Traffic forecasting has been regarded as the basis for many intelligent transportation system
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …

Spatio-temporal prediction tasks play a crucial role in facilitating informed decision-making
through anticipatory insights. By accurately predicting future outcomes, the ability to …

Fine-grained traffic forecasting is crucial for the management of urban transportation
systems. Road segments and intersection turns, as vital elements of road networks, exhibit …

Abstract Graph Convolutional Networks (GCNs), which can model data in non-Euclidean
space, have received extensive attention in multivariate traffic flow forecasting in recent …

Traffic flow prediction is of paramount importance in the field of spatio-temporal forecasting.
In recent years, research efforts have primarily been directed towards developing intricate …

Accurate remaining useful life (RUL) prediction of rolling bearings plays a vital role in
ensuring the safe operation of mechanical equipment. Graph-based models have become …

Accurate and fine-grained traffic state prediction has always been an important research
field. For long-term traffic flow prediction, the high-dimensional and coupled traffic feature …

Accurate traffic forecasting is a fundamental problem in intelligent transportation systems
and learning long-range traffic representations with key information through spatiotemporal …

Short-term passenger flow prediction (STPFP) helps ease traffic congestion and optimize
urban rail transit (URT) system resource allocation. Although graph-based models have …

Traffic flow forecasting on a large-scale sensor network is of great practical significance for
policy decision-making, urban management, and transport planning. Recently, several …