Method, device and system for traffic flow prediction based on Bat algorithm

Abstract

The embodiment of the invention discloses a method, device and system for traffic flow prediction based on the Bat algorithm. The method comprises: obtaining traffic flow data; employing a wavelets neural network traffic flow prediction model established in advance to perform processing and obtain a traffic flow prediction result, wherein the wavelets neural network traffic flow prediction model is formed through training based on the Bat algorithm, and the training process comprises calculation of initialization wavelets neural network parameters according to the historical data and the Bat algorithm; and employing the wavelets neural network and the historical data to perform training of the initialization wavelets neural network parameters to obtain the wavelets neural network traffic flow prediction model. Therefore, according to the embodiment of the invention, when the wavelets neural network traffic flow prediction model trained through adoption of the initialization wavelets neural network parameters obtained based on the Bat algorithm is used for prediction of the traffic flow, the prediction speed and the prediction precision are improved to a certain extent.

Description

A traffic flow prediction method, device and system based on bat algorithm technical field 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 bat algorithm. Background technique 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 fl...

AI Technical Summary

Problems solved by technology

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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