2355 KB0 KB201420262021Li, K. H.Liao, L.Ma, W. G.Yu, Q.Zhang, Y. D.letnik:16številka:3str. 285-296DOI:10.14743/apem2021.3.400ISSN:1854-6250COBISSID_HOST:270066947URN:URN:NBN:SI:doc-WAO2J26WenFakulteta za strojništvo, Inštitut za proizvodno strojništvoAdvances in production engineering and managementactual train operation datadelay propagation feature extractiondelay type identificationdensity-based spatial clustering of applications with noise (DBSCAN)ekstrakcija značilnosti širjenja zakasnitvegradient boosting decision tree (GBDT)identifikacija tipa zakasnitvek-nearest neighbor (KNN)multilayer perceptron (MLP)naključni gozdnapoved zamud vlakovpodatki o dejanskem obratovanju vlakovprostorsko združevanje aplikacij s šumom na podlagi gostoterandom forest (RF)regresija podpornih vektorjevsupport vector regression (SVR)train delay predictionUsing 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 servicesTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Mariboru, Fakulteta za strojništvo