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This paper focuses on traffic flow forecasting which is an essential component in traffic control or route guidance system. A combination forecasting model called GM-GRNN based on GM(1, 1) and GRNN is built for short-term traffic flow time series. The basic theory and features of General Regression Neural Network (GRNN) and its advantages are introduced. The weight of combination model is determined...
An accurate mathematical model for describing traffic accidents is difficult to be constructed due to various factors such as humans, vehicles and environments. To achieve a better estimation of traffic crashes, a novel composite grey BP neural network (CGBNN) model is presented in this paper. First, the original predicted values of traffic accidents are separately obtained by the GM (1,1) model,...
According to the problem of predicting highway port freight volume, the BP neural network combination forecasting model is proposed based on the study of commonly used forecasting methods including time series forecast, gray system forecast and combination forecast method. Combined with the freight traffic condition of Suifenhe highway port, the combination forecasting model is verified. The experimental...
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