Misreporting of certain behaviors in crash data, specifically alcohol and/or drug-impaired driving and distracted driving, may result in problematic predictive model estimations. Under/overreporting of impaired and distracted driving also has the potential to impact other areas that rely on reported crash data, such as drug recognition expert (DRE) training, high-visibility enforcement, where to employ saturation patrols, existing laws on cell phone use, and marijuana legislation. With the growth of multidisciplinary datasets, research is needed to investigate the extent impaired and distracted driving have been under or over reported in crash data, and the potential negative impacts of such misreporting on driver behavior related crash analysis. The objectives of this research are to develop procedures to assess the existence and extent of under/over reporting of impaired and distracted driving in crash data, and to propose a methodology to improve the reporting of impaired and distracted driving in motor vehicle crashes. A series of workshops will be planned to demonstrate the application of the methodology, how it was developed, and assist states with facilitating this effort on their own.

Project Details


Start Date
August 3, 2022

End Date
August 2, 2025

Focus Areas
Data Analytics, Modeling and Simulation
Economy and Policy
Urban Mobility

The National Academies

Research Centers
Institute for Physical Infrastructure and Transportation (IPIT)

Principal Investigator

Qin, Xiao
Director, Institute for Physical Infrastructure and Transportation (IPIT)
Professor, Civil and Environmental Engineering University of Wisconsin-Milwaukee

Co-Principal Investigator