Although speeding is a significant contributor to traffic fatalities, attempts to address this problem havenot led to a significant reduction in speed-related fatalities. There are a number of inherent shortcomingsin using primarily self-report surveys and crash data to learn more about why drivers speed, and inselecting countermeasures that will most effectively address speeding behaviors. An emerging empiricalapproach is to study the speeding choices that drivers make under everyday driving conditions usingnaturalistic driving methods. Such an approach has the potential to yield highly informative data aboutspeeding. These data, however, are complicated and prone to analytical confusion and uncertaininterpretation if some key conceptual and methodological issues are not addressed. In this paper, weprovide an overview of a naturalistic driving study that was intended to: (1) identify the reasons whydrivers speed, (2) model the relative roles of situational, demographic, and personality factors inpredicting travel speeds, (3) classify speeders, and (4) identify interventions/countermeasures andstrategies for reducing speeding behaviors. We focus on discussing lessons learned associated with threemethodological issues in particular (defining speeding, identifying a way to measure exposure, andobtaining accurate posted speeds) that were crucial to successfully analyzing the data that this studyprovided, and for generating useful results and conclusions. We believe that careful consideration of theseissues will greatly benefit the traffic safety community, especially as we think about future analyses ofnaturalistic driving data.DISCLAIMER: The opinions, findings, and conclusions expressed in this publication are those of theauthors and not necessarily those of the Department of Transportation or the National Highway TrafficSafety Administration.
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