Citation

Liang C, Ghazel M. Int. J. Inj. Control Safe. Promot. 2018; 25(2): 162-172.

Affiliation

University Lille 1 , Lille-Villeneuve d'Ascq , France.

Copyright

(Copyright © 2018, Informa - Taylor and Francis Group)

DOI

10.1080/17457300.2017.1416480

PMID

29280400

Abstract

Level crossing (LX) safety continues to be one of the most critical issues for railways despite an ever increasing focus on improving design and practices. In the present paper, a framework of probabilistic risk assessment and improvement decision based on Bayesian belief networks (PRAID-BBN) is proposed. The developed framework aims to analyse various impacting factors which may cause LX accidents, and quantify the contribution of these factors so as to identify the crucial factors which contribute most to the LX accidents. A detailed statistical analysis is first carried out based on the accident/incident data. A BBN risk model is established according to the statistical results. Then, we apply the PRAID-BBN framework on the basis of the accident/incident data provided by SNCF, the French national railway operator. The main outputs of our study are conducive to efficiently focusing on the effort/budget to make LXs safer.

Language: en

Keywords

Bayesian belief networks; Level crossing safety; risk assessment; statistical analysis