Citation

Tang T, Gan J. China Saf. Sci. J. 2022; 32(6): 123-130.

Copyright

(Copyright © 2022, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2022.06.2634

PMID

unavailable

Abstract

In order to accurately predict train running time while considering requirement on accuracy and timeliness of its operation, a time prediction model was established. Firstly, data of railway timetable were extracted and analyzed to determine possible influencing factors of its running time, and Box-Cox transformation was used to normalize operation data considering their non-normality. Then, the model's input features and hyper-parameters were optimized based on decision tree and grid-search algorithm, respectively, and its performance was improved. Finally, a train running time prediction model was established by adopting HGBT based on optimized input features and hyper-parameters, and operation data of a Chinese and European railway were utilized to evaluate works at each stage. The results show that Box-Cox transformation can significantly improve data normality and goodness-of-fit of the prediction model, while grid-search algorithm can simultaneously improve the model's efficiency and accuracy. Compared with other commonly used machine learning algorithms for train running time prediction, the proposed HGBT model features higher accuracy and efficiency. © 2022 China Safety Science Journal. All rights reserved.

Language: zh