| Title: | Using the gradient boosting decision tree (GBDT) algorithm for a train delay prediction model considering the delay propagation feature |
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| Authors: | ID Zhang, Y. D. (Author)
ID Liao, L. (Author)
ID Yu, Q. (Author)
ID Ma, W. G. (Author)
ID Li, K. H. (Author) |
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| Files: |  APEM16-3_285-296.pdf (2,38 MB)
MD5: EB1713E93E8F4B59F6D82E917865287F
 https://apem-journal.org/Archives/2021/APEM16-3_285-296.pdf
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | 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. |
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| Keywords: | 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) |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 24.07.2021 |
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| Article acceptance date: | 28.10.2021 |
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| Publication date: | 31.10.2021 |
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| Publisher: | Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering |
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| Year of publishing: | 2021 |
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| Number of pages: | str. 285-296 |
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| Numbering: | Vol. 16, no. 3 |
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| PID: | 20.500.12556/DKUM-97389  |
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| UDC: | 004.9:656.2 |
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| ISSN on article: | 1854-6250 |
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| COBISS.SI-ID: | 270066947 |
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| DOI: | 10.14743/apem2021.3.400 |
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| Copyright: | 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. |
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| Publication date in DKUM: | 03.03.2026 |
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| Views: | 157 |
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| Downloads: | 2 |
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| Metadata: |    |
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| Categories: | Misc.
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