Trends and disparities in alcohol-DWI license suspensions by suspension duration, North Carolina, 2007-2016.

PURPOSE: To examine trends and potential disparities in North Carolina (NC) driving while impaired by alcohol (alcohol-DWI) license suspensions from 2007-2016. Specific objectives included: 1) examining personal (e.g., race/ethnicity) and contextual (e.g., residential segregation) characteristics of alcohol-DWI license suspensions by suspension duration; and 2) examining trends in annual suspension rates by race/ethnicity, sex, and duration. METHODS: We linked NC administrative licensing and county-level survey data from several sources from 2007-2016. Suspensions were categorized by duration: 1 to <4 years and 4 years or longer (proxies for initial and repeat suspensions, respectively). We calculated counts, percentages, and suspensions rates (per 1,000 person-years) with 95% confidence intervals, examined trends in annual suspension rates by race/ethnicity, sex, and suspension duration. RESULTS: We identified 220,471 initial and 41,526 repeat license suspensions. Rates among males were three times that of females. 21-24-year-old (rates: 6.9 per 1,000 person-years for initial; 1.5 for repeat) and Black (4.1 for initial; 1.0 for repeat) individuals had the highest suspension rates. We observed decreases in annual initial and repeat suspension rates among males, but only in repeat suspensions for females during the study period. A substantial decrease in annual initial suspension rates was observed among Hispanic individuals relative to other racial/ethnic groups, while annual repeat suspension rates exhibited large decreases for most racial/ethnic groups. The highest overall suspension rates occurred in counties with higher proportions of the population without health insurance and with the highest levels of Black/White residential segregation. CONCLUSIONS: Potential disparities by race/ethnicity and sex existed by alcohol-DWI license suspension duration (i.e., initial vs. repeat suspensions) in NC. Contextual characteristics associated with suspensions, including a high degree of residential segregation, may provide indications of underlying structures and mechanisms driving potential disparities in alcohol-DWI outcomes.

Designing a new integrated control solution for electric power steering systems based on a combination of nonlinear techniques.

An EPS system is used to improve the stability and safety of the car when steering while also simplifying the steering process. This article introduces a novel control solution for the EPS system called BSSMCPID. This algorithm combines two nonlinear techniques, BS and SMC, with the input signal corrected by a PID technique. This algorithm provides three new contributions compared to existing algorithms: reducing system errors and eliminating phase differences, ensuring stability even when exposed to external disturbances, and reducing power consumption. The system's stability is evaluated according to the Lyapunov criterion, while the algorithm's performance is evaluated based on numerical simulation results. According to the article findings, the RMS error of the steering column angle and steering motor angle values (controlled objects) is approximately zero, and the RMS error of the steering column speed and steering motor speed is about 0.01 rad/s, which is much lower than the results obtained with traditional BS and PID controllers. When the EPS system is controlled by the integrated nonlinear method proposed in this work, the output values always closely follow the reference values with negligible errors under all investigated conditions. Additionally, power steering performance increases as speed decreases or driver torque increases, which follows the ideal assisted power steering curve. In general, the responsiveness and stability of the system are always ensured when applying this algorithm.

IoT-Based Assessment of a Driver's Stress Level.

Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing stress and workload levels. This motivates the development of advanced monitoring architectures taking into account psycho-physiological aspects. In this work, we propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system to assess the stress status of the driver. In detail, the system leverages heterogeneous components and techniques to collect driver (and, possibly, vehicle) data, aiming at estimating the driver's arousal level, i.e., their psycho-physiological response to driving tasks. In particular, a wearable sensorized bodice and a thermal camera are employed to extract physiological parameters of interest (namely, the heart rate and skin temperature of the subject), which are processed and analyzed with innovative algorithms. Finally, experimental results are obtained both in simulated and real driving scenarios, demonstrating the adaptability and efficacy of the proposed system.

Multi-Index Driver Drowsiness Detection Method Based on Driver's Facial Recognition Using Haar Features and Histograms of Oriented Gradients.

