Predicting Traffic Flow Using Attention and Spatiotemporal Graph Convolutional Networks
TL;DR
A prediction model that incorporates multiple attention mechanisms with spatiotemporal graph convolutional networks (HASTGCN) and designs a spatiotemporal map convolution module to collaboratively model the dynamic spatiotemporal connection of traffic flow collaboratively model is used.
Abstract
: With the progress of the system of intelligent transportation, traffic flow forecasting is essential to achieve efficient traffic management and control. It can not only optimize real-time traffic flow and improve travel efficiency, but also provide a basis for long-term road network planning and reduce carbon emissions. However, present models are unable to acquire the complicated spatio-temporal dependency of data, leading to low prediction accuracy. Therefore, this research uses a prediction model that incorporates multiple attention mechanisms with spatiotemporal graph convolutional networks (HASTGCN). This model combines channel attention, spatial attention, and temporal attention mechanisms, and designs a spatiotemporal map convolution module to collaboratively model the dynamic spatiotemporal connection of traffic flow collaboratively model. The trial results display the performance of HASTGCN on the PEMS04 dataset (Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) indicators) performed better than other models which proving its effectiveness in capturing spatiotemporal characteristics and improving prediction accuracy.