A risk assessment study on accidents at French level crossings using Bayesian belief networks

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.

KEYWORDS:

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

Acknowledgments

This work has been in the framework of ‘MORIPAN project: MOdèle de RIsque pour les PAssages à Niveau’ within the Railenium Technological Research Institute, in cooperation with the National Society of French Railway Networks (SNCF Réseau) and the French Institute of Science and Technology for Transport, Development and Networks (IFSTTAR).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

the National Society of French Railway Networks (SNCF Réseau) and the French Institute of Science and Technology for Transport, Development and Networks (IFSTTAR).