As a fundamental spatiotemporal sequence forecasting problem, traffic prediction is pivotal
in transportation management and urban computing. Nonetheless, the intricate and dynamic …

Graph neural networks (GNNs), especially dynamic GNNs, have become a research hotspot
in spatiotemporal forecasting problems. While many dynamic graph construction methods …

Traffic flow forecasting is crucial for making appropriate route guidance and vehicle
scheduling schemes in intelligent transportation systems. However, recent graph-based …

Traffic flow prediction is a critical component of Intelligent Transportation Systems (ITS).
However, the dynamic temporal variations in traffic flow, especially in potential occurrence of …

Predicting traffic flow represents a critical undertaking within the domain of Intelligent
Transportation Systems (ITS), given its pivotal role in optimizing traffic management …

Traffic prediction is an important topic in intelligent transportation systems (ITSs) that can
provide support for many traffic applications. However, accurate traffic prediction is a …

The rapid expansion of dockless bicycle-sharing systems has presented challenges in
efficiently scheduling bicycles due to their uneven distribution across time and space. The …

Traffic flow prediction plays a crucial role in the management and operation of urban
transportation systems. While extensive research has been conducted on predictions for …

While the increased availability of traffic data is allowing us to better understand urban
mobility, research on data-driven and predictive modeling is also providing new methods for …

As a core technology of Intelligent Transportation System (ITS), traffic flow prediction has a
wide range of applications. Traffic flow data are spatial-temporal, which are not only …