摘要
Abstract
In this study,traffic data of multiple sections of Suzhou expressway were used.Based on various existing short-term traffic flow prediction methods,the vehicle speed prediction model was established by using three different models,namely,secondary exponential smoothing model,LSTM neural network model and BP neural network model,and MSE supplemented with experimental data visualization was used as the model evaluation benchmark to explore the prediction performance of different models under random congestion.The results show that when the traffic flow runs smoothly,the three prediction performance is good,and the MSE value is about 30.However,in the case of random short-term congestion,the predictive performance of LSTM and quadratic exponential smoothing model declines,while the BP model shows better performance when dealing with uncertain changes in a certain period of time.The MSE value of the training result is around 20,and the MSE value can also be maintained below 40 when dealing with emergencies.Compared with exponential smoothing model and LSTM model,BP neural network model has better predictive performance.关键词
交通碳排放/交通流预测/快速路/指数平滑/LSTM/BP神经网络Key words
transportation carbon emissions/traffic flow forecasting/expressway/exponential smoothing/LSTM/BP neural network分类
交通工程
