BACKGROUND: Almost a third of car accidents involve driving after alcohol consumption. Autonomous vehicles (AVs) may offer accident-prevention benefits, but at current automation levels, drivers must still perform manual driving tasks when automated systems fail. Therefore, understanding how alcohol affects driving in both manual and automated contexts offers insight into the role of future vehicle design in mediating crash risks for alcohol-impaired driving.
METHODS: This study conducted a systematic review on alcohol effects on manual and automated (takeover) driving performance. Fifty-three articles from eight databases were analyzed, with findings structured based on the information processing model, which can be extended to the AV takeover model.
RESULTS: The literature indicates that different Blood Alcohol Concentration (BAC) levels affect driving skills essential for traffic safety at various information processing stages, such as delayed reacting time, impaired cognitive abilities, and hindered execution of driving tasks. Additionally, the driver\'s driving experience, drinking habits, and external driving environment play important roles in influencing driving performance.
CONCLUSIONS: Future work is needed to examine the effects of alcohol on driving performance, particularly in AVs and takeover situations, and to develop driver monitoring systems.
CONCLUSIONS: Findings from this review can inform future experiments, AV technology design, and the development of driver state monitoring systems.

Drug-impaired driving is an increasing public safety concern across Canada, particularly due to the demonstrated increase in use of recreational drugs such as cocaine. Cocaine is a central nervous system stimulant drug; however, it can impair an individual\'s driving ability in both the stimulant and crash phases. Despite the scientific consensus regarding cocaine\'s potential for driving impairment, there is relatively little information available regarding blood concentrations and associated observations of impairment in suspected impaired drivers. Retrospective data analysis was performed to evaluate suspected impaired driving cases in which cocaine and/or benzoylecgonine were detected alone, or in combination with other drugs, in blood and urine samples submitted to the Toxicology Section of the Centre of Forensic Sciences with incident dates between 2021 and 2022. Cocaine and/or benzoylecgonine were detected in 46% (blood) and 66% (urine) of the total impaired driving samples submitted. In 41 cases where cocaine and/or benzoylecgonine were the only drug finding in blood, concentrations of cocaine and benzoylecgonine ranged from 0.0073 to 0.26 mg/L (mean 0.096 mg/L) and 0.13 to 5.3 mg/L (mean 2.1 mg/L), respectively. Driving observations reported by the arresting officer in cases where cocaine and/or benzoylecgonine were the only drug finding in blood and urine included the driver being involved in a collision, the vehicle leaving the roadway, erratic driving and the driver being asleep at the wheel; observations of drug impairment reported by the drug recognition expert at the time of driver evaluation included abnormal speech patterns, poor balance/incoordination, abnormal body movements and the individual falling asleep. The results provide concentrations of cocaine and benzoylecgonine observed in suspected impaired drivers, insight into observations that may be associated with prior cocaine use and additional information to inform on the effects of cocaine on driving.

This review aimed to quantitatively summarize the evidence concerning the effectiveness of psychoeducational interventions on driving behavior. A final pool of 138 studies, totaling approximately 97,000 participants, was included in the analyses and covered all types of driving behavior targeted by the interventions. Using a random effects model, significant results were found for almost all driving outcomes, both post-intervention and long-term. The strongest effect was for reducing distracted driving at post-intervention (d = 1.87 [1.12, 2.60], Z = 4.94, p < 0.001). The only non-significant effects were for reducing errors in the long term (d = 0.50 [-0.87, 1.86], Z = 0.71, p = 0.48) and driving under the influence at post-intervention (d = 0.35 [0.00, 0.71], Z = 1.96, p = 0.05). Concerning which type of intervention was more effective, feedback, training and motivational ones appear to work best. Educational interventions show only weak effects, while awareness interventions seem mostly ineffective. Overall, our results show that most interventions can reduce different types of driving behaviors, but there are specific aspects to be considered based on the targeted behavior.