It is estimated that 10% to 20% of road accidents are related to fatigue, with accidents caused by drowsiness up to twice as deadly as those caused by other factors. In order to reduce these numbers, strategies such as advertising campaigns, the implementation of driving recorders in vehicles used for road transport of goods and passengers, or the use of drowsiness detection systems in cars have been implemented. Within the scope of the latter area, the technologies used are diverse. They can be based on the measurement of signals such as steering wheel movement, vehicle position on the road, or driver monitoring. Driver monitoring is a technology that has been exploited little so far and can be implemented in many different approaches. This work addresses the evaluation of a multidimensional drowsiness index based on the recording of facial expressions, gaze direction, and head position and studies the feasibility of its implementation in a low-cost electronic package. Specifically, the aim is to determine the driver's state by monitoring their facial expressions, such as the frequency of blinking, yawning, eye-opening, gaze direction, and head position. For this purpose, an algorithm capable of detecting drowsiness has been developed. Two approaches are compared: Facial recognition based on Haar features and facial recognition based on Histograms of Oriented Gradients (HOG). The implementation has been carried out on a Raspberry Pi, a low-cost device that allows the creation of a prototype that can detect drowsiness and interact with peripherals such as cameras or speakers. The results show that the proposed multi-index methodology performs better in detecting drowsiness than algorithms based on one-index detection.

Internet of things based smart framework for the safe driving experience of two wheelers.

Several parameters affect our brain's neuronal system and can be identified by analyzing electroencephalogram (EEG) signals. One of the parameters is alcoholism, which affects the pattern of our EEG signals. By analyzing these EEG signals, one can derive information regarding the alcoholic or normal stage of an individual. Many road accident cases around the world, including drinking and driving scenarios, which result in loss of life, have been reported. Another reason for such incidents is that riders avoid wearing helmets while driving two-wheelers. Many road accident cases involving two-wheelers, including drinking, driving, overspeeding, and nonwearing helmets, have been reported. Therefore, to solve such issues, the present work highlights the features of an intelligent model that can predict the alcoholism level of the subject, wearing of a helmet, vehicle speed, location, etc. The system is designed with the latest technologies and is smart enough to make decisions. The system is based on multilayer perceptron, histogram of oriented gradients (HoG) feature extraction, and random forest to make decisions in real time. The accuracy of the proposed method is approximately 95%, which will reduce the fatality rate due to road accidents. The system is tested under different working environments, i.e., indoor and outdoor, and satisfactory outcomes are observed.

Driving change: Understanding consumers' reasons influencing electric vehicle adoption from the lens of behavioural reasoning theory.

The present study attempts to explore consumer-centric reasons affecting the adoption of electric vehicles (EVs) are investigated using behavioural reasoning theory (BRT). Our study is among the first to examine consumer's EV adoption intention using BRT through the integration of the reasons "for and against" electric vehicle (EV) adoption. On data of 312 urban consumers, second order confirmatory factor analysis (CFA) revealed the existence of underlying reasons and SEM helped in testing the proposed relationships. This study also investigates the interaction effect of financial incentive policy with the consumer reasons on EV adoption. Findings revealed that "reasons for" adoption are environmental concern, perceived technology, and maintenance of knowledge and "reasons against" adoption are scepticism, price, and instrumental utility. Environmental beliefs and values influence the "reasons for" consumer intentions to approve electric vehicle adoption. Financial incentives policy was found significant in dampening the impact of reasons against adoption of electric vehicle. The study delineates the strategies for strengthening the promotion of electric vehicles.

Neuropsychology's Role in Assessment of Medical Decision-Making Capacity, Home Safety, and Driving Ability.

Individuals with neurologic illness/injury often experience changes in their daily functioning. Clinical providers caring for these individuals are frequently called upon for input as patients and their families navigate challenging decisions to ensure safety. This article reviews 3 areas in which recommendations from clinicians are often requested: medical decision-making, home safety, and driving. Strategies for making recommendations in these areas is offered including discussion of how neuropsychology evaluations can provide useful information to aid in making recommendations.

Theory-Based Determinants of Stopping Drowsy Driving Behavior in College Students: A Cross-Sectional Study.

