The rapid urbanization and continuous improvement of road traffic equipment result in
massive daily production of traffic data. These data contain the long-term evolution of traffic …

Recently, spatiotemporal graph convolutional networks are becoming popular in the field of
traffic flow prediction and significantly improve prediction accuracy. However, the majority of …

Multivariate time series (MTS) prediction has been widely adopted in various scenarios.
Recently, some methods have employed patching to enhance local semantics and improve …

Accurate traffic prediction plays a vital role in intelligent transport managements and
applications. However, in the vast majority of existing works, the focus is mainly on modeling …

Human mobility forecasting is the key to energizing considerable mobile computing
services. However, we find that the collective mobility suffers the spatiotemporal …

With the progress of urban transportation systems, a significant amount of high-quality traffic
data is continuously collected through streaming manners, which has propelled the …

Traffic patterns of urban road intersections are important in traffic monitoring and accident
prediction, thus play crucial roles in urban traffic management. Although real-time traffic …

Time series forecasting (TSF) plays a pivotal role in many real-world applications. Recently,
the utilization of Large Language Models (LLM) in TSF has demonstrated exceptional …

Traffic forecasting is a challenging research topic due to the complex spatial and temporal
dependencies among different roads. Though great efforts have been made on traffic …

Time series forecasting facilitates various real-world applications and has attracted great
research interests. In real-world scenarios, time series forecasting models confront a …