Spatial-temporal feature variational inference model for traffic flow forecasting
OUYANG Yi
TANG Wen-yan
SHAO Yong-bo
LI Yan-ling
Abstract：Traffic flow spatial-temporal data forecasting is a crucial task for intelligent transportation systems.This paper proposes a variational learning model based on the spatio-temporal feature fusion(ST-FVAE).The model aims to address the nonlinear and multi-modal features of urban traffic flow sequences by utilizing local spatiotemporal feature fusion and global feature fusion.It also takes into account the graph spatial topological characteristics to predict traffic flow data.The local feature fusion module is composed of a temporal convolutional residual unit and a graph convolutional neural network model(GCN).It extracts the local temporal feature information of traffic nodes and uses GCN to embed the spatial topological information into the local temporal feature information.We learn global spatial-temporal correlation features via this variational auto-encoder traffic flow prediction model of local spatial-temporal graph feature fusion.During the learning process of global spatio-temporal graph feature fusion variational auto-encoder,to make the variational Q distribution approximate the actual data P distribution,we use the variational inference ELBO(evidence lower bound)that maximizes the likelihood function to minimize the KL scatter between the two distributions.Meanwhile,we construct the training loss function using the KL function property.We perform prediction evaluation on three different large-scale traffic datasets.Experiments show that the model proposed in this paper has better prediction performance in both traffic flow and speed.Moreover,our method is more robust for 30 and 60-minute forecasting.
Keywords：traffic flow predictionspatio-temporal fusionvariational autoencodergraph convolution
Publication Date：2025-01-27
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 158-166 )