Bayesian Networks Probabilistic Safety Analysis of Highways and Roads

Abstract

A probabilistic safety analysis methodology based on Bayesian networks models for the probabilistic safety assessment (PSA) of highways and roads is presented. The main idea consists of (a) identifying all the elements encountered when travelling the road, (b) reproducing these elements by sets of variables, (c) identifying the direct dependencies among variables, (d) building a directed acyclic graph to reproduce the qualitative structure of the Bayesian network, and (e) building the conditional probability tables for each variable conditioned on its parent nodes. Since human error is the most important cause of accidents, driver’s tiredness and attention are used to model how the driver’s behaviour evolves with driving and how it is affected by the environment, signs and other factors. A computer program developed in Matlab implements the Bayesian network model from the list of road items and a set of parameter values given by a group of experts. In this way, the most critical elements can be identified and sorted by importance, thus, an improvement of the global safety of the road can be done savings time and money. The proposed methodology is illustrated in real examples of a Spanish highway and a conventional road.