Support vector regression models for delay time predicting considering high-speed rail facility failure

doi:10.16265/j.cnki.issn1003-3033.2019.S2.003

Abstract

Abstract: In order to accurately predict the total delay time caused by train fault, the support vector regression model was established by soft margin SVR method (ε-SVR) and fault tolerance ratio SVR (ν-SVR) method on the basis of real operation data of HSR obtained from Guangzhou Railway Bureau. The primary delay time, the number of affected trains and delay causes were set as the independent variables while the total influenced time was the dependent variable. The predictive ability assessment was carried out using the testing data, and the results show that the predicting accuracies of both ε-SVR and ν-SVR model are over 0.8 when the permitted error was within 20%, and that ν-SVR model has a higher predicting accuracy than ε-SVR model.

Key words: high-speed rail, train real operation data, primary delay, delay time prediction, support vector regression (SVR) model

CLC Number:

X951

TANG Yixiong, XU Chuanling, WEN Chao, LI Zhongcan, SONG Shaojie. Support vector regression models for delay time predicting considering high-speed rail facility failure[J]. China Safety Science Journal, 2019, 29(S2): 18-23.

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