sharing becomes more and more important. Although …

Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and
motion planning of autonomous vehicles. This paper proposes a graph-based spatial …