Yin, J, Su, S, Xun, J et al. (2 more authors) (2020) Data-driven approaches for modeling train control models: Comparison and case studies. ISA Transactions, 98. pp. 349-363. ISSN 0019-0578
Abstract
In railway systems, the train dynamics are usually affected by the external environment (e.g., snow and wind) and wear-out of on-board equipment, leading to the performance degradation of automatic train control algorithms. In most existing studies, the train control models were derived from the mechanical analyzation of train motors and wheel-track frictions, which may require many times of field trials and high costs to validate the model parameters. To overcome this issue, we record the explicit train operation data in Beijing Metro within three years and develop three data-driven approaches, involving a linear regression-based model (LAM), a nonlinear regression-based model (NRM), and furthermore a deep neural network based (DNN) model, where the LAM and NRM can act as benchmarks for evaluating DNN. To improve the training efficiency of DNN model, we especially customize the input and output layers of DNN, batch normalization based layers and network parameter initialization techniques according to the unique characteristics of railway train models. From the model training and testing results with field data, we observe that DNN significantly enhances the predicting accuracy for the train control model by using our customized network structure compared with LAM and NRM models. These data-driven approaches are successfully applied to Beijing Metro for designing efficient train control algorithms.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 ISA. Published by Elsevier Ltd. All rights reserved. This is an author produced version of a paper published in ISA Transactions. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Train control models; Data-driven approaches; Artificial neural networks; Field test |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Sep 2019 13:39 |
Last Modified: | 21 Aug 2020 00:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.isatra.2019.08.024 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:150313 |
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Filename: Yin et al - 2019ISAT-Train control models - WRR.pdf
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