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Statistical Methods for Naturalistic Driving Studies

The naturalistic driving study (NDS) is an innovative research method characterized by the continuous recording of driving information using advanced 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 epidemiological 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 key issues related to NDS analysis, such as crash surrogates and alternative reference exposure levels.