Robust-LSTM: a novel approach to short-traffic flow prediction based on signal decomposition

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dc.authoridDOGAN, Erdem/0000-0001-7802-641X
dc.contributor.authorDogan, Erdem
dc.date.accessioned2025-01-21T16:44:17Z
dc.date.available2025-01-21T16:44:17Z
dc.date.issued2022
dc.departmentKırıkkale Üniversitesi
dc.description.abstractIntelligent transport systems need accurate short-term traffic flow forecasts. However, developing a robust short-term traffic flow forecasting approach is a challenging task due to the stochastic character of traffic flow. This study proposes a novel approach for short-term traffic flow prediction task, namely Robust Long Short Term Memory (R-LSTM) based on Robust Empirical Mode Decomposing (REDM) algorithm and Long Short Term Memory (LSTM). Short-term traffic flow data provided from the Caltrans Performance Measurement System (PeMS) database were used in the training and testing of the model. The dataset was composed of traffic data collected by 25 traffic detectors on different freeways' main lanes. The time resolution of the dataset was set to 15 min, and the Hampel preprocessing algorithm was applied for outlier elimination. The R-LSTM predictions were compared with the state-of-the-art models, utilizing RMSE, MSE, and MAPE as performance criteria. Performance analyses for various periods show that R-LSTM is remarkably successful in all time periods. Moreover, developed model performance is significantly higher, especially during midday periods when traffic flow fluctuations are high. These results show that R-LSTM is a strong candidate for short-term traffic flow prediction, and can easily adapt to fluctuations in traffic flow. In addition, robust models for short-term predictions can be developed by applying the signal separation method to traffic flow data.
dc.identifier.doi10.1007/s00500-022-07023-w
dc.identifier.endpage5239
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85127566533
dc.identifier.scopusqualityQ1
dc.identifier.startpage5227
dc.identifier.urihttps://doi.org/10.1007/s00500-022-07023-w
dc.identifier.urihttps://hdl.handle.net/20.500.12587/25414
dc.identifier.volume26
dc.identifier.wosWOS:000777874300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofSoft Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectShort-term prediction; Traffic flow; LSTM; Signal decomposition
dc.titleRobust-LSTM: a novel approach to short-traffic flow prediction based on signal decomposition
dc.typeArticle

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