Multi-view dynamic graph convolution neural network for traffic flow prediction
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 …
massive daily production of traffic data. These data contain the long-term evolution of traffic …
Pattern expansion and consolidation on evolving graphs for continual traffic prediction
Recently, spatiotemporal graph convolutional networks are becoming popular in the field of
traffic flow prediction and significantly improve prediction accuracy. However, the majority of …
traffic flow prediction and significantly improve prediction accuracy. However, the majority of …
Hdmixer: Hierarchical dependency with extendable patch for multivariate time series forecasting
Multivariate time series (MTS) prediction has been widely adopted in various scenarios.
Recently, some methods have employed patching to enhance local semantics and improve …
Recently, some methods have employed patching to enhance local semantics and improve …
Knowledge expansion and consolidation for continual traffic prediction with expanding graphs
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 …
applications. However, in the vast majority of existing works, the focus is mainly on modeling …
Predicting collective human mobility via countering spatiotemporal heterogeneity
Human mobility forecasting is the key to energizing considerable mobile computing
services. However, we find that the collective mobility suffers the spatiotemporal …
services. However, we find that the collective mobility suffers the spatiotemporal …
Towards Dynamic Spatial-Temporal Graph Learning: A Decoupled Perspective
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 …
data is continuously collected through streaming manners, which has propelled the …
Inferring intersection traffic patterns with sparse video surveillance information: An st-gan method
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 …
prediction, thus play crucial roles in urban traffic management. Although real-time traffic …
[PDF][PDF] Leret: Language-empowered retentive network for time series forecasting
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 …
the utilization of Large Language Models (LLM) in TSF has demonstrated exceptional …
Adaptive and Interactive Multi-Level Spatio-Temporal Network for Traffic Forecasting
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 …
dependencies among different roads. Though great efforts have been made on traffic …
Meta Koopman decomposition for time series forecasting under temporal distribution shifts
Time series forecasting facilitates various real-world applications and has attracted great
research interests. In real-world scenarios, time series forecasting models confront a …
research interests. In real-world scenarios, time series forecasting models confront a …