Traffic Safety: Non-linear Causation for Injury Severity
Traffic Safety: Non-linear Causation for Injury Severity
复制标题
交通安全：伤害严重程度的非线性因果关系
DOI：
10.2495/safe110221
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
R. Azencott
中科院分区：
文献类型：
--
作者：
M. Mougeot;R. Azencott
在欧洲，交通事故现在被广泛记录在国家数据库中。鉴于事故数据量巨大，使用数据挖掘工具对于筛选真正相关的信息至关重要。经典的统计工具通过基本上线性的技术来评估潜在因果关系的强度，或者强烈依赖于特定的特定模型。我们在这里概述了如何基于条件熵的互信息比有助于严格量化因果因素对损伤严重程度的影响，没有假设观察到的变量之间的潜在关系。我们成功地应用这种方法来分析因果关系的因素在德国的深度事故研究数据库，这是一个最大的和最完整的深度事故调查和数据收集在欧洲。结果表明，智能速度自适应系统、碰撞预警和防撞系统可以提高车辆的安全性。
In Europe traffic accidents are now widely recorded in national databases. In view of the massive amounts of accident data, the use of data mining tools is essential to sift truly relevant information. Classical statistical tools evaluate the strength of potential causal relationships by essentially linear techniques, or strongly rely on ad hoc specific models. We outline here how mutual information ratios based on conditional entropy contribute to rigorously quantify the influence of causation factors on injury severity, with no hypothesis on underlying relationships between observed variables. We successfully apply this approach to analyze causation factors in the German In Depth Accident Study database, which is one of the largest and most complete in depth accident survey and data collection in Europe. The resultsshow that additionalsafety gainspotentialare expectedfromintelligent speed adaptation systems, collision warning and collision avoidance systems.