|Table of Contents|

Traffic congestion prediction method based onlong short-term memory model(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2020年01期
Page:
26-32
Research Field:
Publishing date:

Info

Title:
Traffic congestion prediction method based onlong short-term memory model
Author(s):
Lv Xian1Qi Yong1Zhang Weibin2
1.School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China; 2.School of Electronic Engineering and Photoelectric Technology,Nanjing University of Science and Technology,Nanjing 210094,China
Keywords:
traffic congestion prediction traffic flow long short-term memory model denoising autoencoder deep learning
PACS:
TP181
DOI:
10.14177/j.cnki.32-1397n.2020.44.01.005
Abstract:
In order to fully consider the influence of various factors and mine the deep features hidden in the traffic flow data in the traffic congestion prediction algorithm,a traffic congestion prediction method based on a long short-term memory(LSTM)model is proposed. The method fully considers the characteristics of traffic flow,weather,holidays and other factors. Firstly,the denoising autoencoder model is used to extract the core features of the input data,and the LSTM model’s long-term memory of historical data is used. The combination predicts the urban traffic congestion degree effectively. Compared with the existing traffic congestion prediction model,the results show that the proposed method has high prediction accuracy and robustness,and the accuracy can reach more than 92%.

References:

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Last Update: 2020-02-29