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Data-driven train operation models based on data mining and driving experience for the diesel-electric locomotive
Zhang C.-Y.3; Chen D.3; Yin J.1; Chen L.2
2016-08-01
Source PublicationAdvanced Engineering Informatics
ISSN14740346
Volume30Issue:3Pages:553-563
Abstract

Traditional control methods in automatic train operation (ATO) models have some disadvantages, such as high energy consumption and low riding comfort. To alleviate these shortcomings of the ATO models, this paper presents three data-driven train operation (DTO) models from a new perspective that combines data mining methods with expert knowledge, since the manual driving by experienced drivers can achieve better performance than ATO model in some degree. Based on the experts knowledge that are summarized from experienced train drivers, three DTO models are developed by employing K-nearest neighbor (KNN) and ensemble learning methods, i.e., Bagging-CART (B-CART) and Adaboost.M1-CART (A-CART), into experts systems for train operation. Furthermore, the DTO models are improved via a heuristic train parking algorithm (HPA) to ensure the parking accuracy. With the field data in Chinese Dalian Rapid Rail Line 3 (DRRL3), the effectiveness of the DTO models are evaluated on a simulation platform, and the performance of the proposed DTO models are compared with both ATO and manual driving strategies. The results indicate that the developed DTO models obtain all the merits of the ATO models and the manual driving. That is, they are better than the ATO models in energy consumption and riding comfort, and also outperform the manual driving in stopping accuracy and punctuality. Additionally, the robustness of the proposed model is verified by a number of experiments with some steep gradients and complex speed limits.

KeywordAutomatic Train Operation Data-driven Train Operation Model Ensemble Learning Machine Learning Manual Driving
DOI10.1016/j.aei.2016.07.004
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Multidisciplinary
WOS IDWOS:000382793700020
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Citation statistics
Cited Times [WOS]:8   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Beijing Jiaotong University
2.Universidade de Macau
3.Fuzhou University
Recommended Citation
GB/T 7714
Zhang C.-Y.,Chen D.,Yin J.,et al. Data-driven train operation models based on data mining and driving experience for the diesel-electric locomotive[J]. Advanced Engineering Informatics,2016,30(3):553-563.
APA Zhang C.-Y.,Chen D.,Yin J.,&Chen L..(2016).Data-driven train operation models based on data mining and driving experience for the diesel-electric locomotive.Advanced Engineering Informatics,30(3),553-563.
MLA Zhang C.-Y.,et al."Data-driven train operation models based on data mining and driving experience for the diesel-electric locomotive".Advanced Engineering Informatics 30.3(2016):553-563.
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