Analysis And Comparison Of Long Short-Term Memory Networks Short-Term Traffic Prediction Performance
Yükleniyor...
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
FAC TRANSPORT SILESIAN UNIV TECHNOLOGY
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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 prediction problems were investigated in this study. To achieve this, the numbers of data in the training set was set between 480 and 2800, and the prediction performance of the LSTMs trained using these adjusted training sets was measured. In addition, LSTM prediction results were compared with nonlinear autoregressive neural networks (NAR) trained using the same training sets. Consequently, it was seen that the increase in LSTM's training cluster size increased performance to a certain point. However, after this point, the performance decreased. Three main results emerged in this study: First, the optimum training set size for LSTM significantly improves the prediction performance of the model. Second, LSTM makes short-term traffic forecasting better than NAR. Third, LSTM predictions fluctuate less than the NAR model following instant traffic flow changes.
Açıklama
DOGAN, Erdem/0000-0001-7802-641X
Anahtar Kelimeler
deep learning, traffic flow, short-term, prediction, LSTM, nonlinear autoregressive, training set size
Kaynak
SCIENTIFIC JOURNAL OF SILESIAN UNIVERSITY OF TECHNOLOGY-SERIES TRANSPORT
WoS Q Değeri
N/A
Scopus Q Değeri
Q3
Cilt
107
