Novice driver crashes: The relation between putative causal factors, countermeasures, real world implementations, and policy - A case study in simple, scalable solutions.

Novice drivers are at a greatly inflated risk of crashing. This led in the 20th century to numerous attempts to develop training programs that could reduce their crash risk. Yet, none proved effective. Novice drivers were largely considered careless, not clueless. This article is a case study in the United States of how a better understanding of the causes of novice driver crashes led to training countermeasures targeting teen driving behaviors with known associations with crashes. These effects on behaviors were large enough and long-lasting enough to convince insurance companies to develop training programs that they offered around the country to teen drivers. The success of the training programs at reducing the frequency of behaviors linked to crashes also led to several large-scale evaluations of the effect of the training programs on actual crashes. A reduction in crashes was observed. The cumulative effect has now led to state driver licensing agencies considering as a matter of policy both to include items testing the behaviors linked to crashes on licensing exams and to require training on safety critical behaviors. The effort has been ongoing for over a quarter century and is continuing. The case study highlights the critical elements that made it possible to move from a paradigm shift in the understanding of crash causes to the development and evaluation of crash countermeasures, to the implementation of those crash countermeasures, and to subsequent policy changes at the state and federal level. Key among these elements is the development of simple, scalable solutions.

Booster Dose of Attention Training Program for Young Novice Drivers: A Longitudinal Driving Simulator Evaluation Study.

OBJECTIVE: We examined the effectiveness of a second exposure to ACCEL, a novel driving training program, on latent hazard anticipation (HA) performance several months after their first exposure. BACKGROUND: Past research has demonstrated that PC-based driver training programs can improve latent HA performance in young novice drivers, but these improvements are below the ceiling level. METHOD: Twenty-five participants were randomly assigned to either the Placebo group, the ACCEL-1 group, or the ACCEL-2 group. Following the completion of the assigned training program, participants drove a series of eighteen scenarios incorporating latent hazards in a high-fidelity driving simulator with their eyes tracked. Participants returned two to six months following the first session and completed either the placebo program (ACCEL-1 and Placebo groups), or a second dose of training program (ACCEL-2 group), again followed by simulated evaluation drives. RESULTS: The ACCEL-2 group showed improved HA performance compared to the ACCEL-1 and Placebo groups in the second evaluation. CONCLUSION: ACCEL enhances young novice drivers' latent HA performance. The effectiveness of ACCEL is retained up to 6 months, and a second dose further improves HA performance. APPLICATION: Policy makers should consider requiring such training before the completion of graduate driver license programs. Young novice drivers that do not show successful latent HA performance could be required to complete additional training before being allowed to drive without restrictions.

Using SHRP2 NDS data to examine infrastructure and other factors contributing to older driver crashes during left turns at signalized intersections.

Drivers age 65 and over have higher rates of crashes and crash-related fatalities than other adult drivers and are especially over-represented in crashes during left turns at intersections. This research investigated the use of SHRP2 Naturalistic Driving Study (NDS) data to assess infrastructure and other factors contributing to left turn crashes at signalized intersections, and how to improve older driver safety during such turns. NDS data for trips involving signalized intersections and crash or near-crash events were obtained for two driver age groups: drivers age 65 and over (older drivers) and a sample of drivers age 30-49, along with NDS pre-screening and questionnaire data. Video scoring of all trips was performed to collect additional information on intersection and trip conditions. To identify the most influential factors of crash risk during left turns at signalized intersections, machine learning and regression models were used. The results found that in the obtained NDS dataset, there was a relatively small volume of crashes during left turns at signalized intersections. Further, model results found the statistically significant variables of crash risk for older drivers were associated more with health and cognitive factors rather than the infrastructure or design of the intersections. The results suggest that a study using only SHRP2 NDS data will not lead to definitive findings or recommendations for infrastructure changes to increase safety for older drivers at signalized intersections and during left turns. Moreover, the findings of this study indicates the need to consider other data sources and data collection methods to address this critical literature gap in older driver safety.