Examination of the Effect of Task Complexity and Coping Capacity on Driving Risk: A Cross-Country and Transportation Mode Comparative Study.

The i-DREAMS project established a 'Safety Tolerance Zone (STZ)' to maintain operators within safe boundaries through real-time and post-trip interventions, based on the crucial role of the human element in driving behavior. This paper aims to model the inter-relationship among driving task complexity, operator and vehicle coping capacity, and crash risk. Towards that aim, data from 80 drivers, who participated in a naturalistic driving experiment carried out in three countries (i.e., Belgium, Germany, and Portugal), resulting in a dataset of approximately 19,000 trips were collected and analyzed. The exploratory analysis included the development of Generalized Linear Models (GLMs) and the choice of the most appropriate variables associated with the latent variables "task complexity" and "coping capacity" that are to be estimated from the various indicators. In addition, Structural Equation Models (SEMs) were used to explore how the model variables were interrelated, allowing for both direct and indirect relationships to be modeled. Comparisons on the performance of such models, as well as a discussion on behaviors and driving patterns across different countries and transport modes, were also provided. The findings revealed a positive relationship between task complexity and coping capacity, indicating that as the difficulty of the driving task increased, the driver's coping capacity increased accordingly, (i.e., higher ability to manage and adapt to the challenges posed by more complex tasks). The integrated treatment of task complexity, coping capacity, and risk can improve the behavior and safety of all travelers, through the unobtrusive and seamless monitoring of behavior. Thus, authorities should utilize a data system oriented towards collecting key driving insights on population level to plan mobility and safety interventions, develop incentives for road users, optimize enforcement, and enhance community building for safe traveling.

Data-Driven Estimation of a Driving Safety Tolerance Zone Using Imbalanced Machine Learning.

Predicting driving behavior and crash risk in real-time is a problem that has been heavily researched in the past years. Although in-vehicle interventions and gamification features in post-trip dashboards have emerged, the connection between real-time driving behavior prediction and the triggering of such interventions is yet to be realized. This is the focus of the European Horizon2020 project "i-DREAMS", which aims at defining, developing, testing and validating a 'Safety Tolerance Zone' (STZ) in order to prevent drivers from risky driving behaviors using interventions both in real-time and post-trip. However, the data-driven conceptualization of STZ levels is a challenging task, and data class imbalance might hinder this process. Following the project principles and taking the aforementioned challenges into consideration, this paper proposes a framework to identify the level of risky driving behavior as well as the duration of the time spent in each risk level by private car drivers. This aim is accomplished by four classification algorithms, namely Support Vector Machines (SVMs), Random Forest (RFs), AdaBoost, and Multilayer Perceptron (MLP) Neural Networks and imbalanced learning using the Adaptive Synthetic technique (ADASYN) in order to deal with the unbalanced distribution of the dataset in the STZ levels. Moreover, as an alternative approach of risk prediction, three regression algorithms, namely Ridge, Lasso, and Elastic Net are used to predict time duration. The results showed that RF and MLP outperformed the rest of the classifiers with 84% and 82% overall accuracy, respectively, and that the maximum speed of the vehicle during a 30 s interval, is the most crucial predictor for identifying the driving time at each safety level.

Driving and Alzheimer's dementia or mild cognitive impairment: a systematic review of the existing guidelines emphasizing on the neurologist's role.

BACKGROUND: Driving is a complex task requiring the integrity and the cooperation of cognition, motor, and somatosensory skills, all of which are impacted by neurological diseases. OBJECTIVE: Identification of neurologist's role when assessing fitness to drive of cognitively impaired individuals. METHODS: We performed a systematic review of the guidelines/recommendations (G/Rs) regarding the evaluation of driving fitness of patients with mild cognitive impairment (MCI) and/or dementia. Emphasis was put on the neurological and neuropsychological aspects of the evaluation. RESULTS: Eighteen G/Rs were included in the review (9 national guidelines, 5 recommendation papers, 3 consensus statements, and 1 position paper). All G/Rs referred to drivers with dementia and 9/18 referred to drivers with MCI. A common approach among G/Rs is the initial trichotomization of patients in safe to drive, unsafe to drive, and undetermined cases, which are referred to a second-line evaluator. First-line evaluators are general practitioners in 10/18 G/Rs; second-line evaluators are neurologists in 7/18 G/Rs. Specific neuropsychological tests are proposed in 11/18 G/Rs and relative cut-off values in 7/18. The most commonly used tests are the MMSE, TMT, and CDT. A thorough neurological examination is proposed in only 1/18 G/R. CONCLUSION: Although extensive multi-disciplinary research has provided useful information for driving behavior of cognitively impaired individuals, we are still far from a widely accepted approach of driving ability evaluation in this increasing population. A comprehensive assessment from a multi-disciplinary team in which the neurologist plays a critical role seems to be required, although this has not yet been implemented in any G/Rs.

