Short-term Traffic Flow Prediction Method of Expressway in Plateau Mountainous Areas
Accurate short-term traffic flow prediction is the key to effectively avoid expressway traffic accidents in plateau mountain-ous areas.However,due to the influence of high altitude terrain,the short-term traffic flow data characteristics of expressway in plat-eau mountainous areas are more complex than those in plain areas,and the prediction model applicable to plain areas may not be appli-cable to plateau mountainous areas.The SARIMA,GRNN and LSTM models are selected as the representatives of mathematical statis-tics,traditional machine learning and deep learning respectively,and the online toll collection data of the G4217 Rongchang Expressway from Wenchuan to Maerkang in Aba Tibetan and Qiang Autonomous Prefecture of Sichuan Province is taken as the sample.The results show that the three models all have good prediction performance,among which SARIMA and LSTM models have the same prediction effect,both R2 are close to 0.97,and the MAE is reduced by 53.12%and 57.70%respectively,the MAPE is reduced by 38.19%and 43.72%respectively compared with GRNN model.The research shows that even the mathematical statistical models can also have a good prediction effect on short-term traffic flow prediction of expressway in plateau mountainous areas,and the data has a choice of models,the LSTM model has the best prediction effect,followed by the SARIMA model,and the GRNN model comes last.
plateau mountainous areas expresswaytraffic accidentsshort-term traffic flow prediction
万方数据
维普
