Traffic flow forecasting method based on deep graph gaussian processes

A traffic flow forecasting method based on deep graph Gaussian processes, comprising the following steps:

• S1, with respect to the dynamics existing in a spatial dependency, using an attention kernel function to describe a dynamic dependency among vertices on a topological graph, and using the attention kernel function as a covariance function in an aggregation Gaussian process to extract dynamic spatial features;
• S2, obtaining a temporal convolutional Gaussian process from weights at different times and a convolution function obeying Gaussian processes, and obtaining temporal features in traffic data by combining the aggregation Gaussian process;
• S3, constructing a deep graph Gaussian process method integrating a Gaussian process and a depth structure from the aggregation Gaussian process, the temporal convolutional Gaussian process and the Gaussian process with a linear kernel function, inputting a data sample to be forecasted into the deep graph Gaussian process method, extracting the spatial dependency by the aggregation Gaussian process in step Si, then obtaining the spatiotemporal features by the convolution function in step S2, and inputting the spatiotemporal features into the Gaussian process with the linear kernel function to obtain a forecasted result.