Predictors of accidents in people with mild cognitive impairment, mild dementia due to Alzheimer's disease and healthy controls in simulated driving.

OBJECTIVES: To examine the driving variables that predict accident probability in mild dementia due to Alzheimer's disease (AD), mild cognitive impairment (MCI) and healthy older control drivers in simulated driving. To compare the three groups in mean performance and in frequency of scores exceeding 1.5 SD from the mean. METHODS/DESIGN: Participants were 37 drivers with MCI, 16 drivers with AD, and 21 control drivers over the age of 52. Driving measures were derived from four rural driving conditions: moderate traffic without and with distraction and high traffic without and with distraction. The measures were z-transformed based on the performance of 90 control drivers of different ages. Two unexpected incidents occurred per condition, requiring the sudden breaking to avoid an accident. RESULTS: Drivers with AD showed significantly lower average speed, speed variability, greater headway distance, headway variability and average reaction time (RT) than control drivers. Drivers with MCI showed significantly lower average speed, greater headway distance and average RT than control drivers in the two conditions of distraction. No differences were found in accident probability. Drivers with AD had more deviant scores than both control drivers and drivers with MCI in most comparisons. Predictors of accident probability were average RT, speed variability and lateral position variability but MCI and AD status were not significant predictors in any of the regression models. CONCLUSIONS: Despite significant differences in performance, drivers with MCI and AD did not differ in accident probability from control drivers. An individualized approach of examining individual driving performance is recommended.

Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior.

The aim of this paper was to provide a methodological framework for estimating the amount of driving data that should be collected for each driver in order to acquire a clear picture regarding their driving behavior. We examined whether there is a specific discrete time point for each driver, in the form of total driving duration and/or the number of trips, beyond which the characteristics of driving behavior are stabilized over time. Various mathematical and statistical methods were employed to process the data collected and determine the time point at which behavior converges. Detailed data collected from smartphone sensors are used to test the proposed methodology. The driving metrics used in the analysis are the number of harsh acceleration and braking events, the duration of mobile usage while driving and the percentage of time driving over the speed limits. Convergence was tested in terms of both the magnitude and volatility of each metric for different trips and analysis is performed for several trip durations. Results indicated that there is no specific time point or number of trips after which driving behavior stabilizes for all drivers and/or all metrics examined. The driving behavior stabilization is mostly affected by the duration of the trips examined and the aggressiveness of the driver.

Self-awareness of Driving Ability in the Healthy Elderly and Patients With Mild Cognitive Impairment (MCI).

INTRODUCTION: According to latest research, a percentage of cognitively impaired drivers fail to recognize their areas of weakness and overestimate their driving abilities. METHODS: Twenty-seven individuals with amnestic mild cognitive impairment (MCI) and 26 healthy elderly drivers participated in a driving simulator study. After the driving assessment, participants were asked to self-evaluate their performance in comparison with what they considered as average for people of similar age and educational level. RESULTS: According to the applied mixed analysis of variance model, the MCI patients presented increased difficulties in estimating their driving performance to a greater extent in the rural environment in comparison with the urban condition. DISCUSSION: Our findings suggest that the ability of MCI patients to evaluate their driving performance accurately seems to be enhanced or compromised, depending on the number of cues available in their environment, suggesting that providing feedback may improve their metacognitive abilities.

Network-wide road crash risk screening: A new framework.

Network-wide road crash risk screening is a crucial issue for road safety authorities in governing the impact of road infrastructures over road safety worldwide. Specifically, screening methods, which also enable a proactive approach (i.e., pinpointing critical segments before crashes occur), would be extremely beneficial. Existing literature provided valuable insights on road network screening and crash prediction models. However, no research tried to quantify the risk of crash on the road network by considering its main components together (i.e., probability, vulnerability, and exposure). This study covers this gap by a new framework. It integrates road safety factors, prediction models and a risk-based method, and returns the risk value on each road segment as a function of the probability of a crash occurrence and the related severity as well as the exposure model. Next, road segments are ranked according to the risk value and classified by a five-level scale, to show the parts of road network with the highest crash risk. Experiments show the capability of this framework by integrating base map data, context information, road traffic data and five years of real-world crash data records of the whole non-urban road network of the Province of Brescia (Lombardy Region - Italy). This framework introduces a valid support for road safety authorities to help identify the most critical road segments on the network, prioritise interventions and, possibly, improve the safety performance. Finally, this framework can be incorporated in any safety managerial system.