Drowsy driving among college students is a critical public health issue due to its significant impact on road safety. This cross-sectional study aimed to investigate the determinants of stopping drowsy driving behavior among college students using the multi-theory model (MTM) of health behavior change. Data for this study were collected from September to October 2023 via a 42-item psychometric valid, web-based survey disseminated via Qualtrics, involving 725 students from a large southwestern university. Nearly half of the participants (49.38%) reported drowsy driving in the past month. Hierarchical multiple regression analysis revealed that participatory dialogue (p = 0.0008) and behavioral confidence (p < 0.0001) significantly predicted the initiation of refraining from drowsy driving, with the final model explaining 36.4% of the variance. Similarly, emotional transformation (p < 0.0001) and practice for change (p = 0.0202) significantly predicted the sustenance of behavior change, with the final model accounting for 40.6% of the variance. These findings underscore the importance of targeted MTM-based interventions focusing on enhancing students' awareness and confidence in managing drowsiness to mitigate drowsy driving, ultimately improving road safety and student well-being.

Long-Haul Truck Drivers' Perceptions of Truck Stops and Rest Areas: Focusing on Health and Wellness.

The work and life routine of long-haul truck drivers (LHTDs) involve the use of truck stops and rest areas to meet their basic human needs. These extensions of their workspaces on the road do not always offer adequate physical structures and services that drivers need for optimal health. This study aimed to evaluate long-haul truck drivers' perceptions of food services, safety, physical activity, rest, and personal hygiene offered at truck stops and rest areas, as well as the correlation between these perceptions and sociodemographic, health, and work conditions variables. A cross-sectional, quantitative, and descriptive study was conducted with long-haul truck drivers from the southern region of Brazil. For data collection, a sociodemographic questionnaire and a Likert scale on food, rest, personal hygiene, safety, and physical activity services offered at truck stops and rest areas along Brazilian roads from March to August 2023 were used. The data were analyzed with simple frequency descriptive statistics. The sample consisted of 175 long-haul truck drivers. Out of these, 70.29% declared that the services of the truck stops and rest areas were charged; more than half (53.59%) of the professionals evaluated the rest service as "good" or "excellent"; the food services were "good" or "excellent" for 42.24% of the drivers. The spaces for physical activities were the worst evaluated as "bad" or "terrible" by 41.61%, followed by bathroom services (28.42%) and safety (34.24%). Rest and feeding services had better evaluations, while the services of bathroom, safety, and physical activity presented worse evaluations. Variables such as nationality, weekly working days, and marital status presented positive significance and influenced drivers' perceptions of the services offered at truck stops and rest areas. Drivers who were Brazilian and worked more than five days a week negatively evaluated the services of rest (p = 0.018), safety [0.020], physical activity (0.003), and bathrooms (0.020). In addition, the physical activity services were better evaluated by single drivers than married drivers. These findings suggest that the work conditions and nationality may influence LHTDs' perceptions of services and structures of truck stops and rest areas. These findings may reflect a lack of investments and support efforts to improve basic services such as personal hygiene, a safe environment, and physical exercises, which are fundamental to the health of the workers and aimed at reducing vulnerability and a sedentary lifestyle and meeting the basic human needs of LHTDs.

Advanced Necklace for Real-Time PPG Monitoring in Drivers.

Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects' movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver's well-being by providing information about the driver's physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace's design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor's performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer's algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads.

Correlation between Muscular Activity and Vehicle Motion during Double Lane Change Driving.

The aim of this study was to compare the correlation between electromyography (EMG) activity and vehicle motion during double lane change driving. This study measured five vehicle motions: the steering wheel angle, steering wheel torque, lateral acceleration, roll angle, and yaw velocity. The EMG activity for 19 muscles and vehicle motions was applied for envelope detection. There was a significantly high positive correlation between muscles (mean correlation coefficient) for sternocleidomastoid (0.62) and biceps brachii (0.71) and vehicle motions for steering wheel angle, steering wheel torque, lateral acceleration, and yaw velocity, but a negative correlation between the muscles for middle deltoid (-0.75) and triceps brachii long head (-0.78) and these vehicle motions. The ANOVA test was used to analyze statistically significant differences in the main and interaction effects of muscle and vehicle speed. The mean absolute correlation coefficient exhibited an increasing trend with the increasing vehicle speed for the muscles (increasing rate%): upper trapezius (30.5%), pectoralis major sternal (38.7%), serratus anterior (13.3%), and biceps brachii (11.0%). The mean absolute correlation coefficient showed a decreasing trend with increasing vehicle speed for the masseter (-9.6%), sternocleidomastoid (-12.9%), middle deltoid (-5.5%), posterior deltoid (-20.0%), pectoralis major clavicular (-13.4%), and triceps brachii long head (-6.3%). The sternocleidomastoid muscle may decrease with increasing vehicle speed as the neck rotation decreases. As shoulder stabilizers, the upper trapezius, pectoralis major sternal, and serratus anterior muscles are considered to play a primary role in maintaining body balance. This study suggests that the primary muscles reflecting vehicle motions include the sternocleidomastoid, deltoid, upper trapezius, pectoralis major sternal, serratus anterior, biceps, and triceps muscles under real driving conditions.

