Citation

Qizhou H, Xin F, Lishuang B. Transp. Saf. Environ. 2021; 3(4): tdab019.

Copyright

(Copyright © 2021, Oxford University Press)

DOI

10.1093/tse/tdab019

PMID

unavailable

Abstract

In this paper, the least square support vector machine (LSSVM) is used to study the safety of a high-speed railway. According to the principle of LSSVM regression prediction, the parameters of the LSSVM are optimized to model the natural disaster early warning of safe operation of a high-speed railway, and the management measures and methods of high-speed railway safety operation under natural disasters are given. The relevant statistical data of China's high-speed railway are used for training and verification. The experimental results show that the LSSVM can well reflect the nonlinear relationship between the accident rate and the influencing factors, with high simulation accuracy and strong generalization ability, and can effectively predict the natural disasters in the safe operation of a high-speed railway. Moreover, the early warning system can improve the ability of safety operation evaluation and early warning of high-speed railway under natural disasters, realize the development goals of high-speed railway (safety, speed, economic, low-carbon and environmental protection) and provide a theoretical basis and technical support for improving the safety of a high-speed railway.

Language: en