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...

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Problems solved by technology

Although they improve the accuracy and efficiency of traffic forecasting, they fail to si

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