Traffic-flow forecasting method, device and system based on wolf-pack algorithm

Abstract

The embodiment of the invention discloses a traffic-flow forecasting method, device and system based on the wolf-pack algorithm. The traffic-flow forecasting method includes the steps that traffic-flow data is obtained; the traffic-flow data is processed through a pre-established wavelet-neural-network traffic-flow forecasting model to obtain the traffic-flow forecasting result, wherein the wavelet-neural-network traffic-flow forecasting model is trained based on the wolf-pack algorithm, and the training process of the pre-established wavelet-neural-network traffic-flow forecasting model is that an initialized wavelet-neural-network parameter is calculated according to historical data and the wolf-pack algorithm; the initialized wavelet-neural-network parameter is trained through a wavelet neural network and the historical data to obtain the wavelet-neural-network traffic-flow forecasting model. According to the traffic-flow forecasting method, device and system based on the wolf-pack algorithm in the embodiment, when traffic flow is forecasted through the wavelet-neural-network traffic-flow forecasting model trained through the initialized wavelet-neural-network parameter obtained based on the wolf-pack algorithm, the forecasting speed and the forecasting accuracy are increased to a certain degree.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of road traffic, in particular to a traffic flow prediction method, device and system based on wolf pack algorithm. Background technique [0002] When predicting road traffic flow, it is usually affected by factors such as road conditions, time points, weather changes, etc., resulting in high uncertainty in road traffic flow data, and the law is not obvious. In the prior art, the traditional wavelet neural network method is used to train the network parameters of the wavelet neural network when the traffic flow of the road is predicted. The same gradient descent method for the network, and the gradient descent method is unidirectional, and the relevant network parameters are randomly generated, so that the network parameters are extremely easy to fall into the local minimum during the optimization process, so that the traffic flow prediction speed and prediction accuracy reduce. ...

AI Technical Summary

Problems solved by technology

[0002] When predicting road traffic flow, it is usually affected by factors such as road conditions, time points, weather changes, etc., resulting in high uncertainty in road traffic flow data, and the law is not obvious

In the prior art, the traditional wavelet neural network method is used to train the network parameters of the wavelet neural network when the traffic flow of the road is predicted. The same gradient descent method for the network, and the gradient descent method is unidirectional, and the relevant network parameters are randomly generated, so that the network parameters are extremely easy to fall into the local minimum during the optimization process, so that the traffic flow prediction speed and prediction accuracy reduce

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