Keep on truckin': how effective are health behaviour interventions on truck drivers' health? A systematic review and meta-analysis.

BACKGROUND: Truck drivers are a vital workforce, but have higher rates of obesity and other chronic diseases than the general population. The occupation's sedentary nature, limited physical activity opportunities and access to healthy food, and irregular sleeping patterns contribute to poor health. This systematic review and meta-analysis aimed to evaluate the effectiveness of interventions on health behaviours and cardiometabolic biomarkers of health in truck drivers. METHODS: A systematic search was conducted in February 2024, and reported according to PRISMA 2020 guidelines. Experimental studies targeting physical activity, sedentary behaviour, sleep, diet, weight loss, drug/alcohol use, and/or smoking were eligible. Two reviewers independently screened and completed data extraction and risk of bias assessment. Data were combined at the study level. Pooled statistics were calculated using mean differences (MD) or standardised mean differences (SMD) for outcomes that were reported in ≥2 studies. Pre- and post-intervention means and standard deviations (SD) for the intervention and control groups were used to compute effect sizes. RESULTS: Nineteen studies (n=2137 participants) were included. Meta-analyses found a small-to-moderate increase in fruit and vegetable consumption (SMD 0.32, p=0.03) with no other significant effects on other outcome variables. CONCLUSIONS: Interventions are moderately effective in increasing truck drivers' fruit and vegetable consumption, but not other outcomes. There is a dearth of research in the driver population compared to other occupational groups. Future interventions should consider workplace and environmental factors to promote the health and wellbeing of truck drivers. TRIAL REGISTRATION: The study protocol was registered on PROSPERO (CRD42021283423).

Unveiling the risks of speeding behavior by investigating the dynamics of driver injury severity through advanced analytics.

Single-vehicle crashes, particularly those caused by speeding, result in a disproportionately high number of fatalities and serious injuries compared to other types of crashes involving passenger vehicles. This study aims to identify factors that contribute to driver injury severity in single-vehicle crashes using machine learning models and advanced econometric models, namely mixed logit with heterogeneity in means and variances. National Crash data from the Crash Report Sampling System (CRSS) managed by the National Highway Traffic Safety Administration (NHTSA) between 2016 and 2018 were utilized for this study. XGBoost and Random Forest models were employed to identify the most influential variables using SHAP (Shapley Additive Explanations), while a mixed logit model was utilized to model driver injury severity accounting for unobserved heterogeneity in the data collection process. The results revealed a complex interplay of various factors that contribute to driver injury severity in single-vehicle crashes. These factors included driver characteristics such as demographics (male and female drivers, age below 26 years and between 35 and 45 years), driver actions (reckless driving, driving under the influence), restraint usage (lap-shoulder belt usage and unbelted), roadway and traffic characteristics (non-interstate highways, undivided and divided roadways with positive barriers, curved roadways), environmental conditions (clear and daylight conditions), vehicle characteristics (motorcycles, displacement volumes up to 2500 cc and 5,000-10,000 cc, newer vehicles, Chevy and Ford vehicles), crash characteristics (rollover, run-off-road incidents, collisions with trees), temporal characteristics (midnight to 6 AM, 10 AM to 4 PM, 4th quarter of the analysis period: October to December, and the analysis year of 2017). The findings emphasize the significance of driving behavior and roadway design to speeding behavior. These aspects should be given high priority for driver training as well as the design and maintenance of roadways by relevant agencies.

