Traffic data prediction method and system

Abstract

The invention discloses a traffic speed prediction method. The method comprises the following steps of acquiring a data set including road network information and vehicle speed information; extracting spatial correlation of K-order adjacent road sections in the road network by using a graph attention mechanism, and outputting a vehicle speed feature matrix V (K) of each road section; and using a time convolution network to learn a time rule of V(K), outputting a time sequence feature matrix, using a multi-head self-attention mechanism to learn a coupling relation between the feature matrixes, and outputting the coupling relation. According to the invention, the graph attention mechanism and the time convolution network are used to extract space dependence and time dependence; and the multi-head self-attention mechanism is introduced to extract a space-time coupling effect, and the multi-head self-attention mechanism can learn information from different representation subspaces at different positions so that the time information of a traffic flow can be captured, and higher prediction precision can be realized.

Description

technical field [0001] The invention is suitable for the technical field of intelligent transportation and data processing, and in particular relates to a traffic data prediction method and system. Background technique [0002] Traffic condition prediction is an essential component in traffic modeling, operation and management. Unreasonable guidance when the traffic flow is large may lead to problems such as traffic jams and traffic accidents. High-accuracy traffic speed prediction plays an important role in intelligent transportation system (ITS) applications, such as alleviating urban traffic problems, improving urban transportation efficiency, helping travelers make better route planning and departure timing, dynamic traffic signals optimization and ramp control, etc. The methods of traffic speed prediction can be mainly divided into two categories: classical statistical models and machine learning methods. [0003] Most classical statistical models are based on some a...

AI Technical Summary

Problems solved by technology

Use the cyclic neural network for timing prediction, but the cyclic neural network may forget some important information when learning a long sequence, and it cannot be calculated in parallel

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