Graph neural network for traffic forecasting: A survey
Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …
learning models, including convolution neural networks and recurrent neural networks, have …
Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …
critical problem globally, resulting in negative consequences such as lost hours of additional …
Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction
As a core technology of Intelligent Transportation System, traffic flow prediction has a wide
range of applications. The fundamental challenge in traffic flow prediction is to effectively …
range of applications. The fundamental challenge in traffic flow prediction is to effectively …
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Pre-training enhanced spatial-temporal graph neural network for multivariate time series forecasting
Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications.
Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly …
Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly …
Spatial-temporal fusion graph neural networks for traffic flow forecasting
Spatial-temporal data forecasting of traffic flow is a challenging task because of complicated
spatial dependencies and dynamical trends of temporal pattern between different roads …
spatial dependencies and dynamical trends of temporal pattern between different roads …
Decoupled dynamic spatial-temporal graph neural network for traffic forecasting
We all depend on mobility, and vehicular transportation affects the daily lives of most of us.
Thus, the ability to forecast the state of traffic in a road network is an important functionality …
Thus, the ability to forecast the state of traffic in a road network is an important functionality …
Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution
Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
A survey of traffic prediction: from spatio-temporal data to intelligent transportation
Intelligent transportation (eg, intelligent traffic light) makes our travel more convenient and
efficient. With the development of mobile Internet and position technologies, it is reasonable …
efficient. With the development of mobile Internet and position technologies, it is reasonable …
Spatio-temporal meta-graph learning for traffic forecasting
Traffic forecasting as a canonical task of multivariate time series forecasting has been a
significant research topic in AI community. To address the spatio-temporal heterogeneity …
significant research topic in AI community. To address the spatio-temporal heterogeneity …