Holiday Highway Traffic Flow Prediction Method Based on Deep Learning

doi:10.16182/j.issn1004731x.joss.19-0565

Abstract

Abstract: Accurately predicting highway traffic holiday flow can provide important data for the emergency management of highway. The LSTM-SVR prediction model is established by using the theoretical framework of deep learning. The BP neural network is used to process the sample data, and the data features captured by LSTM are input into the SVR regression layer to realize the traffic flow prediction. Before and after the “Eleventh” Golden Week, the LSTM-SVR model was verified by using the traffic monitoring data of the intermodulation station in Lijiang City and the prediction results were compared with the others. It is found that the LSTM-SVR model has good applicability in the highway traffic flow prediction of different periods, weathers and traffic conditions.

Key words: traffic engineering, holiday traffic flow prediction, deep learning, LSTM-SVR, expressway traffic flow

CLC Number:

TP391.9

Ji Xiaofeng, Ge Yicheng. Holiday Highway Traffic Flow Prediction Method Based on Deep Learning[J]. Journal of System Simulation, 2020, 32(6): 1164-1171.

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