Traffic volume prediction of extra-long subsea tunnel based on AGRU-Trans fusion model
In order to help the subsea tunned traffic control department of master accurate traffic data and provide better traffic guidance to travelers,this paper studies recurrent neu-ral network(RNN)and Transformer algorithm,combines the advantages of GRU and Transformer model algorithm and adds self-attention mechanism.It finally proposes a traffic volume prediction model of subsea tunnel based on AGRU-Trans fusion model.It then se-lects the Shinan-Huangdao traffic operation data of Jiaozhou Bay subsea tunnel and compares the AGRU-Trans fusion model with three benchmark models.It is found that the mean ab-solute error(MAE)values of LSTM,Transformer and GRU models are respectively 31.48%,67.54%and 20.57%larger than that of AGRU-Trans.Root mean square error(RMSE)increased by 35.63%,38.45%and 32.02%respectively.The results show that the prediction results of AGRU-Trans fusion model fit the real data best,and the prediction accuracy is higher than that of the benchmark models.Therefore,it can be concluded that this model can provide theoretical reference for the management department of Jiaozhou Bay subsea tunnel to conduct refined vehicle guidance and control.
subsea tunneltraffic volume predictionAGRU-Trans fusion modelrecurrent neural network(RNN)Transformer model
NETL
NSTL
万方数据
维普
