Bias caused by self-reporting distraction and its impact on crash estimates

Over the last decade, driver distractions, such as cell phone use and texting, have become a significant contributor to roadway crashes. Some states now have legislation that severely restricts or bans driver activities deemed distracting. However, many policies and engineered countermeasures are ba...

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Bibliographic Details
Published in:Accident analysis and prevention 2012-11, Vol.49, p.360-365
Main Authors: Hanley, Paul F., Sikka, Nikhil
Format: Article
Language:English
Subjects:
Accidents, Traffic - statistics & numerical data
Adolescent
Adult
Aged
Aged, 80 and over
Attention
Bias
Categories
Crashes
Databases, Factual
Driver distractions
Estimates
Female
Humans
Injuries
Injury severity
Likelihood Functions
Logistic Models
Male
Middle Aged
Occupant injuries
Odds Ratio
Policies
Self Report
Self-reporting bias
Trauma Severity Indices
Two-stage correction
United States
Wounds and Injuries - etiology
Young Adult
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Summary:Over the last decade, driver distractions, such as cell phone use and texting, have become a significant contributor to roadway crashes. Some states now have legislation that severely restricts or bans driver activities deemed distracting. However, many policies and engineered countermeasures are based on self-reported crash data. This raises the issue of potential bias and when not controlled for in analysis supporting policy decisions, can lead to poor allocation of public resources. This study explores the impact of self-reporting driver distraction on the likelihood estimates of the injury severity category of vehicle crashes. Using a two-step correction technique, the presence of bias is tested, when present corrected, and its impact is interpreted. The findings show that self-reporting bias is present in the national database, a database often used to help evaluate policy and engineering options, self-reporting bias understates the true effect of driver distraction on injury severity, and it is not uniform across injury categories. As a result, the forecast of potential savings of countermeasure policies or in-vehicle devices will be distorted leading to inefficient allocation of public resources.
ISSN:0001-4575
1879-2057
DOI:10.1016/j.aap.2012.02.008