Parkinson's Disease and Driving Fitness: A Systematic Review of the Existing Guidelines.

BACKGROUND: Motor/nonmotor symptomatology and antiparkinsonian drugs deteriorate the driving ability of Parkinson's disease (PD) patients. OBJECTIVES: Treating neurologists are frequently asked to evaluate driving fitness of their patients and provide evidence-based consultation. Although several guidelines have been published, the exact procedure along with the neurologist's role in this procedure remains obscure. METHODS: We systematically reviewed the existing guidelines, regarding driving fitness evaluation of PD patients. We searched MEDLINE and Google Scholar and identified 109 articles. After specified inclusion criteria were applied, 15 articles were included (nine national guidelines, five recommendation papers, and one consensus statement). RESULTS: The treating physician is proposed as the initial evaluator in 8 of 15 articles (neurologist in 2 articles) and may refer patients for a second-line evaluation. The evaluation should include motor, cognitive, and visual assessment (proposed in 15, 13, and 8 articles, respectively). Specific motor tests are proposed in eight articles (cutoff values in four), whereas specific neuropsychological and visual tests are proposed in seven articles each (cutoff values in four and three articles, respectively). Conditional licenses are proposed in 11 of 15 articles, to facilitate driving for PD patients. We summarized our findings on a graphic of the procedure for driving fitness evaluation of PD patients. CONCLUSIONS: Neurological aspects of driving fitness evaluation of PD patients are recognized in most of the guidelines. Motor, neuropsychological, visual, and sleep assessment and medication review are key components. Clear-cut instructions regarding motor, neuropsychological, and visual tests and relative cutoff values are lacking. Conditional licenses and periodical reevaluation of driving fitness are important safety measures.

Analysis of mobile phone use engagement during naturalistic driving through explainable imbalanced machine learning.

While driver distraction remains an issue in modernized societies, technological advancements in data collection, storage and analysis provide the means for deeper insights of this complex phenomenon. In this research, factors influencing when driver distraction through mobile phone use occurs during naturalistic driving are investigated. Naturalistic data from a 6-stage, 230-driver experiment are exploited, in which drivers installed a non-intrusive driving recording application in their devices and conducted their trips normally across a 21-month timespan, coupled with corresponding questionnaire data. The various experiment stages involved providing progressively more behavioral feedback to drivers while continuing to record them. Subsequently, supervised Machine Learning XGBoost algorithms were employed to model the contributions of naturalistic driving and questionnaire features to the decision to engage mobile phone use. Mobile phone use percentages were heavily skewed towards zero, therefore imbalanced ML with a minority-oversampling approach in a binary format was employed. To increase the explainability offered by the algorithm, SHAP values were calculated for the informative features. Results indicate that the decision of drivers to use a mobile while driving is governed by a number of complex, non-linear relationships. Total trip distance is the most significant predictor variable by a wide margin, with mean SHAP values of 0.79 towards affecting the model decisions for the probability of mobile phone use of each driver. However, other variables influence the final predictions as well, such as the number of tickets in the last three years (m.SHAP = 0.30), declared mobile phone use (m.SHAP = 0.26), the amount and variety of provided feedback (m.SHAP = 0.17) (i.e. experiment phase number) and family member numbers (m.SHAP = 0.09) decrease the probability of using a mobile phone while driving. Conversely, increases in driver experience (m.SHAP = 0.22), driver age (m.SHAP = 0.11), engine capacity (m.SHAP = 0.11) and total kilometers driven annually (m.SHAP = 0.08) increase the probability of using a mobile phone in naturalistic driving conditions. SHAP dependency plots reveal non-linear effects present in almost all variables. Fuel consumption had a particularly strong non-linear effect, as higher values of this variable lead to both higher and lower probability of drivers using a mobile phone, deviating from the safer average. Legislation, campaigns and enforcement measures can be restructured to take advantage of gains margins in terms of understanding and predicting driver distraction behavior, as explored in the present study.

