Teen Driving Risk and Prevention: Naturalistic Driving Research Contributions and Challenges

Characterizing driver speeding behavior when using partial-automation in real-world driving

Objective: Speeding is a prevalent and complex risky behavior that can be affected by many factors. Understanding how drivers speed is important for developing countermeasures, especially as new automation features emerge. The current study seeks to identify and describe types of real-world speeding behaviors with and without the use of partial-automation.Methods: This study used a combination of supervised and unsupervised data analysis techniques to assess relevant factors in real-world speeding epochs, extracted from the MIT Advanced Vehicle Technology Naturalistic Driving Study, and classified them into distinct speeding behaviors. Speeding epochs were defined as traveling at least 5 mph over the speed limit for a minimum duration of 3 s. Vehicle speed-exceedance profiles were characterized over time using Dynamic Time Warping and included in multivariate models that evaluated the associations between different features of the speeding epochs, such as speeding duration and magnitude. Finally, the identified features were used to cluster speeding behaviors using the Gower dissimilarity measure.Results: The analysis yielded four types of behaviors in both partially-automated and manual driving: (i) Incidental speeding (low duration, low magnitude), (ii) Moderate speeding (low duration, moderate magnitude), (iii) Elevated speeding (moderate duration, high magnitude), and (iv) Extended speeding (long duration, high magnitude). When comparing the behaviors with and without partial-automation use, both Incidental and Moderate speeding were found to have significantly longer durations with partial-automation than manual driving. Elevated speeding was found to be more prevalent and associated with higher magnitudes during manual than with partially-automated driving. Finally, although Extended speeding was more prevalent during automation use, it was associated with a lower mean and maximum speed magnitude compared to Extended speeding during manual driving.Conclusions: This work highlights the variability in speeding behavior between and within partially-automated and manual driving. The design of systems that mitigate risky speeding behaviors should consider targeting divergent behaviors observed between manual and automated driving as a mechanism to mitigate the prevalence of the different behaviors associated with each state.

Awareness level of safe driving knowledge and practice of specific population drivers: behavioral assessment and educational intervention

PURPOSE

This study aimed to assess car-driving behaviors and attitudes and to measure the changes in the knowledge and behavioral practices associated with road safety measures of 1333 randomly selected young-adult participants (aged 18-24 years) from Jazan University in Gizan city, Saudi Arabia.

METHODS

Data were collected using cross-sectional survey and quasi experimental pre- and post-evaluation educational intervention study including structured questionnaire and take-home educational material.

RESULTS

Results revealed that low compliance with the safety index significantly increased the risk of traffic injuries by 20% [OR = 0.80, 95% CI = (0.59-1.01)]. Whereas the medium category of the safety compliance index is significantly associated with an increasing number of injuries by 6% [OR = 0.94, 95% CI = (0.61-1.52)]. In contrast, both high and medium categories of risk behavior index increased the risk of traffic injuries [OR = 1.08, 95% CI = (0.82-1.43); OR = 0.80, 95% CI = (0.57-1.10), respectively]. Some improvement in risky behavioral practices was seen after the intervention including 'not playing loud music' [86.3%, (P = 0.05)], 'not using mobile phones' [89.4%, (P < 0.05)], and 'not using unprofessional checkups of the car' [71.5%, (P < 0.05)].

CONCLUSION

Ultimately, this unpleasant situation may recommend avoidance of these kinds of intermittent awareness programs; instead, embedding a continuous road safety learning within the life-long educational system as a better and more realistic intervention for reducing the number of road accidents and injuries. Further, establishing various modes of high-capacity city-link public transportation remains among the most recommended strategic and effective options that can curb road traffic injuries in the long run.

Alcohol-related deaths among young passengers: An analysis of national alcohol-related fatal crashes

INTRODUCTION

There is consensus that riding with an impaired driver (RWI) constitutes a major threat to public health. The aim of this study was to characterize the factors contributing to the motor-vehicle deaths of 15-20 year-old (y/o) passengers that RWI with a peer.

