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

机译：道路概率安全评估贝叶斯网络模型中的复杂度降低和敏感性分析

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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.
机译：本文关注于改进现有的道路和高速公路概率安全性分析方法。在快速回顾了贝叶斯网络模型后，其中特别关注了人为错误，并考虑了沿途存在的所有与安全相关的项目或元素，处理了重要问题并提供了一些解决方案。这包括：（1）一种新的通用方法，用于详细描述变量的条件概率（给定变量的父级导致闭合形式的公式）；（2）一种分区技术，该技术可使我们大大减少计算所需的CPU时间，基于使用条件独立性将贝叶斯网络划分为非常小的子网络并导致降低的复杂度的方法，该方法的变量数或道路长度呈线性关系，而不是替代方法的非线性特征，并且（3）范围敏感性分析方法，该方法利用了分区技术，并且优于局部灵敏度分析。最后，提供了一些真实的例子来说明所提出的方法对评估公路或常规道路的安全性的实用性。

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    Castillo Enrique; Grande Zacarias; Mora Elena; Lo Hong K.; Xu Xiangdong;

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    Univ Cantabria, Dept Appl Math & Computat Sci, Santander, Cantabria, Spain;

    Univ Cantabria, Dept Appl Math & Computat Sci, Santander, Cantabria, Spain;

    Univ Cantabria, Dept Appl Math & Computat Sci, Santander, Cantabria, Spain;

    Hong Kong Univ Sci & Technol, Dept Civil & Environm Engn, Hong Kong, Hong Kong, Peoples R China;

    Tongji Univ, Dept Traff Engn, Shanghai, Peoples R China;

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