基于Wavelet-LSTM模型的高速公路入口短时交通流预测

Short-term traffic flow prediction at highway entrances based on Wavelet-LSTM model

为提高高速公路短时交通流预测的准确度,基于高速公路实时的收费数据,采用小波分解(wavelet decomposition,Wavelet)和长短时记忆(longshort-term memory,LSTM)相结合的方法,构建Wavelet-LSTM短时交通流组合预测模型,并与单一模型LSTM、随机森林(Randomforest,RF)及组合模型Wavelet-RF进行对比。结果表明,提出的组合预测模型具有更高的预测精度,且能更有效地把握高速公路交通流的变化,该模型预测准确度接近94%,平均绝对百分比误差为6.7%,比LSTM、RF、Wavelet-RF分别提高了11.41%、13.65%、1.73%。借助于更精确的交通流预测模型,可为智慧高速建设提供一定的助力。

In order to improve the accuracy of highway shortterm traffic flow prediction,this paper uses a combination of wavelet decomposition(Wavelet)and long short term memory(LSTM)to construct a Wavelet-LSTM short-term traffic flow prediction model based on real-time highway toll data,and compares it with some single model LSTM,random forest(RF)and combined model Wavelet-RF.The results show that the combined prediction model proposed in this paper has higher prediction accuracy and is more effective in grasping the changes of highway traffic flow,with a prediction accuracy of nearly 94%and an average absolute percentage error of 6.7%,which is 11.41%,13.65%and1.73%higher than LSTM,RF and Wavelet-RF respectively,with the help of more accurate traffic flow prediction model,a certain amount of assistance can be provided to the construction of smart highway.