ANALYSIS AND COMPARISON OF LONG SHORT-TERM MEMORY NETWORKS SHORT-TERM TRAFFIC PREDICTION PERFORMANCE

ANALYSIS AND COMPARISON OF LONG SHORT-TERM MEMORY NETWORKS SHORT-TERM TRAFFIC PREDICTION PERFORMANCE

Long short-term memory networks (LSTM) produces promising results in the prediction of traffic flows. However, LSTM needs large numbers of data to produce satisfactory results. Therefore, the effect of LSTM training set size on performance and optimum training set size for short-term traffic flow pr...

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Bibliographic Details
Main Author: Erdem DOGAN
Format: Article
Language:English
Published: Silesian University of Technology 2020-08-01
Series:Scientific Journal of Silesian University of Technology. Series Transport
Subjects:
deep learning
traffic flow
short-term
prediction
lstm
nonlinear autoregressive
training set size
Online Access:http://sjsutst.polsl.pl/archives/2020/vol107/019_SJSUTST107_2020_Dogan.pdf

Internet

http://sjsutst.polsl.pl/archives/2020/vol107/019_SJSUTST107_2020_Dogan.pdf

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