Abstract
According to accident/incident statistics, railway level crossing (LX) is one of the most critical points in railways form the safety point of view. In the present paper, causal reasoning analysis of LX accidents is carried out based on Bayesian networks (BNs). In particular, causal structural constraints are introduced to establish BN risk model for the purpose of combining empirical knowledge and statistical data, thus to identify effective causalities and avoid inappropriate structural connections. Moreover, forward and reverse inferences based on the BN risk model are performed to predict LX accident occurrence and quantify the contribution degree of various impacting factors respectively, so as to identify the riskiest factors. Besides, influence strength analysis is further carried out to scrutinize the influence strength of various causal factors on LX accident occurrence. The outcomes of the BN risk model offer significant insights on exploring practical improvement recommendations to improve LX safety.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
