Real world crash data are used to estimate the size of crash populations addressable by crash avoidance countermeasures. Until the release of the data from the Large Truck Crash Causation Study (LTCCS) that was conducted from 2001 to 2003 by the Federal Motor Carrier Safety Administration (FMCSA) and the National Highway Traffic Safety Administration (NHTSA), only coarse estimates of those target populations were possible using data from the Fatality Analysis Reporting System (FARS) and the National Automotive Sampling System’s General Estimates System (NASS GES). Both of these databases contain limited information that is coded from police reported data.

The LTCCS conducted on-scene investigations of real world crashes that resulted in a database of 1070 cases rich in detail, specifically related to precrash conditions and factors associated to why the crash occured. The detail in the data was enough to make clinical (case by case) estimations of the applicability of crash avoidance countermeasures for each crash, based on our knowledge of these systems and how effective they are in certain scenarios. Final benefit estimates would take into account the applicable target populations and the effectiveness of a system, as determined through field operational tests or some other measure.

This study presents the results of clinical reviews of truck crashes from the LTCCS to determine which target populations of crashes could be candidates for prevention given the multiple factors that came into play. Countermeasures related to the truck, truck driver, or trucking industry might have prevented 61 percent of the crashes in LTCCS, including 50 percent that might have been prevented by advanced technologies that are currently available for trucks. The newly coded data from these clinical reviews can be used to further refine the applicable crash populations estimated from FARS and GES. This research indicates that only a portion of applicable crash scenarios identified through FARS and the NASS GES are candidates for prevention by crash avoidance countermeasures.

The results present an option for a more accurate methodology for estimating the size of crash populations addressable by crash avoidance countermeasures. Using these results it is possible to prioritize research on crash avoidance countermeasures.