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
Project ID
MIL118975
Status
Ongoing
Start Date
August 3, 2022
End Date
August 2, 2025
Focus Areas
Data Analytics, Modeling and Simulation
Economy and Policy
Safety
Urban Mobility
Sponsors
The National Academies
UW-Madison
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