One year of COVID-19: Impacts on safe driving behavior and policy recommendations.

INTRODUCTION: In the unprecedented year of 2020, the rapid spread of COVID-19 disrupted everyday activities worldwide, leading the majority of countries to impose lockdowns and confine citizens in order to minimize the exponential increase in cases and casualties. To date, very few studies have been concerned with the effect of the pandemic on driving behavior and road safety, and usually explore data from a limited time span. METHOD: This study presents a descriptive overview of several driving behavior indicators as well as road crash data in correlation with the strictness of response measures in Greece and the Kingdom of Saudi Arabia (KSA). A k-means clustering approach was also employed to detect meaningful patterns. RESULTS: Results indicated that during the lockdown periods, speeds were increased by up to 6%, while harsh events were increased by about 35% in the two countries, compared to the period after the confinement. However, the imposition of another lockdown did not cause radical changes in Greek driving behavior during the late months of 2020. Finally, the clustering algorithm identified a "baseline," a "restrictions," and a "lockdown" driving behavior cluster, and it was shown that harsh braking frequency was the most distinctive factor. POLICY RECOMMENDATIONS: Based on these findings, policymakers should focus on the reduction and enforcement of speed limits, especially within urban areas, as well as the incorporation of active travelers in the current transport infrastructure.

From conflicts to crashes: Simulating macroscopic connected and automated driving vehicle safety.

Safety evaluation is a critical aspect through the future stages of automation development. Since there is a lack of historical and generalizable safety data in high levels of Connected and Autonomous Vehicles (CAVs), a possible approach to follow is the microscopic simulation method. Through microsimulation, vehicle trajectories are able to be exported and traffic conflicts to be identified using the Surrogate Safety Assessment Model (SSAM). Therefore, it is crucial to develop techniques in order to analyze conflict data extracted from microsimulation and evaluate crash data aiming to support road safety applications of automation technologies. This paper attempts to propose a safety evaluation approach for estimating crash rate of CAVs through microsimulation. For this purpose, the city center of Athens (Greece) was modelled using the Aimsun Next software paying attention to the calibration and validation of the model using real data of traffic characteristics. Moreover, different scenarios were formulated concerning different market penetration rates (MPRs) of CAVs and two fully automated generations (1st and 2nd generation) were simulated for modelling them. Subsequently, the SSAM software was used in order traffic conflicts to be identified and then converted to crash rate. Analysis of the outputs along with traffic data and network geometry characteristics were then conducted. The results indicated that in higher CAV MPRs, crash rates will be significantly lower as well as when the following-vehicle in the occurred conflict is a 2nd generation CAV. Lane change conflicts caused the highest crash rates compared to rear-end conflicts, which presented the lowest rates.

Real-time monitoring of driver distraction: State-of-the-art and future insights.

Driver distraction and inattention have been found to be major contributors to a large number of serious road crashes. It is evident that distraction reduces to a great extent driver perception levels as well as their decision making capability and the ability of drivers to control the vehicle. An effective way to mitigate the effects of distraction on crash probability, would be through monitoring the mental state of drivers or their driving behaviour and alerting them when they are in a distracted state. Towards that end, in recent years, several inexpensive and effective detection systems have been developed in order to cope with driver inattention. This study endeavours to critically review and assess the state-of-the-art systems and platforms measuring driver distraction or inattention. A thorough literature review was carried out in order to compare and contrast technologies that can be used to detect, monitor or measure driver's distraction or inattention. The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The results indicated that in most of the identified studies, driver distraction was measured with respect to its impact to driver behaviour. Real-time eye tracking systems, cardiac sensors on steering wheels, smartphone applications and cameras were found to be the most frequent devices to monitor and detect driver distraction. On the other hand, less frequent and effective approaches included electrodes, hand magnetic rings and glasses.

Texting While Driving: A Literature Review on Driving Simulator Studies.

