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 …
A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting
S Lan, Y Ma, W Huang, W Wang… - … on machine learning, 2022 - proceedings.mlr.press
As a typical problem in time series analysis, traffic flow prediction is one of the most
important application fields of machine learning. However, achieving highly accurate traffic …
important application fields of machine learning. However, achieving highly accurate traffic …
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 …
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 …
Graph neural controlled differential equations for traffic forecasting
Traffic forecasting is one of the most popular spatio-temporal tasks in the field of machine
learning. A prevalent approach in the field is to combine graph convolutional networks and …
learning. A prevalent approach in the field is to combine graph convolutional networks and …
Frequency-domain MLPs are more effective learners in time series forecasting
Time series forecasting has played the key role in different industrial, including finance,
traffic, energy, and healthcare domains. While existing literatures have designed many …
traffic, energy, and healthcare domains. While existing literatures have designed many …
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 …
Scinet: Time series modeling and forecasting with sample convolution and interaction
One unique property of time series is that the temporal relations are largely preserved after
downsampling into two sub-sequences. By taking advantage of this property, we propose a …
downsampling into two sub-sequences. By taking advantage of this property, we propose a …
Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting
Dynamic Graph Neural Networks (DGNNs) have become one of the most promising
methods for traffic speed forecasting. However, when adapting DGNNs for traffic speed …
methods for traffic speed forecasting. However, when adapting DGNNs for traffic speed …