Developing a Model for the Risk of the Rail Vehicles Collision Using Bayesian Network

Risk analysis of the rail vehicles collision as one of the most important accidents with the rate of nearly 4 percent of the total accidents has always been of great interest especially for the railway safety decision makers. The reason might be revealed while considering the chain of causes and the scenarios of consequences. The superiority of modeling by Bayesian networks method for analyzing the risk of such accidents is that not only the conditional probability of each cause (hazard) as a variable is assessed but also with having any new evidence, the model can be updated subsequently and the consequences can be monitored. The methodology consists of two major categories of qualitative and quantitative parts while the concluded model is a mixture of the two methods. Main causes of a collision are grouped as environmental, signaling and human factors. Resulted model gives the highest probability to the shunting limit signal as a subdivision of the human errors. Evaluating the consequences resulted in the severity of “second degree” based on the Iranian railway accidents severity categorization guideline.