Risky Driving by Recently Licensed Teens: Self-Reports and Simulated Performance

Abstract

Summary: U.S. teens are overrepresented in motor vehicle crashes, with the majority due to driver error; however, causal pathways remain to be elucidated. This research aimed to identify driving performance factors that might underlie newly-licensed male teens' risk. Surveys were conducted with 21 16-year-olds at the time of intermediate licensure. During the second month of licensure they completed drives in a high-fidelity simulator. Simulator scenarios allowed assessment of responses to yellow traffic lights changing to red and to a visual search task, for which previous data on older age groups of drivers were available. All teens had an A or B grade point average, previously found to be associated with lower crash and citation risk. Nonetheless, 71% reported risky driving in terms of prior unlicensed, unsupervised driving. In the simulator, 46% went through an intersection as the light turned red, compared to 33% of adults. In the visual search task, teens had shorter mean perception-reaction times and identified more targets than adults and older drivers, but similar to young drivers. Therefore, even teens with good grades, perceived to be less risky, were willing to take driving risks. Their driving performance suggests there may be subtle differences in the way recently-licensed teens drive that might predispose them to crashes. Further research of this nature can increase understanding of such differences and inform the development of more targeted intervention.

Drivers' Behavior Through a Yellow Light: Effects of Distraction and Age

“…The purpose of this current study is to examine the likelihood that a driver will proceed through an intersection when a light turns yellow. A similar analysis was conducted on this data by Senserrick et al (2007) and findings already showed that novice drivers (age 16 years) ran more yellow lights when compared to other age groups. This current study extends upon Senserrick et al (2007)'s work by also considering the interactions with driver distraction factors and time to stop line (TSL).…”

“…A similar analysis was conducted on this data by Senserrick et al (2007) and findings already showed that novice drivers (age 16 years) ran more yellow lights when compared to other age groups. This current study extends upon Senserrick et al (2007)'s work by also considering the interactions with driver distraction factors and time to stop line (TSL). The length of yellow light phase timing, which is closely related to time to stop line, has been shown to predict red-light compliance (Retting & Greene, 1997).…”

“…Previous results from studies performed on driving simulators have shown that the decision to stop or go is mediated by age (Senserrick et al, 2007) and the time to stop line (TSL) (Caird, Chisholm, Edwards & Creaser, 2007). Several studies have shown that younger drivers who choose to proceed through a yellow light are significantly less likely than older drivers to clear the intersection before the light turns red (Caird et al, 2007;Milloy, Caird, Ohlhauser & Pearson, 2010).…”

Advancing simulation-based driver training

“…The error-score had a more erratic pattern as a function of age and is therefore not shown. 4.6% of the men and 14.6% of the women had an error-score greater than 1. have also been used recently to study aggressive driving and road rage , running yellow/red lights (Allen et al, 2005b;Senserrick et al, 2007), voluntary violations (Chalmé et al, 2006), and violating behaviour under time pressure (Bonsall & Palmer, 1997). An interesting finding of Chalmé et al (2006) was that there were large individual differences: There were participants who (almost) never voluntarily violated a rule and others who made a large number of violations.…”

How do drivers behave during indecision zone maneuvers?

“…Previous driving simulator studies have shown that age of driver (Senserrick et al, 2007) and the time to stop line (Caird et al, 2007) had significant impact on drivers' decision to stop or not at a yellow signal indication. Hurwitz et al (2012) applied fuzzy logic modeling distance to stop line and vehicle position to predict drivers stopping probability.…”