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首页> 外文会议>Safety and security engineering IV >Traffic safety: non-linear causation for injury severity
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Traffic safety: non-linear causation for injury severity

机译：交通安全：造成伤害严重程度的非线性原因

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摘要

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 results show that additional safety gains potential are expected from intelligent speed adaptation systems, collision warning and collision avoidance systems.
机译：在欧洲，交通事故现已广泛记录在国家数据库中。鉴于大量事故数据，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的线性技术或强烈依赖于特定模型来评估潜在因果关系的强度。我们在这里概述了基于条件熵的互信息比率如何严格量化因果关系对伤害严重性的影响，而没有关于观测变量之间潜在关系的假设。我们在德国深度事故研究数据库中成功应用了这种方法来分析因果关系，该数据库是欧洲最大，最完整的深度事故调查和数据收集之一。结果表明，智能速度自适应系统，碰撞警告和避免碰撞系统有望带来额外的安全收益。

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