The tales of two cities: use of evidence for introducing 20 miles per hour speed limits in Edinburgh and Belfast (United Kingdom).

BACKGROUND: In 2016, large-scale 20 miles per hour speed limits were introduced in the United Kingdom cities of Edinburgh and Belfast. This paper investigates the role that scientific evidence played in the policy decisions to implement lower speed limits in the two cities. METHODS: Using a qualitative case study design, we undertook content analysis of a range of documents to explore and describe the evolution of the two schemes and the ways in which evidence informed decision-making. In total, we identified 16 documents for Edinburgh, published between 2006 and 2016, and 19 documents for Belfast, published between 2002 and 2016. FINDINGS: In both cities, evidence on speed, collisions and casualties was important for initiating discussions on large-scale 20 mph policies. However, the narrative shifted over time to the idea that 20 mph would contribute to a wider range of aspirations, none of which were firmly grounded in evidence, but may have helped to neutralize opposing discourses. DISCUSSION AND CONCLUSIONS: The relationship between evidence and decision-making in Edinburgh and Belfast was neither simple nor linear. Widening of the narrative appears to have helped to frame the idea in such a way that it had broad acceptability, without which there would have been no implementation, and probably a lot more push back from vested interests and communities than there was.

A comprehensive approach to evaluate human-machine conflicts in shared steering systems.

The shared control authority between drivers and the steering system may lead to human-machine conflicts, threatening both traffic safety and driving experience of collaborative driving systems. Previous evaluation methods relied on subjective judgment and had a singular set of evaluation criteria, making it challenging to obtain a comprehensive and objective assessment. Therefore, we propose a two-phase novel method that integrates eye-tracking data, electromyography signals and vehicle dynamic features to evaluate human-machine conflicts. Firstly, through driving simulation experiments, the correlations between subjective driving experience and objective indices are analyzed. Strongly correlated indices are screened as the effective criteria. In the second phase, the indices are integrated through sparse principal component analysis (SPCA) to formulate a comprehensive objective measure. Subjective driving experience collected from post-drive questionnaires was applied to examine its effectiveness. The results show that the error between the two sets of data is less than 7%, proving the effectives of the proposed method. This study provides a low-cost, high-efficiency method for evaluating human-machine conflicts, which contributes to the development of safer and more harmonious human-machine collaborative driving.

Real-time accident anticipation for autonomous driving through monocular depth-enhanced 3D modeling.

The primary goal of traffic accident anticipation is to foresee potential accidents in real time using dashcam videos, a task that is pivotal for enhancing the safety and reliability of autonomous driving technologies. In this study, we introduce an innovative framework, AccNet, which significantly advances the prediction capabilities beyond the current state-of-the-art 2D-based methods by incorporating monocular depth cues for sophisticated 3D scene modeling. Addressing the prevalent challenge of skewed data distribution in traffic accident datasets, we propose the Binary Adaptive Loss for Early Anticipation (BA-LEA). This novel loss function, together with a multi-task learning strategy, shifts the focus of the predictive model towards the critical moments preceding an accident. We rigorously evaluate the performance of our framework on three benchmark datasets - Dashcam Accident Dataset (DAD), Car Crash Dataset (CCD), and AnAn Accident Detection (A3D), and DADA-2000 Dataset - demonstrating its superior predictive accuracy through key metrics such as Average Precision (AP) and mean Time-To-Accident (mTTA).

Auditing learner driver information about floodwaters: An environmental scan of government issued resources in Australia.

