Review and Simulation of Short-Term Traffic Flow Prediction Methods Based on Deep Learning
In recent years,thanks to the upgrading of traffic detection equipment and urban data storage infrastructure as well as the rapid de-velopment of deep learning technology,the application of deep learning technology to solve the problem of short-term traffic flow prediction has become a research hotspot in the field of intelligent transportation.Different from the traditional short-time traffic flow prediction methods,the short-time traffic flow prediction method based on deep learning can make full use of massive traffic data,dig deeply the hidden features and associations between traffic nodes,and effectively improve the accuracy of short-term traffic flow prediction.Firstly,this paper briefly re-views the development history of short-term traffic flow prediction methods,and focuses on analyzing and discussing the latest technical prog-ress and theoretical research results of short-term traffic flow prediction methods based on deep learning model.Then,the open traffic flow da-ta sets,which are widely used to verify the effectiveness of the algorithm and make comparative analysis,are combed and summarized.In addi-tion,the specific process and detailed steps of applying the short-time traffic flow prediction algorithm based on the deep learning model to solve the actual traffic flow prediction problem are described.The short-term traffic flow prediction algorithm based on the deep learning model LSTM and GRU is simulated with the open test data set PEMS04.Simulation results verify the effectiveness of the algorithm and its advantages compared with traditional methods.Finally,the challenges and future development directions in the field of short-term traffic flow forecasting have been summarized and prospected.
万方数据
维普
