A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks
A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks
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DOI：
10.1016/j.jsr.2011.06.010
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发表时间：
2011-10-01
影响因子：
4.1
通讯作者：
de Ona, Juan
中科院分区：
文献类型：
--
作者：
Oqab Mujalli, Randa;de Ona, Juan
介绍：本研究描述了一种方法，用于减少经常考虑在建模的交通事故的严重程度的变量的数量。该方法的效率进行评估，通过构建贝叶斯网络（BN）。方法：它是基于两个阶段的选择过程。几个变量选择算法，通常用于数据挖掘，以选择变量的子集。使用选定的子集构建BN，并使用五个指标将其性能与原始BN（具有所有变量）进行比较。进一步分析改善指标值的BN，以识别最重要的变量（事故类型、年龄、大气因素、性别、照明、受伤人数和涉及的乘员）。使用这些变量构建新的BN，其中指标的结果表明，在大多数情况下，相对于原始BN，统计上显着改善。结论：它是可能的，以减少使用的变量的数量来模拟交通事故伤害的严重程度，通过贝叶斯网络，而不降低模型的性能。对行业的影响：该研究为安全分析人员提供了一种方法，该方法可用于最大限度地减少所使用的变量数量，以便在不降低模型性能的情况下有效地确定交通事故的伤害严重程度。(C)2011年国家安全理事会和爱思唯尔有限公司保留所有权利。
Introduction: This study describes a method for reducing the number of variables frequently considered in modeling the severity of traffic accidents. The method's efficiency is assessed by constructing Bayesian networks (BN). Method: It is based on a two stage selection process. Several variable selection algorithms, commonly used in data mining, are applied in order to select subsets of variables. BNs are built using the selected subsets and their performance is compared with the original BN (with all the variables) using five indicators. The BNs that improve the indicators' values are further analyzed for identifying the most significant variables (accident type, age, atmospheric factors, gender, lighting, number of injured, and occupant involved). A new BN is built using these variables, where the results of the indicators indicate, in most of the cases, a statistically significant improvement with respect to the original BN. Conclusions: It is possible to reduce the number of variables used to model traffic accidents injury severity through BNs without reducing the performance of the model. Impact on Industry: The study provides the safety analysts a methodology that could be used to minimize the number of variables used in order to determine efficiently the injury severity of traffic accidents without reducing the performance of the model. (C) 2011 National Safety Council and Elsevier Ltd. All rights reserved.