The National Academies

BTSCRP BTS-20 [Anticipated]

Underreporting of Impaired and Distracted Driving Behaviors in Motor Vehicle Crashes

  Project Data
Funds: $450,000
Staff Responsibility: Richard Retting
Fiscal Year: 2022

This project has been tentatively selected and a project statement (request for proposals) is expected to be available on this website. The problem statement below will be the starting point for a panel of experts to develop the project statement.

Statistical and analytical models have been used widely to predict the counts and probabilities of crashes on roadway locations using historical crash data. Unbiased model estimation is critical in accurately predicting crashes and allocating funds for improving traffic safety. However, the underreporting of certain behaviors in crash data, specifically alcohol and/or drug-related and distracted driving, may result in problematic model estimation results. Underreporting of these behaviors also has the potential to impact other areas that rely on reported crash data, including drug recognition expert (DRE) training, high-visibility enforcement, existing laws on cell phone use, and marijuana legislation. Although previous studies have been developed to investigate the effects of crash underreporting on crash prediction models, most of the existing studies relied on simulated data, which might be difficult to validate in real-world situations. With the growth of multidisciplinary datasets, research is needed to investigate to what extent impaired and distracted driving have been underreported in crash data, and the potential negative impacts of underreporting on driver behavior related crash analysis. Additional sources of data that can be used to investigate this issue include hospital injury data, toxicology data, and citation data to name a few. Research also is needed to propose what solutions can be used to reduce or eliminate the impacts of underreporting in crash data.

The objective of this research is to develop a framework that enables and facilitates uncovering the magnitude and overarching impact of underreported impaired and distracted driving behaviors in crash data, and to propose a methodology that can be used to address the underreporting issue in crash analysis. The research should (1) examine and document current statewide/jurisdictional efforts in (a) identifying current practices for collecting data on impaired (e.g., alcohol and/or drugs) and distracted driving in crash data; (b) identifying commonalities and differences between state practices; and (c) identifying and describing current challenges and gaps in data collection and reporting which might lead to underreporting; (2) develop a method to verify underreporting in crash data and quantify the impacts of underreporting on crash analysis; (3) develop guidelines to reduce or eliminate crash underreporting and improve crash data collection that includes best practices for capturing impaired and distracted driving behaviors in crash data; and (4) identify potential supplemental data sources (e.g., EMS, toxicology data) that can be used to identify and mitigate the underreporting issues in crash data.

A peer exchange or smaller workshops should be planned, inviting state departments of transportation to demonstrate how to implement the methodology and assist states with facilitating this effort on their own. Other deliverables may include a PowerPoint presentation detailing how the methodology was developed and how it can be utilized to correct this issue. This can be easily shared among stakeholders in various states.

Impaired and distracted driving are largely understood to contribute to increased crash risk and motor vehicle deaths, regardless of jurisdictional or population differences. In recent years, there has been a substantial shift to data-driven initiatives in transportation safety research and planning. Without a better understanding of the true prevalence of these risky behaviors, efforts to combat impaired and distracted driving with limited data may be futile. Collecting timely, accurate, complete, and uniform data to formulate a complete picture of driver behavior, intention, and risk is imperative to the success of any strategies meant to reduce harm. The development of a methodology will help initiate the process to improve data quality and reporting of these behaviors. Identifying and providing a solution to the problem of underreporting has the potential to improve traffic safety on a national and possibly global scale.

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