Data-driven short-term traffic flow prediction model for toll stations of urban ring expressway
In order to improve prediction accuracy of traffic flow at existing high-speed toll stations,according to the characteristics of the traffic flow of the toll station,a short-term traffic flow prediction model for urban ring expressway toll stations based on variational mode decomposition(VMD),long short-term memory(LSTM)networks,and support vector regression(SVR)is proposed,and the input parameters of LSTM module and SVR module in the model are optimized by genetic algorithm.The VMD module in the model decomposed the original traffic flow sequence into time series components with different frequencies and random component.Among them,the first major component and the random component reflect the overall daily variation trend,seasonal variation trend and random disturbance information of the original traffic flow sequence respectively,while the other components reflect the traffic flow variation law with different frequency periods.Then,LSTM and SVR models are introduced to predict the different components,and the predicted values of all components are superimposed accordingly to obtain the final prediction results of the original traffic flow sequence.Based on the toll collection data of Guiyang City Ring Expressway toll stations,eight toll stations including Guiyang North Main Line,Jinhua Main Line,Shangmai,Guiyang East,Caoguan,Guiyang South,Shibanshao,and Qinqi were selected for case analysis.Under the same set of model parameters and compared with the KNN,BP,SVR,LSTM and ARIMA models,the average MAPE of the VMD-LSTM-SVR model is 11.30％,which is at least an average reduction of 9.95％(13.86％),and the MAE and RMSE are the lowest,and the R2 and Accuracy are the highest.This shows that the proposed VMD-LSTM-SVR model not only has good prediction performance,but also has good generalization.
intelligent transportationshort-term traffic flow prediction at toll stationvariational mode decompositionlong short-term memory neural networkssupport vector machine for regressiongenetic algorithm
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