Short-term traffic flow prediction method based on nerve network combination model

Abstract

The invention provides a short-term traffic flow prediction method based on a nerve network combination model. The method is used to construct a counterpropagation nerve network combination prediction model and the short-term traffic flow prediction method is provided based on the model. Aiming at a characteristic of a traffic flow, a fuzzy C mean value clustering algorithm is used to cluster the traffic flow. For bunch generated through clustering, a counterpropagation nerve network prediction model is constructed. According to grade of membership, a weighted sum of prediction model prediction results is calculated and is taken as a final prediction result. In order to increase prediction precision, a taguchi method is used to carry out test designing so as to test influences of different structure parameters on prediction model prediction precision, and an optimum structure parameter is used as an initial structure of the prediction model. By using the method in the invention, the prediction precision of the short-term traffic flow can be effectively increased, an influence of a noise on the prediction precision in training data is reduced and operation time is reasonable.

Description

technical field [0001] The invention relates to a short-term traffic flow prediction method, which uses a neural network combination model to improve the prediction accuracy of short-term traffic flow and reduces the influence of noise in training data on the prediction accuracy, and belongs to the cross-technical application field of traffic flow and neural network. Background technique [0002] Intelligent transportation system is a real-time, accurate and efficient transportation management system, which effectively integrates advanced information technology, data communication technology, electronic control technology and computer processing technology. The core technology of intelligent transportation system——traffic control technology and traffic guidance technology is the most effective way to solve urban traffic congestion and improve the efficiency of road network traffic, and it is a research hotspot in recent years. The basis for realizing traffic control and traf...

AI Technical Summary

Problems solved by technology

[0008] Technical problem: the purpose of this invention is to provide a kind of short-term traffic flow forecasting method based on neural network combination model, this method solves the prediction accuracy of short-term traffic flow is low, the noise in the training data has a great impact on the prediction accuracy, and the running time is short. Reasonable and other issues

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