Short-term Traffic Prediction Based on Genetic Improved Wavelet Neural Network

Abstract

Efficient and accurate traffic prediction is the premise of the development of autonomous driving technology. In-depth research is made on the issue of short-term traffic speed prediction in autonomous driving systems. In view of the time-varying characteristics of the traffic main sentence, this paper designs and implements a traffic prediction system based on genetically improved wavelet neural networks. Through the training and learning of the historical average speed data of roads, it realizes the prediction of future road traffic conditions and helps the planning of travel routes. This algorithm circumvents the shortcomings of wavelet neural networks that easily fall into local minimums, and proposes to optimize the initial coefficients of wavelet neural networks by using the characteristics of global search of genetic algorithms to construct better neural networks. We have verified that the traffic speed prediction based on genetically improved wavelet neural network has a high degree of agreement with real data, and the effect is significantly better than the results of ordinary wavelet neural network, which has higher practical value.