Exploring Naturalistic Driving Data for Distracted Driving Measures : Tech Summary

2017Technical Summary 580PDFTech ReportState Project No. DOTLT1000053; LTRC Project No. 15-1SAAgeAutomatic data collection systemsAccident risk forecastingDistractionDriver performanceGenderGlobal Positioning SystemLateral accelerationNeural networksStatistical analysisTime series analysisStrategic Highway Research Program 2United StatesLouisianaLouisiana Transportation Research CenterIshak, Sherif S.Zeringue, Kirk M.Louisiana Transportation Research CenterLouisiana. Department of Transportation and DevelopmentLouisiana State University (Baton Rouge, La.)US Transportation CollectionDistracted driving is a dangerous epidemic that continues to cause deaths and injuries in related crashes throughout the U.S. According to the National Highway Traffic Safety Administration, 3,328 people (including 540 non-occupants) were killed and an estimated additional 421,000 were injured in 2012 from distraction-affected crashes. In Louisiana, a recent study funded by LTRC and UTC, "Distracted Driving and Associated Crash Risks," concluded that texting and talking to passengers while driving impaired driving performance but failed to find any significant effects for cellphone conversation. The study was, however, unable to make any statistical findings on the driving performance based on demographics and road facility type because of the limited sample utilized. With the recent availability of data from the Strategic Highway Research Program (SHRP 2) Naturalistic Driving Studies (NDS), there is ample opportunity to utilize a bigger sample size in a further study that will allow statistical conclusions to be drawn on various strata including gender, road facility type, age, and time of day. Therefore, this study aimed to perform a comprehensive exploration of the SHRP 2 NDS data with the view of identifying if it can provide the data required for an enhanced study on the crash risks of distracted driving. This study also included an outline for the development of a Crash Risk Index to evaluate potential risk associated with drivers based on their socioeconomic characteristics and secondary task involvement