OBJECTIVE: Drivers who have committed a traffic violation are a particularly high-risk group, yet studies conducted among this group are scarce. We analyzed and synthesized the current literature concerning subsequent risky driving behaviors, recidivism, and crashes among drivers with a traffic violation.
METHODS: We searched PubMed, Education Resources Information Center (ERIC), Academic Search Complete, Web of Science, and Scopus for articles published in English between January 1, 1999, and May 31, 2023. A total of 25 articles met the selection criteria and were included in the final analysis. Two coders independently extracted and analyzed the selected articles to identify common categories across the articles, including study design, study population, type of traffic violation, and study outcomes including subsequent driving behaviors, recidivism, and crash risks.
RESULTS: Of the 25 selected articles, 19 (76%) involved both male and female participants. Fourteen (56%) studies were interventions/evaluation studies, with the other 11 (44%) being observational. Nineteen (76%) studies included alcohol-impaired driving violations, and 23 (92%) studies examined recidivism as an outcome measure. Over half of the observational studies demonstrated that traffic offenders were more likely to commit a subsequent traffic violation or had elevated risk of crashes. Most of the intervention/evaluation studies demonstrated a significant reduction in driving under the influence (DUI) of alcohol among the study participants. However, such positive effects observed during the active intervention period were not always sustained.
CONCLUSIONS: Traffic offenders are a high-risk group for subsequent violations and crashes. Evidence from this review calls for more effective interventions implemented following a traffic violation to prevent recidivism, crashes, and crash-related injuries and deaths.

Data on driving under the influence of alcohol (DUIA) are not always available, accurate, or reliable, making it difficult to study the effects of alcohol policies on road traffic outcomes. The objectives of our study were twofold: 1) to describe how road traffic outcomes of alcohol policies are assessed when DUIA data are missing, and 2) to explore the effects of alcohol policies when DUIA data are missing.
We conducted a scoping review of non-randomized studies that assessed the road traffic outcomes of alcohol policies when DUIA data are missing. Until November 2021, we searched studies published between 2000 and 2021, in English or French, via MEDLINE, APA PsycInfo, CINAHL, and SocINDEX. We assessed the risk of bias in the included studies with the Quality Assessment Tool for Before-After (Pre-Post) Studies With No Control Group. The selection process, data extraction, and the risk of bias assessment were conducted independently and in duplicate. We used vote counting based on the direction of the effects of alcohol policies as a synthesis method. The protocol for this review was published in PROSPERO under record number CRD42021266744.
Twenty-four eligible studies were included. Regarding objective 1, most studies used uncontrolled interrupted time series designs to assess road traffic fatalities resulting from night-time crashes. The reasons for missing DUIA data were generally not reported. Regarding objective 2, we found evidence for an association between alcohol policies and decreased road traffic fatalities. Subgroup analyses found no evidence for an association between methodological modifiers and positive effect directions for road traffic fatalities.
Caution is needed when interpreting road traffic outcomes associated with alcohol policies when DUIA data are missing. Greater efforts should be made to improve the reporting of outcomes assessments. Future studies must address several methodological issues (e.g., more granular data, well-defined intervention and implementation, and controlled designs). Our results should be compared to those from others reviews where DUIA data were available to confirm or recalibrate the associations found in studies where DUIA data were missing.

Alcohol-impaired driving is a major contributor to motor vehicle crash deaths and injury. Many survey studies include self-report measures of alcohol-impaired driving, but no guidance is available to help researchers select from among available measures. The aims of this systematic review were to compile a list of measures that researchers have used previously, to compare performance between measures, and to identify the measures with highest validity and reliability.
Literature searches of PubMed, Scopus, and Web of Science identified studies that assessed alcohol-impaired driving behavior through self-report. The measures from each study and, if available, indices of reliability or validity were extracted. Using the measures\' text, we developed 10 codes to group similar measures and compare them. For example, the \"alcohol effects\" code refers to driving while feeling dizzy or lightheaded after drinking, and the \"drink count\" code pertains to the number of drinks someone consumed before driving. For measures with multiple items, each item was categorized separately.
After screening according to the eligibility criteria, 41 articles were included in the review. Thirteen articles reported on reliability. No articles reported on validity. The self-report measures with the highest reliability coefficients contained items from multiple codes, namely alcohol effects and drink count.
Self-report alcohol-impaired driving measures with multiple items evaluating distinct aspects of alcohol-impaired driving show better reliability than measures using a single item. Future work investigating the validity of these measures is needed to determine the best approach for conducting self-report research in this area.

