SWOV Catalogus

337899

Analyzing driver behavior using data from the SHRP 2 naturalistic driving study.
20131144 ST [electronic version only]

Washington, D.C., Transportation Research Board TRB, 2013, 4 p., 1 ref.; The Second Strategic Highway Research Program A SHRP 2 Capacity Project Brief

Samenvatting Studies have long shown that the most significant factor in crashes is the driver. To date, however, the driver and the driver’s interaction with the road, vehicle, and environment have been difficult to study in an objective way. A new method–naturalistic driving studies (NDS)–provides objective data, which transportation agencies can use to derive improved countermeasures and more effective uses of existing countermeasures to reduce crashes and improve roadway safety. The second Strategic Highway Research Program (SHRP 2) is conducting the largest and most comprehensive NDS ever undertaken. The study has recruited more than 3,100 volunteer drivers, ages 16—80, at sites in six states: Florida, Indiana, North Carolina, New York, Pennsylvania, and Washington. All their trips are recorded for up to two years. Data include vehicle speed, acceleration, and braking; vehicle controls; lane position; forward radar; and video views to the front and rear of the vehicle and on the driver’s face and hands. When complete in early 2014, the NDS data set will contain in excess of 33,000,000 travel miles from some 3,800 vehicle-years of driving–more than 4 petabytes of data. In parallel, the Roadway Information Database (RID) will contain detailed roadway data on about 12,000 centerline miles of highways in and around the study sites, in addition to information on roadway features, crash histories, traffic and weather conditions, work zones, and active safety campaigns in the study areas from state data sources. The NDS and RID data can be linked to associate driving behavior with the roadway environment. This project brief describes how NDS data can be searched. It also provides an overview of three SHRP 2 projects that are analyzing NDS and RID data to develop real-world safety countermeasures. (Author/publisher)
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