Deep learning has achieved remarkable success in graph-related tasks, yet this
accomplishment heavily relies on large-scale high-quality annotated datasets. However …

Spatio-temporal feature extraction and fusion are crucial to traffic prediction accuracy.
However, the complicated spatio-temporal correlations and dependencies between traffic …

Traffic forecasting is an important task for transportation engineering as it helps authorities to
plan and control traffic flow, detect congestion, and reduce environmental impact. Deep …

Multivariate time series (MTS) forecasting has attracted much attention in many intelligent
applications. It is not a trivial task, as we need to consider both intra-variable dependencies …

Traffic flow prediction is a crucial research area in traffic management. Accurately predicting
traffic flow in each area of the city over the long term can enable city managers to make …

Correlated time series (CTS) forecasting plays an essential role in many cyber-physical
systems, where multiple sensors emit time series that capture interconnected processes …

Traffic speed prediction is a crucial task of the intelligent traffic system. However, due to the
highly nonlinear temporal patterns and non-static spatial dependence of traffic data, timely …

Traffic prediction has been an active research topic in the domain of spatial-temporal data
mining. Accurate real-time traffic prediction is essential to improve the safety, stability, and …

Traffic congestion has a negative impact on our daily life. Predicting the trend of traffic
congestion can provide a valuable guideline to address such problems. Most existing …

Traffic forecasting is a challenging task due to complex spatial and temporal dependencies
across sensor locations and time. Interest in solving this task has increased, but current …