A Gaussian Temporal Based Graph Convolutional Network for Traffic Operation Flow Forecasting

High-Quality traffic flow data may provide planners with a basis for designing road capacity, pavement and intersection control, etc., thus assisting in the construction of a more rational traffic network. This study developed a new Gaussian Temporal Network Module, which is a module based on a Gaussian process that utilizes a kernel Gaussian convolution kernel to assist in the extraction of temporal features. Which is aim to solve the common gradient vanishing and gradient explosion problems in temporal neural networks Further, this study combines the GTNM with the GCN module as a GT-GCN model and tests its performance on a publicly available dataset. Experiment results show that GT-GCN demonstrates superior performance, attaining state-of-the-art or second best outcomes across different test dataset. Several of these results surpass baseline benchmarks by more than 10%, which then well illustrates the effectiveness and stability of GT-GCN model.