INTRODUCTION: Vehicles driving, or being swept, into floodwaters is a leading cause of flood-related death. Establishing safe behaviors among learner drivers may reduce risk throughout their driving lifetime. METHODS: An environmental scan of publicly available government issued learner and driver handbooks across the eight Australian jurisdictions was conducted to identify information provided regarding floodwaters. Search terms included 'flood,' 'rain,' 'water,' and 'wet.' A visual audit of flood-related signage was also conducted. RESULTS: Twelve documents, across eight jurisdictions, were analyzed. Four jurisdictions' documents provided no information on flooding. Of the four jurisdictions that provided information, content varied. This included highlighting risks and discouraging entering floodwaters in a vehicle, including penalties associated with travel on closed roads, to advising depth and current checks if crossing a flooded roadway, with recommendations based on vehicle size (preference given to bigger vehicles, i.e., 4wds). Information on flood-related signage was found in one jurisdiction. DISCUSSION: Learner and driver handbooks represent a missed opportunity to provide flood safety information. Currently, information is not provided in all jurisdictions, despite flood-related vehicle drowning deaths of drivers and passengers being a national issue. Where information is presented, it is limited, often lacks practical guidance on how to assess water depth, current, and road base stability, and could better use evidence regarding the psychological factors underpinning, and behavioral prompts for performing, or avoiding, risky driving behavior during floods. CONCLUSIONS: The provision and content of information in learner driver and driver handbooks must be improved, particularly within the context of increasing flooding and extreme weather associated with the effects of climate change. PRACTICAL APPLICATIONS: We encourage all jurisdictions to provide practical information that draws on evidence-based risk factors and empirically established psychological factors for behavioral change to help establish safe driver behaviors around floods in the formative years of learning to drive.

Exploring the utility of a social norm approach in reducing younger drivers' engagement in hand-held phone use while driving: A qualitative study.

INTRODUCTION: Engagement in hand-held phone use while driving among young drivers is a prevalent concern in society, despite countermeasures to deter the behavior. The social norm approach has been effective in reducing negative behaviors in young adults (e.g., binge drinking, drink driving). However, whether this approach can reduce hand-held phone use while driving in this population has not been thoroughly investigated. METHOD: The qualitative study explored young drivers' attitudes and opinions on social norm messages designed to reduce hand-held phone use while driving. In addition, young drivers' opinions on current campaigns were explored to provide further insight into the effectiveness of these messages. Thirty young drivers were interviewed and shown six social norm messages. RESULTS: The data were analyzed using reflexive thematic analysis, resulting in five themes and one sub-theme: (1) Road safety messages with minimal impact on hand-held phone use while driving; (2) What constitutes an effective road safety message for hand-held phone use while driving; (3) Comparisons between social norm messages and road safety messages; (4) The potential benefits of combined social norms, (4a) Improving and optimizing the message; and (5) "It's kinda just numbers on a screen": Negative views on social norm messages. Results highlight the diverse opinions towards road safety campaigns and the need to increase exposure to these messages. Further, a combined social norm message was perceived as most effective in reducing engagement in hand-held phone use while driving. CONCLUSIONS: The current study provides preliminary evidence that the social norm approach may be effective in reducing hand-held phone use among young drivers. Further, this study highlights the need to maximize exposure to phone use while driving campaigns in this high-risk cohort. PRACTICAL APPLICATIONS: Results support the development of a social norm messaging intervention to reduce young drivers hand-held phone use while driving.

Exploring the perceived effectiveness and strictness of penalties for traffic offences: The influence of prior punishment experience and evaluation perspective (personal vs. general).

INTRODUCTION: Previous research has identified the perception of penalties as one of the most important deterrents to road traffic offenses. This study investigated whether the perceived effectiveness and the perceived strictness of penalties for different traffic offenses are associated with prior punishment experience and evaluation perspective (personal - if participants were being punished themselves, vs. general - for punishing all drivers). METHOD: A convenience sample of 1,374 Lithuanian drivers participated in the survey (56.3% males; aged 18-77 years). Among them, 801 participants had no penalties for traffic offenses, 333 reported monetary fines in the last year, and 240 reported having their drivers license suspended at least once in their driving career. A scale with 10 specific penalties for traffic offenses was developed to measure the perceived effectiveness and strictness of penalties. Half of the participants evaluated the penalties as if they were personally sanctioned for each traffic offense (personal perspective), while the other half assessed the effectiveness and strictness of the penalties for drivers in general. RESULTS: The results indicated that the perceived effectiveness of penalties was related to the evaluation perspective, being higher for changing one's own behavior than for changing behavior in general. However, the perceived effectiveness of penalties was not related to prior punishment experience. Males and drivers who had experienced their license being suspended reported the highest perceived strictness of the penalties, whereas females and drivers with no prior punishment experience perceived penalties as the least strict. PRACTICAL APPLICATIONS: These findings suggest the need for developing new ways of communicating penalties to different groups of drivers. Nevertheless, this research was exploratory in nature and further research is warranted.