Spatio‐temporal adaptive graph convolutional networks for traffic flow forecasting

Abstract

Accurate forecasting of traffic flow is crucial for intelligent traffic control and guidance. It is very challenging to forecast the traffic flow due to the high non‐linearity, complexity and dynamicity of the data. Most existing forecasting methods focus on designing complicated graph neural network architectures to capture the spatio‐temporal features of traffic data with the help of predefined graphs. However, traffic data exhibit a strong spatial dependency, which means that there are often complex correlations between nodes in a road network topology graph. Moreover, the spatial correlations of the road network change over time. To capture the properties of the road network topology graph, a novel spatio‐temporal adaptive graph convolutional networks model (STAGCN) based on deep learning is proposed. Utilize adaptive graph generation block to capture the static and dynamic structures of the traffic road network respectively, and they are integrated to construct an adaptive road network topology graph. Then the spatio‐temporal features of the traffic data are captured using spatio‐temporal convolution blocks. Experiments were conducted on publicly available traffic datasets for freeway traffic in California, USA, and the results showed that the prediction accuracy of the STAGCN model outperformed multiple baseline methods.

STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction

“…The model-driven approaches mainly depend on mathematical statistics or historical observations in constructing parametric models, which include time-series models, Kalman filtering models, spectral analyses, etc. Many classic models are proposed for traffic-flow prediction, such as the autoregression moving-average model (ARMA) and autoregression integral moving-average model (ARIMA) [18,19]. However, parametric models, which are easily affected by external environmental factors, do not effectively deal with the non-linear issue of traffic.…”

Dynamic Graph Convolution-Based Spatio-Temporal Feature Network for Urban Water Demand Forecasting

“…However, all the aforementioned methods rely on predefined static graphs and thus, lack the ability to model the dynamic correlation of time series. Recently, researchers have been working on dynamic graph generation for two research directions, feature embedding [18][19][20][21] and multi-stage graph training [22], respectively.…”