Complexity Reduction and Sensitivity Analysis in Road Probabilistic Safety Assessment Bayesian Network Models

This article is concerned with improving some existing methods for probabilistic safety analysis of roads and highways. After a quick review of a Bayesian network model, in which special attention is devoted to human error and all safety related items or elements existing along the road are considered, important problems are dealt with and some solutions provided. This includes: (1) a new and general method for a detailed description of the conditional probabilities of variables given their parents leading to closed-form formulas, (2) a partitioning technique that allows us to reduce drastically the CPU time required for the calculations, based on dividing the Bayesian network into very small subnetworks using the conditional independence property and leading to a reduced complexity, which is linear in the number of variables or road length instead of the nonlinear character of alternative methods, and (3) a range sensitivity analysis method, which takes advantage of the partitioning technique and is superior to a local sensitivity analysis. Finally, some real examples are provided to show the usefulness of the proposed methodologies to assess the safety of highways or conventional roads.