Short-Term Traffic Flow Forecast of Expressway Under Heavy Rain
摘要
The incidence rate of traffic accidents increases in heavy rain due to the limited vision of drivers.In order to accurately predict the traffic flow of expressway under heavy rain and reduce accidents,this paper proposes a method based on the improved Cuckoo Search(CS) algorithm and Radial Basis Function(RBF) neural network for expressway traffic flow forecast under heavy rain.The method uses monkey climbing process in monkey swarm algorithm to optimize the cuckoo position update strategy,and then adopts the adaptive update strategy for recognition rate to establish a traffic flow forecast model for expressway based on improved CS-RBF Neural Network(CS-RBFNN).Experimental results show that the improved CS-RBFNN model has a higher convergence speed and prediction accuracy than the improved GSO-RBFNN model.The Mean Absolute Percentage Error(MAPE) of the proposed method is 8.2% and the Mean Absolute Error(MAE) is 20.14.The Root Mean Square Error(RMSE) of the method is 19.2,and its prediction accuracy is higher than 90%.