Traffic flow prediction method based on global diffusion convolution residual network

Abstract

The invention discloses a traffic flow prediction method based on a global diffusion convolution residual network, and belongs to the technical field of intelligent traffic systems. The method comprises the following steps of: 1, establishing a traffic prediction model based on a global diffusion convolution residual network; 2, learning dynamic correlation and local and global spatial correlation; 3, capturing time correlation and global space-time correlation; and 4, fusing branch results and outputting a final result. According to the traffic flow prediction method, a global diffusion convolution residual network is provided, the model is composed of a plurality of periodic branches with the same structure, and the global attention diffusion convolution network and the global residual network of each branch are used for obtaining the spatial-temporal correlation of each period. Particularly, the global attention diffusion convolution network uses a PPMI matrix based on an attentionmechanism to capture dynamic space-time correlation, and the global residual network uses gating convolution and a global residual unit to capture time correlation and global space-time correlation atthe same time, so that the precision and efficiency of traffic prediction are improved.

Description

technical field [0001] A traffic flow prediction method based on a global diffusion convolution residual network belongs to the technical field of intelligent transportation systems. Background technique [0002] Traffic flow prediction is a key problem in intelligent transportation systems. Due to the complex topology of the traffic network and the dynamic spatio-temporal patterns of the traffic situation, the forecasting of the traffic flow in the traffic network is still a challenging task. Most existing research methods mainly focus on the local spatio-temporal correlation, while ignoring the global spatial correlation and the global dynamic spatio-temporal correlation. [0003] Traffic forecasting is a challenging task due to its complex nonlinear dynamic spatio-temporal dependencies. Researchers have put a lot of effort into traffic prediction. Statistical regression methods such as ARIMA and its variants are representative models in early research on traffic foreca...

AI Technical Summary

Problems solved by technology

Although they improve the accuracy and efficiency of traffic forecasting, they fail to simultaneously capture the global and local spatio-temporal correlations in traffic networks

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