Traffic flow prediction method based on GMDH neural network

Abstract

The present invention relates to a traffic flow prediction method based on a GMDH neural network. The method comprises GMDH neural network offline traffic flow training and GMDH neural network onlinetraffic flow real-time prediction. The method provided by the invention employs a GMDH neural network algorithm to perform prediction of traffic flow at a traffic intersection, a general method is long in time in processing process of huge data and low in accuracy and is difficult to achieve requirements of real-time prediction of traffic flow; and the GMDH neural network online has a good approximation capability to divide the prediction of the traffic flow into two parts consisting of offline learning and online prediction, wherein the offline learning link combines a lot of data to performtraining of the neural network and learn the rule of traffic flow change, and the online prediction part calls the neural network which has complete learning to rapidly and effectively perform real-time prediction of the pass states of vehicles.

Description

technical field [0001] The invention relates to a traffic vehicle flow prediction method, in particular to a traffic vehicle flow prediction method based on a GMDH neural network. Background technique [0002] Most of the existing traffic flow forecasting methods use time series models, or use linear models similar to time series models. This type of method regards the traffic flow at a certain moment as a non-stationary random sequence, and analyzes and calculates it in the dimension of time. Take methods based on time series models as an example. This type of method is based on a large amount of uninterrupted data and has high prediction accuracy, but requires complex parameter estimation, and the calculated parameters are not portable. In practical applications, due to various reasons, it is easy to cause data omission, which can easily lead to a reduction in the prediction accuracy of the model. In addition, it also relies on a large amount of historical data, and the d...

AI Technical Summary

Problems solved by technology

For example, take the method based on time series model as an example. This type of method is based on a large amount of uninterrupted data and has high prediction accuracy, but requires complex parameter estimation, and the calculated parameters are not portable.

In practical applications, due to various reasons, it is easy to cause data omission, which can easily lead to a reduction in the prediction accuracy of the model. In addition, it also relies on a large amount of historical data, and the data cost is relatively high.

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