METHOD

Secondary analyses of the 2010-2018 Fatality Analysis Reporting System. 5,673 passengers aged 15-20 y/o killed while riding in passenger cars with a driver aged 21 or older, 3,542 of these drivers also aged 15-20 y/o. Analyses were conducted between October 2019 and December 2020.

RESULTS

Sixty-three percent of the young passengers were killed while riding with a driver 15-20 y/o. Of these drivers, 26.8% had a blood alcohol concentration (BAC) >0.00 g/dL and 77.1% had a BAC ≥0.08 g/dL. Compared with those occurring during the day on weekdays, fatalities of young passengers who RWI with a peer driver with a BAC ≥ 0.08 g/dL often occurred on weekend nights (OR = 8.2) and weekday nights (OR = 5.2), and when the passenger and driver were both male (OR = 1.8). Race/ethnicity was not a significant contributor to RWI fatalities.

CONCLUSIONS

Most 15-20 y/o RWI fatalities occurred on weekends, at night, when the driver was a young peer with a high BAC, and the passenger and driver were male. The high prevalence of fatalities in these high-risk situations suggests that young driver-passenger dynamics may contribute to alcohol-related fatalities. Practical Applications: To curb RWI fatalities among underage passengers, countermeasures should focus not only on underage drinking drivers and riders, but also on drinking drivers of all ages. Prevention should increase focus on situations in which both the young passenger and young driver are males.

Experience as a Safety Factor in Driving; Methodological Considerations in a Sample of Bus Drivers

Experience is generally seen as an important factor for safe driving, but the exact size and details of this effect has never been meta-analytically described, despite a fair number of published results. However, the available data is heterogeneous concerning the methods used, which could lead to very different results. Such method effects can be difficult to identify in meta-analysis, and a within-study comparison might yield more reliable results. To test for the difference in effects between some different analytical methods, analyses of data on bus driver experience and crash involvement from a British company were conducted. Effects of within- and between-subjects analysis, non-linearity of effects, and direct and induced exposure methods were compared. Furthermore, changes in the environmental risk were investigated. Between-subject designs yielded smaller effects as compared to within-subjects designs, while non-linearity was not found. The type of exposure control applied had a strong influence on effects, as did differences in overall environmental risk between years. Apparently, “the effect of driving experience” means different things depending upon how calculations have been undertaken, at least for bus drivers. A full meta-analysis, taking several effects of methodology into account, is needed before it can be said that the effect of driving experience on crash involvement is well understood.

Learning to drive: A reconceptualization

Drivers' population-level crash rates incrementally decrease following licensure, which has led to the implicit assumption that an individual driver's crash risk also decreases incrementally after licensure as they accrue experience. However, in the aggregate data an incremental decrease in crash rate can reflect both incremental reductions in crash risk within individuals and an incremental increase in the proportion of drivers who have experienced an abrupt decrease in crash risk. Therefore, while it is true to say that the population of drivers' crash risk reduces in the months following licensure, it is not necessarily true to say that a driver's crash risk reduces in the months following licensure; that is, it cannot be assumed that individual-level changes in crash risk mirror the population-level changes in crash rates. In statistics, this is known as an ecological fallacy and in formal logic it is known as the fallacy of division, a type of category error. Using computational cognitive modeling methods we demonstrate that aggregating individual-level abrupt decreases in crash risk (i.e., non-incremental change trajectories) accurately fits population-level crash rate data from over 1 million adolescents and uniquely accounts for effects of two interventions found to reduce police-reported MVCs. Thus, we demonstrate that (1) a power-law artifact is readily observable in newly licensed drivers' aggregate crash data, which is not necessarily indicative of individual-level change processes, (2) interventions can alter crash risk trajectories by inducing immediate phase changes in crash risk into a lower risk stratum, or increasing the probability of such a change, (3) a phase transition model provides a stronger and more parsimonious account of the existing data than an incremental-accrual model.