20.500.12556/DKUM-97389 Using the gradient boosting decision tree (GBDT) algorithm for a train delay prediction model considering the delay propagation feature Accurate prediction of train delay is an important basis for the intelligent adjustment of train operation plans. This paper proposes a train delay prediction model that considers the delay propagation feature. The model consists of two parts. The first part is the extraction of delay propagation feature. The best delay classification scheme is determined through the clustering method of delay types for historical data based on the density-based spatial clustering of applications with noise algorithm (DBSCAN), and combining the best delay classification scheme and the k-nearest neighbor (KNN) algorithm to design the classification method of delay type for online data. The delay propagation factor is used to quantify the delay propagation relationship, and on this basis, the horizontal and vertical delay propagation feature are constructed. The second part is the delay prediction, which takes the train operation status feature and delay propagation feature as input feature, and use the gradient boosting decision tree (GBDT) algorithm to complete the prediction. The model was tested and simulated using the actual train operation data, and compared with random forest (RF), support vector regression (SVR) and multilayer perceptron (MLP). The results show that considering the delay propagation feature in the train delay prediction model can further improve the accuracy of train delay prediction. The delay prediction model proposed in this paper can provide a theoretical basis for the intelligentization of railway dispatching, enabling dispatchers to control delays more reasonably, and improve the quality of railway transportation services. train delay prediction actual train operation data delay type identification delay propagation feature extraction density-based spatial clustering of applications with noise (DBSCAN) k-nearest neighbor (KNN) gradient boosting decision tree (GBDT) random forest (RF) support vector regression (SVR) multilayer perceptron (MLP) napoved zamud vlakov podatki o dejanskem obratovanju vlakov identifikacija tipa zakasnitve ekstrakcija značilnosti širjenja zakasnitve prostorsko združevanje aplikacij s šumom na podlagi gostote naključni gozd regresija podpornih vektorjev true false true Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering Angleški jezik Slovenski jezik https://apem-journal.org/ Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Članek v reviji 2026-03-03 12:11:34 2026-03-03 12:11:34 2026-03-04 03:14:28 0000-00-00 00:00:00 2021 0 0 str. 285-296 no. 3 Vol. 16 Sept. 2021 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2021-07-24 2021-10-28 2021-10-31 004.9:656.2 270066947 10.14743/apem2021.3.400 1854-6250 APEM16-3_285-296.pdf APEM16-3_285-296.pdf 1 EB1713E93E8F4B59F6D82E917865287F 5c975657d6db7dd63de676cb8e7775c674cb7ee55f40accef5cc8d08e2bf4d58 07bf9570-16f2-11f1-9bb8-00155d580e1a https://dk.um.si/Dokument.php?lang=slv&id=204193 https://apem-journal.org/Archives/2021/APEM16-3_285-296.pdf 0 0e126e9c-16f2-11f1-9bb8-00155d580e1a https://dk.um.si/Dokument.php?lang=slv&id=204194 Fakulteta za strojništvo Advances in production engineering & management 0 0 0