Data-driven gale-induced risk assessment strategy for the high-speed railway system

Author:

Zhao Guanyuan1,Ma Xiaoping2ORCID,Qiu Xuying3,Zhang Hanqing4,Zhang Zhiping5

Affiliation:

1. School of Civil Engineering, Beijing Jiaotong University, Beijing, China

2. State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China

3. Department of Transportation Engineering, Southeast University, Nanjing, China

4. Department of Transportation Engineering, School of Transportation and Traffic, Beijing Jiaotong University, Beijing, China

5. Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai, China

Funder

National Natural Science Foundation of China

State Key Laboratory of Rail Traffic Control and Safety

Fundamental Research Funds for the Central Universities

Publisher

Informa UK Limited

Subject

Safety Research,Transportation

Reference30 articles.

1. An effective railway intrusion detection method using dynamic intrusion region and lightweight neural network

2. Influence of environmental wind on fluctuation velocity of contact wire of high-speed railway;Chen X.;Journal of Traffic and Transportation Engineering,2020

3. China Railway Corporation (2014). Notice of China Railway Corporation on printing and distributing “railway technology management regulations” (part of high-speed railway) (Tie Zong Technology [2014] No. 172). China Railway Corporation.

4. A Review of Online Dynamic Models and Algorithms for Railway Traffic Management

5. Scientific challenges in disaster risk reduction for the Sichuan–Tibet Railway

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