Classification systems concerning driving-impairing medicines can help healthcare providers identify medicinal drugs with no or the least impairing effects and inform patients of the potential risks of certain medicines to safe driving. This study aimed to comprehensively assess the characteristics of classifications and labeling systems regarding driving-impairing medicines.
Google Scholar and several databases, including PubMed, Scopus, Web of Science, EMBASE, safetylit.org, and TRID were searched to identify the relevant published material. The retrieved material was assessed for eligibility. Data extraction was done to compare the categorization/labeling systems concerning driving-impairing medicines in terms of characteristics such as the number of categories, description of each category, and description of pictograms.
After screening 5,852 records, 20 studies were selected for inclusion in the review. This review identified 22 categorization/labeling systems regarding medicines and driving. Classification systems had different characteristics, but most of them were designed based on the graded categorization system described by Wolschrijn. Initially, categorization systems had seven levels, but later the medicine impacts were summarized into 3 or 4 levels.
Although different categorization/labeling systems regarding driving-impairing medicines are available, the most effective systems in changing driver\'s behavior are the simple and understandable ones. Besides, health care providers should consider patient\'s socio-demographic features when informing them about driving under the influence.

Policy enforcement is crucial to achieve impacts on alcohol-related harm. It is not clear what level of enforcement intensity or \'dosage\' is necessary for addressing drink driving and related harms. Given competing enforcement demands and agencies\' resource constraints, understanding how much enforcement is sufficient to deter drink driving is critical.
This systematic literature review followed Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) guidelines to examine research about dosage effects of enforcement and related visibility on drink-driving outcomes, including motor vehicle crashes and fatalities. Risk of bias was assessed using the Cochrane Collaboration Effective Practice and Organization of Care tool and the JBI checklist.
The 21 studies that met the inclusion criteria for this review differed in measures of enforcement dosage and outcomes, making it difficult to synthesise results across studies and draw conclusions about a threshold or optimal level of enforcement. Although most included studies found that sustained enforcement was associated with reductions in drink driving or related harms, only two studies tested an optimal dosage. Due to study design limitations, a substantial percentage of these studies must be considered with caution.
Additional research with rigorous study designs with appropriate controls is needed to determine an optimal high visibility enforcement dosage level to help law enforcement agencies make realistic decisions about allocating enforcement resources to address drink driving.
Consistent evidence about a drink-driving enforcement dosage threshold is lacking, partly due to an insufficient number of well-designed studies. Addressing challenges of conducting rigorous studies in community settings is crucial.

Ride-hailing businesses, including Uber and Lyft, have reshaped road traffic since they first began operating in the United States approximately a decade ago. It follows that ride-hailing may also alter the incidence and distribution of road traffic crash injuries and deaths. The available evidence relating ride-hailing to crashes is critically reviewed in this article. We present a theoretical model that synthesizes the hypothesized mechanisms, and we identify common methodological challenges and suggest priorities for future research. Mixed results have been reported for the overall incidence of road traffic crash injuries and deaths, likely due to heterogeneous impacts on vehicular traffic flow (e.g., increasing the volume of vehicles); on vehicle-, person-, and event-level characteristics (e.g., reducing alcohol-impaired driver crashes); on road-user types (e.g., increasing pedestrian crashes); and on environmental conditions (e.g., reducing crashes most substantially where public transit access is poorest). The lack of a well-developed theory of human mobility and methodological challenges that are common to many ecological studies impede exploration of these sources of moderation. Innovative solutions are required to explicate ride-hailing\'s heterogeneous impacts, to guide policy that can take advantage of the public health benefits of ride-hailing, and to ensure that research keeps pace with technological advances that continue to reshape road traffic use.

Drug driving is a serious problem worldwide that can increase the risk of road crashes. This systematic review seeks to identify factors associated with drug driving (i.e., driving after consuming drugs other than alcohol) to highlight gaps in existing knowledge and inform the design of more effective countermeasures. A search of the literature was conducted for the period January 1, 2005 to July 31, 2021 using six different databases. The search protocol followed PRISMA guidelines and was registered in PROSPERO (#CRD42021234616). Studies that met inclusion criteria compared drug drivers with either non-drug drivers, alcohol-only drivers or drug drivers from an earlier time period, to identify factors specifically associated with drug driving, rather than common to all drivers. Two hundred and nineteen publications met the inclusion criteria and were included within the review. Based on the findings, a logic model was developed that presents the factors associated with drug driving. Various sociodemographic, psychosocial and legal factors emerged as the main factors associated with illegal drug driving. At the sociodemographic and psychological levels, drug drivers were more likely to be single, young males who often drive after using cannabis and who score high on sensation-seeking and impulsivity scales. The key social factor found to be associated with drug driving was peer acceptance/disapproval of the behaviour. At the legal level, the review suggested that the effectiveness of current enforcement approaches to drug driving vary among jurisdictions around the world due to differences in the level of perceived certainty of apprehension and the chances of punishment avoidance. Future research into the anticipated and actual rewards for drug driving is needed to inform the development of more effective countermeasures.