Predicting the effectiveness of supplement time on delay recoveries: a support vector regression approach

Yuexin Wang, Chao Wen and Ping Huang

International Journal of Rail Transportation, 2022, vol. 10, issue 3, 375-392

Abstract: Investigating the effectiveness of supplement time is a critical method for dispatchers to understand the delay recovery capacity of railway sections and stations, thus improving real-time dispatching efficiency. Based on train operation data of the high-speed railway in China, a support vector regression (SVR) algorithm was employed to investigate the effectiveness of supplement times in railway sections and stations. First, the independent factors were determined, and the hyper-parameters of the SVR model were tuned with the operation data. Then, the performance of the predictive model was tested on the testing dataset. The results show that the predicted delay recovery cases of the model coincide highly with the actual cases. Additionally, the predictive performance of the model under allowable errors illustrates that the accuracy of the model can reach 95.96%, with a 1-minute allowable error. Finally, comparison analyses show that the proposed model outperforms other widely-used delay recovery models.

Date: 2022
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DOI: 10.1080/23248378.2021.1937355

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International Journal of Rail Transportation is currently edited by Wanming Zhai and Kelvin C. P. Wang

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