Road safety is increasingly threatened by distracted driving. Studies have shown that there is a significantly increased risk for a driver of being involved in a car crash due to visual distractions (not watching the road), manual distractions (hands are off the wheel for other non-driving activities), and cognitive and acoustic distractions (the driver is not focused on the driving task). Driving simulators (DSs) are powerful tools for identifying drivers' responses to different distracting factors in a safe manner. This paper aims to systematically review simulator-based studies to investigate what types of distractions are introduced when using the phone for texting while driving (TWD), what hardware and measures are used to analyze distraction, and what the impact of using mobile devices to read and write messages while driving is on driving performance. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines. A total of 7151 studies were identified in the database search, of which 67 were included in the review, and they were analyzed in order to respond to four research questions. The main findings revealed that TWD distraction has negative effects on driving performance, affecting drivers' divided attention and concentration, which can lead to potentially life-threatening traffic events. We also provide several recommendations for driving simulators that can ensure high reliability and validity for experiments. This review can serve as a basis for regulators and interested parties to propose restrictions related to using mobile phones in a vehicle and improve road safety.

Driving Behaviour in Depression Based on Subjective Evaluation and Data from a Driving Simulator.

Road traffic collisions are a major issue for public health. Depression is characterized by mental, emotional and executive dysfunction, which may have an impact on driving behaviour. Patients with depression (N = 39) and healthy controls (N = 30) were asked to complete questionnaires and to drive on a driving simulator in different scenarios. Driving simulator data included speed, safety distance from the preceding vehicle and lateral position. Demographic and medical information, insomnia (Athens Insomnia Scale, AIS), sleepiness (Epworth Sleepiness Scale, ESS), fatigue (Fatigue Severity Scale, FSS), symptoms of sleep apnoea (StopBang Questionnaire) and driving (Driver Stress Inventory, DSI and Driver Behaviour Questionnaire, DBQ) were assessed. Gender and age influenced almost all variables. The group of patients with depression did not differ from controls regarding driving behaviour as assessed through questionnaires; on the driving simulator, patients kept a longer safety distance. Subjective fatigue was positively associated with aggression, dislike of driving, hazard monitoring and violations as assessed by questionnaires. ESS and AIS scores were positively associated with keeping a longer safety distance and with Lateral Position Standard Deviation (LPSD), denoting lower ability to keep a stable position. It seems that, although certain symptoms of depression (insomnia, fatigue and somnolence) may affect driving performance, patients drive more carefully eliminating, thus, their impact.

Off-road assessment of cognitive fitness to drive.

Road safety is a major issue in every society. The assessment of driving ability with a real vehicle is a lengthy and costly process; therefore, there is a growing need for the development of a neuropsychological battery that can provide a fast and reliable evaluation of a person's cognitive fitness to drive. In the present study, we examined the relationship of an off-road lab-type test, namely, the Driving Scenes test, with performance on a driving simulator, as well as the influence of cognitive factors on driving ability as evaluated by Driving Scenes. Our results demonstrated a relationship between Driving Scenes and driving simulator performance. They also showed that some cognitive factors (namely, selective attention and verbal memory), were predictive of driving ability (as determined by the Driving Scenes test), but not others (namely visuospatial perception/memory, working memory, and visuospatial recognition). In addition, age strongly predicted performance on this test (younger age was associated with better performance). The conclusions derived from the present study highlight the need to identify off-road tools with high predictive value in assessing driving ability.

Investigation of the speeding behavior of motorcyclists through an innovative smartphone application.

OBJECTIVE: The objective of the present study is twofold: (i) to explore the riding behavior of motorcyclists while speeding, based on detailed riding analytics collected by smartphone sensors, and (ii) to investigate whether personalized feedback can improve motorcyclist behavior. METHODS: In order to achieve the objective, a naturalistic riding experiment with a sample of 13 motorcyclists based on a smartphone application developed within the framework of the BeSmart project was conducted. Using risk exposure and riding behavior indicators calculated from smartphone sensor data, Generalized Linear Mixed-Effects Models are calibrated to correlate the percentage of riding time over the speed limit with other riding behavior indicators. An overall model was developed for all trips, as well as separate models for the parts of trips realized on different road types (urban and rural). RESULTS: Results indicate that the parameters of trip duration, distance driven during risky hours, morning peak hours and the number of harsh accelerations are all determined as statistically significant and positively correlated with the percentage of speeding time. Additionally, the provision of rider feedback and riding during afternoon peak hours are statistically significant and correlated with decreased percentages of speeding time. CONCLUSIONS: The outcomes of this study entail both scientific and social impacts. The present research contributes a preliminary example of the quantitative documentation of the impact of personalized rider feedback on one of the most important human risk factors; speeding. The ultimate objective when providing feedback to riders is to: (i) trigger their learning and self-assessment process, thus enabling them to gradually improve their performance and (ii) monitor the shift of riding behavior as the application provides feedback. The present results capture and quantify the positive effects of rider feedback, thus providing needed impetus for larger-scale applications as well as relevant policy interventions.

