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Statistical Methods for Naturalistic Driving Studies

机译：自然驾驶研究的统计方法

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The naturalistic driving study (NDS) is an innovative research method characterized bythe continuous recordingofdrivinginformationusingadvanced instrumentation under real-world driving conditions. NDSs provide opportunities to assess driving risks that are difficult to evaluate using traditional crash database or experimental methods. NDS findings have profound impacts on driving safety research, safety countermeasures development, and public policy. NDSs also come with attendant challenges to statistical analysis, however, due to the sheer volume of data collected, complex structure, and high cost associated with information extraction. This article reviews statistical and analytical methods for working with NDS data. Topics include the characteristics of NDSs; NDS data components; and epidemio-logical approaches for video-based risk modeling, including case-cohort and case-crossover study designs, logistic models, Poisson models, and recurrent event models. The article also discusses several keyissues related to NDS analysis, such as crash surrogates and alternative reference exposure levels.
机译：自然主义驾驶研究（NDS）是一种创新的研究方法，其特征在于，在现实世界驾驶条件下连续记录综合信息信息。 NDSS提供了评估难以使用传统碰撞数据库或实验方法评估难以评估的风险的机会。 NDS发现对驾驶安全研究，安全对策发展和公共政策产生了深远的影响。然而，NDSS也随附统计分析的挑战，因为由于收集的数据量，复杂结构和与信息提取相关的高成本，因此占统计分析。本文审查了使用NDS数据的统计和分析方法。主题包括NDSS的特征; NDS数据组件;基于视频风险建模的流行性逻辑方法，包括案例 - 队列和案例交叉研究设计，物流模型，泊松模型和经常性事件模型。本文还讨论了与NDS分析有关的几个关键性，例如崩溃代理人和替代参考曝光率。

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