Holiday Highway Traffic Flow Prediction Method Based on Deep Learning

Authors

Xiaofeng Ji, 1. School of Traffic Engineering, Kunming University of Science and Technology, Kunming 650504, China;;2. Yunnan Integrated Transportation Development and Regional Logistics Management Think Tank, Kunming 650504, China;
Yicheng Ge, 1. School of Traffic Engineering, Kunming University of Science and Technology, Kunming 650504, China;;2. Yunnan Integrated Transportation Development and Regional Logistics Management Think Tank, Kunming 650504, China;

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.

Recommended Citation

Ji, Xiaofeng and Ge, Yicheng (2020) "Holiday Highway Traffic Flow Prediction Method Based on Deep Learning," Journal of System Simulation: Vol. 32: Iss. 6, Article 19.
DOI: 10.16182/j.issn1004731x.joss.19-0565
Available at: https://dc-china-simulation.researchcommons.org/journal/vol32/iss6/19

First Page

1164

Revised Date

2020-01-07

DOI Link

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

Last Page

1171

CLC

TP391.9

Recommended Citation

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

DOI

10.16182/j.issn1004731x.joss.19-0565