Impact of socioeconomic and transport indicators on road safety during the crisis period in Europe.

The risk of being involved in a road crash is typically influenced by mobility, which in turn is influenced by various socioeconomic indicators. This study aims to investigate the impact of socioeconomic and transport indicators on road safety during the economic crisis period in Europe. A database containing Human Development Index (HDI), suicides, passenger-kilometers and road fatalities per population was developed. Linear Mixed Models were applied for all the examined countries and the different groups that were selected for the period 2006-2015. The results led to the conclusion that HDI has the most important impact and its increase leads to road fatalities decrease. Moreover, the evolution of human development affects the outcomes of road crashes more than suicides and passenger-kilometers travelled. After the end of the crisis, the impact of human development is even higher. Concerning passenger-kilometers travelled, there is an increase in the relative impact on road fatalities after the end of the crisis.

Temporal analysis of driving efficiency using smartphone data.

This paper attempts to shed light on the temporal evolution of driving safety efficiency with the aim to acquire insights useful for both driving behavior and road safety improvement. Data exploited herein are collected from a sophisticated platform that uses smartphone device sensors during a naturalistic driving experiment, at which the driving behavior from a sample of two hundred (200) drivers during 7-months is continuously recorded in real time. The main driving behavior analytics taken into consideration for the driving assessment include distance travelled, acceleration, braking, speed and smartphone usage. The analysis is performed using statistical, optimization and machine learning techniques. The driver's safety efficiency index is estimated both in total and in several consecutive time windows to allow for the investigation of safety efficiency evolution in time. Initial data analysis results to the most critical components of microscopic driving behaviour evolution, which are used as inputs in the k-means algorithm to perform the clustering analysis. The main driving characteristics of each cluster are identified and lead to the conclusion that there are three main driving groups of the a) moderate drivers, b) unstable drivers and c) cautious drivers.

Analysis of the impact of COVID-19 on collisions, fatalities and injuries using time series forecasting: The case of Greece.

The current study aims to investigate the impact of the COVID-19 pandemic on road traffic collisions, fatalities, and injuries using time series analyses. To that aim, a database containing road collisions, fatalities, and slight injuries data from Greece were derived from the Hellenic Statistical Authority (HSA) and covered a ten-year timeframe (from January 2010 to August 2020. The chosen time period contained normal operations, as well as the period of the first COVID-19-induced lockdown period in Greece. Three different Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models were implemented in order to compare the observed measurements to forecasted values that were intended to depict assumed conditions; namely, without the appearance of the COVID-19 pandemic. Modelling results revealed that the total number of road collisions, fatalities, and slightly injured were decreased, mainly due to the sharp traffic volume decrease. However, the percentage reduction of the collision variables and traffic volume were found to be disproportionate, which probably indicates that more collisions occurred with regard to the prevailing traffic volume. An additional finding is that fatalities and slightly injured rates were significantly increased during the lockdown period and the subsequent month. Overall, it can be concluded that a worse performance was identified in terms of road safety. Since subsequent waves of COVID-19 cases and other pandemics may reappear in the future, the outcomes of the current study may be exploited for the improvement of road safety from local authorities and policymakers.

Examining the relationship between impaired driving and past crash involvement in Europe: Insights from the ESRA study.

Driving under the influence of alcohol, drugs and fatigue are all important factors of crash causation. Exploring the link between driver attitudes and crash involvement provides understanding on these important issues. To that end, questionnaire answers of car drivers disclosing their attitudes on the impacts of driving under the influence of alcohol, drugs and fatigue, and their relationship with past crash involvement as car drivers were analysed. A two-step approach is adopted: Principal Component Analysis (PCA) was employed to consolidate relative questions in numeric factor quantities. Afterwards, binary logistic regression was implemented on the calculated component scores to determine the impact of perspectives of road users for each factor on past crash involvement of car drivers. Data from the international ESRA2015 survey were utilized. PCA indicated that it is possible to meaningfully merge 29 ESRA2015 questions relevant to driving under the influence of alcohol, drugs and fatigue into 8 informative components accounting for an adequate percentage of variance. Binary logistic analysis indicated that components involving overall personal and communal acceptance of impaired driving, overall and past year personal behaviour towards impaired driving and frequency of typical journey checks by traffic police were all quantities positively correlated with past crash involvement.