LSTM training set analysis and clustering model development for short-term traffic flow prediction

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dc.authoridDOGAN, Erdem/0000-0001-7802-641X
dc.contributor.authorDogan, Erdem
dc.date.accessioned2025-01-21T16:42:24Z
dc.date.available2025-01-21T16:42:24Z
dc.date.issued2021
dc.departmentKırıkkale Üniversitesi
dc.description.abstractLong short-term memory (LSTM) is becoming increasingly popular in the short-term flow. In order to develop high-quality prediction models, it is worth investigating the LSTM potential deeply for traffic flow prediction. This study has two objectives: first, to observe the effect of using different sized training sets in LSTM training for various and numerous databases; second, to develop a clustering model that contributes to adjusting the training set size. For this purpose, 83 datasets were divided into certain sizes and LSTM model performances were examined depending on these training set sizes. As a result, enlargement of the training set size reduced LSTM errors monotonic for certain datasets. This phenomenon was modeled with the state-of-the-art clustering algorithms, such as K-nearest neighbor, support vector machine (SVM), logistic regression and pattern recognition networks (PRNet). In these models, statistical properties of datasets were utilized as input. The best results were obtained by PRNet, and SVM model performance was closest to PRNet. This study indicates that enlarging the training set size in traffic flow prediction increases the LSTM performance monotonically for specific datasets. In addition, a high-precision clustering model is presented to assist researchers in short-term traffic forecasting to adjust the size of the training set.
dc.identifier.doi10.1007/s00521-020-05564-5
dc.identifier.endpage11188
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue17
dc.identifier.scopus2-s2.0-85099346767
dc.identifier.scopusqualityQ1
dc.identifier.startpage11175
dc.identifier.urihttps://doi.org/10.1007/s00521-020-05564-5
dc.identifier.urihttps://hdl.handle.net/20.500.12587/25069
dc.identifier.volume33
dc.identifier.wosWOS:000607044700006
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectTraffic flow; LSTM; Training set; Prediction; Clustering
dc.titleLSTM training set analysis and clustering model development for short-term traffic flow prediction
dc.typeArticle

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