Abstract: A Bayesian network model is developed, in which all the items or safety related elements encountered when traveling along a highway or road, such as terrain, infrastructure, light signals, speed limit signs, intersections, roundabouts, curves, tunnels, viaducts, and any other safety relevant elements are reproduced. Since human error is the main cause of accidents, special attention is given to modeling the driver behavior variables (driver's tiredness and attention) and to how they evolve with time or travel length. The sets of conditional probabilities of variables given their parents, which permit to quantify the Bayesian network joint probability, are obtained and written as closed formulas, which allow us to identify the particular contribution of each variable to safety and facilitate the computer implementation of the proposed method. In particular, the probabilities of incidents affecting safety are calculated so that a probabilistic safety assessment of the road can be done and its most critical elements can be identified and sorted by importance. This permits the improvement of road safety making adequate corrections to save time and money in the maintenance program by concentrating on the most critical elements and effective investments. Some real examples of a Spanish highway and a conventional road are provided to illustrate the proposed methodology and show its advantages and performance.

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