Research on the Nonlinear Traffic Flow Time Sequence Prediction Model - Zhu Hai Feng (Institute of Advanced Engineering and Science, 2014)
This paper proposes a non-linear traffic flow time sequence prediction model aiming at the periodic and stochastic characteristics of the traffic flow. First, one-dimension traffic flow time sequence data are transformed to multi-dimension time sequence. Then the RBF neural network which has strong nonlinear prediction capability is applied to model. Finally the simulation experiment is used to testify to the model. The results of the simulation prove the prediction accuracy of the model with RBF neural traffic flow time sequence prediction model is much higher than the traditional one and the prediction results can be utilized in the practical traffic management. The results show that the nonlinear traffic flow time series forecasting model with nonlinear, non-stationary characteristics of different period randomness, chaos and uncertainty. The results of this study to forecast short-term traffic flow have certain theoretical and practical significance.