Drinking and driving behavior at stop signs and red lights.

Alcohol is one of the principal risk factors for motor vehicle crashes. One factor that contributes to vehicle crashes is noncompliance with stop signs and red lights. The present experiment investigated the effects of alcohol and drinking patterns on driving behavior at stop signs and red lights. 28 participants participated in drinking and simulated driving sessions during which they received a moderate dose of alcohol (0.08% BAC) or a placebo. Simulated driving tasks measured participants' driving performance at stop signs and red lights in response to each dose. Results suggested that alcohol impaired the driver control of speed and direction and prolonged their simple and complex reaction time, which were exhibited by impaired speed and lateral control, longer reaction time when the lights turned yellow, and lower deceleration towards stop signs and red lights. Visual degradation may also occur under alcohol intake. It was also suggested that alcohol impaired non-binge drinkers more severely. To be specific, higher acceleration was observed in impaired non-binge drinkers.

Development and validation of a model to predict blood alcohol concentrations: Updating the NHTSA equation.

OBJECTS: To date, multiple models have been developed to estimate blood or breath alcohol concentration (BAC/BrAC). Several factors have been identified that affect the discrepancy between BACs/BrACs and retrospective estimation (eBAC) with existing equations. To the best of our knowledge, a model to quantify the effects of factors on the discrepancy between BAC/BrAC and eBAC is still nonexistent. The goal of this work was to develop a model to provide a more accurate retrospective estimation of breath alcohol concentration (eBAC). METHOD: A laboratory study with alcohol consumption and a driving task was conducted with 30 participants (17 male and 13 female) to explore the factors that may contribute to the discrepancy between BrAC and eBAC obtained with existing models. A new eBAC model was developed to improve the estimation of BrAC by modeling effects of gender, weight, and the delay of BrAC measurement on the discrepancy. The validity of the model was tested and established with the data from the experiment conducted in this study and two published research studies, and compared with existing eBAC models. RESULTS: Results of the model validity examination indicated that the developed model had higher R squares and lower root-mean-squared errors (RMSE) in estimating BrAC in three experiments compared with the existing eBAC models, including the NHTSA equation, the Matthew equation, the Lewis equation, the Watson equation, and the Forrest equation. CONCLUSION: The developed eBAC model had a better performance of BrAC estimation compared with existing eBAC models. The validation of the model with the data from three empirical studies indicated a high level of generalizability in estimating BrAC.

Effects of lead time of verbal collision warning messages on driving behavior in connected vehicle settings.

INTRODUCTION: Under the connected vehicle environment, vehicles will be able to exchange traffic information with roadway infrastructure and other vehicles. With such information, collision warning systems (CWSs) will be able to warn drivers with potentially hazardous situations within or out of sight and reduce collision accidents. The lead time of warning messages is a crucial factor in determining the effectiveness of CWSs in the prevention of traffic accidents. Accordingly, it is necessary to understand the effects of lead time on driving behaviors and explore the optimal lead time in various collision scenarios. METHODS: The present driving simulator experiment studied the effects of controlled lead time at 16 levels (predetermined time headway from the subject vehicle to the collision location when the warning message broadcasted to a driver) on driving behaviors in various collision scenarios. RESULTS: Maximum effectiveness of warning messages was achieved when the controlled lead time was within the range of 5s to 8s. Specifically, the controlled lead time ranging from 4s to 8s led to the optimal safety benefit; and the controlled lead time ranging from 5s to 8s led to more gradual braking and shorter reaction time. Furthermore, a trapezoidal distribution of warning effectiveness was found by building a statistic model using curve estimation considering lead time, lifetime driving experience, and driving speed. CONCLUSIONS: The results indicated that the controlled lead time significantly affected driver performance. PRACTICAL APPLICATIONS: The findings have implications for the design of collision warning systems.