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Highway-Rail Crossing Accident Analysis Using Bayesian Belief Networks
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2021-09-10
[PDF-2.32 MB]
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Abstract:The rate of highway-rail grade crossing collisions has steadily increased year over year since 2009, after a decades long period of decline, beginning in 1972. Several models exist that predict the likelihood and number of collisions at crossings. These models have decreased in accuracy as they have aged. This research employed Bayesian statistics and its graphical representation, Bayesian belief networks, to develop a new model that predicts the probability of a collision at a railway/highway grade crossing, as a function of known characteristics, readily available through open-source data. The final model was found to be a relatively accurate predictor of collision likelihood but showed deficiencies that prevent its use in practical application. Despite these deficiencies, utilizing Bayesian statistics remains a promising method of predicting collision likelihood at a grade crossing, and further study into this application should be conducted.
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