Traffic Flow Forecasting Model Based on Cloud-Self-Organizing Neural Network
Liao Rui-hu · Journal of Transportation Systems Engineering and Information Technology · 2014
Modern transportation systems have complex structure, and the existence of fuzzy, stochastic and uncertainty factors increase the difficulty of huge data involved in qualitative and quantitative integrated analysis. This paper developed the cloud neural network self-organization of traffic flow forecasting model based on the characteristics of cloud model and self-organizing neural network. Using cloud model fuzziness and randomness advantages, the paper proposed the prediction model that can improve the reliability of selforganizing neural network prediction learning sample data to process data problems. Through comparing two models to a city traffic flow forecasting with actual data, the paper found that the forecasting model has higher coefficient of determination than the only using of self-organizing neural network. The results show that the model proposed in the traffic flow forecasting can improve accuracy and reduce generalization error.