Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

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 …

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 …

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

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Understanding the private car aggregation effect is conducive to a broad range of
applications, from intelligent transportation management to urban planning. However, this …

Spatial temporal forecasting plays an important role in improving the quality and
performance of Intelligent Transportation Systems. This task is rather challenging due to the …

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 …

Recent studies have shown great promise in applying graph neural networks for multivariate
time series forecasting, where the interactions of time series are described as a graph …