A data perception model for the safe operation of high-speed rail in rainstorms. (June 2020)

• Record Type: Journal Article
• Authors: Hu, Qizhou; Bian, Lishuang; Tan, Minjia
• Is Part Of: Transportation research. Volume 83(2020)
• Publication Date: 2020-06
• Subjects: High-speed rail (HSR) -- Rainstorm environment -- Early warning -- Perception model; Transportation -- Research -- Periodicals; Transportation -- Environmental aspects -- Periodicals; 354.76
• DOI: 10.1016/j.trd.2020.102326
• Languages: English
• ISSNs: 1361-9209

Abstract

Highlight: An early warning process for the rainstorm disaster is designed. An HSR operation program with different rainstorm degrees is given out. A data perception model of rainstorm is proposed with correction coefficients. Abstract: In order to solve the safety operation problems of High-speed rail (HSR) in different areas and different sections under the rainstorm condition, an early warning process for the rainstorm disaster is designed. Furthermore, in order to control the operation risk, a HSR operation program with different rainstorm degrees is given out based on the analysis of rainstorm warning mechanism and rainstorm warning threshold in this paper. In addition, considering the reality that natural conditions vary greatly and the rainfall is very uneven, a data perception model of rainstorm (DPM) is proposed with correction coefficients for solving the calculation problem of precipitation for rainstorm warning. The DPM mainly adopts Paulhus's empirical equation and uses the linear function to improve it for calculating the precipitation, which is able to calculate the hourly precipitation in different regional environments, and also effectively evaluate the rainstorm warning level of high-speed rail in this period. It can calculate and monitor the process by big data and MATLAB. The result of case analysis shows that the DPM has good practical value for solving the safety operation problem of HSR in different areas under rainstorm environment.