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

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.

A new insight on the risky behavior of motorists at railway level crossings: An observational field study.

Accidents at railway level crossings (LXs) give rise to serious material and human damage. Particularly, collisions between trains and motorized vehicles are the most critical accidents occurring at LXs. It is worth noticing that violations committed by vehicle drivers are the primary cause of such accidents. The present study is a tentative to acquire a better understanding of risky behavior of vehicle drivers while crossing LXs during the closure cycle. Namely, risk analysis based on field measurement conducted at four automated LXs with two half barriers is performed. We focus on vehicle driver behavior during the LX closure cycle while distinguishing between different phases. In fact, the closure cycle is divided into three phases which are "Ph2 Red Flash and Siren", "Ph3 Barriers Coming Down" and "Ph4 Barriers Down"; and vehicle driver behavior in each phase as time increases is scrutinized respectively. Particularly, zigzag scenarios are detected, using an original experimental setting that we have implemented, and analyzed in detail. The main findings based on the analysis demonstrate that the peak of violation rate in the morning is later than the actual rush hour in the morning; a distinct peak of the violation rate shows on Friday, while the violation rate on weekend is fairly low; the relative violation rate of vehicles with high speed decreases continuously as time advances from Ph2 to Ph3 in the daytime; the violation rate during Ph4 decreases as Ph4 duration is prolonged, which contradicts a general speculation that a higher rate of zigzag violations would appear as the duration of Ph4 is extended. These findings open the way towards determining the impacting factors which have an important contribution to the vehicle driver decision-making in this context (e.g., traffic density, time schedule and phase duration). In addition, the outputs of the present study are conducive to identifying potential interventions to improve safety at LXs.