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According to the complexity of the traffic historical data and the randomness of a lot of uncertain factors influence, a hybrid predicting model that combines both autoregressive integrated moving average (ARIMA) and multilayer artificial neural network (MLANN) is proposed in this paper. ARIMA is suitable for linear prediction and MLFNN is suitable for nonlinear prediction. This paper also investigates...
A number of different forecasting methods have been proposed for traffic flow forecasting including historic method, real-time method, time series analysis, and artificial neural networks (ANN), but accuracy and time efficiency in prediction are a couple of contradictions to be hard to resolve for real-time traffic information prediction. In order to improve time efficiency of prediction, a new short-term...
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