Understanding Traffic Accidents among Young Drivers in Qatar

Young drivers are generally associated with risky driving behaviors that can lead to crash involvement. Many self-report measurement scales are used to assess such risky behaviors. This study is aimed to understand the risky driving behaviors of young adults in Qatar and how such behaviors are associated with crash involvement. This was achieved through the usage of validated self-report measurement scales adopted for the Arabic context. A nationwide cross-sectional and exploratory study was conducted in Qatar from January to April 2021. Due to the Covid-19 pandemic, the survey was conducted online. Therefore, respondents were selected conveniently. Hence, the study adopted a non-probability sampling method in which convenience and snowball sampling were used. A total of 253 completed questionnaires were received, of which 57.3% were female, and 42.7% were male. Approximately 55.8% of these young drivers were involved in traffic accidents after obtaining their driving license. On average, most young drivers do have some risky driving behavior accompanied by a low tendency to violate traffic laws, and their driving style is not significantly controlled by their personality on the road. The older young drivers are more involved in traffic accidents than the younger drivers, i.e., around 1.5 times more likely. Moreover, a young male driver is 3.2 times less likely to be involved in traffic accidents than a female driver. In addition, males are only 0.309 times as likely as females to be involved in an accident and have approximately a 70% lower likelihood of having an accident versus females. The analysis is complemented with the association between young drivers’ demographic background and psychosocial-behavioral parameters (linking risky driving behavior, personality, and obligation effects on crash involvement). Some interventions are required to improve driving behavior, such as driving apps that are able to monitor and provide corrective feedback.

Driving Performance in Older Adults: Current Measures, Findings, and Implications for Roadway Safety

Background and Objectives Over 10,000 people a day turn 65 in the United States. For many older adults, driving represents an essential component of independence and is one of the most important factors in overall mobility. Recent survey studies in older adults suggest that up to 60% of older adult drivers with mild cognitive impairment, and up to 30% with dementia, continue to drive. The purpose of this review is to provide a comprehensive and detailed resource on the topics of cognition and driving for clinicians, researchers, and policymakers working on efforts related to older adult drivers. Research Design and Methods Publications on PubMed and Medline and discussions with experts working in geriatrics, technology, driving policy, psychology, and diverse aspects of driving performance were utilized to inform the current review. Results Research indicates that there is a complex and inverse correlation between multiple cognitive measures, driving performance, and risky driving behaviors. The fragmented nature of available peer-reviewed literature, and a reliance on correlative data, do not currently allow for the identification of the temporal and reciprocal nature of the interplay between cognition and driving endpoints. Discussion and Implications There are currently no widely accepted definitions, conceptual models, or uniform set of analyses for conducting geriatric research that is focused on driving. Establishing conventions for conducting research that harmonizes the fields of geriatrics, cognition, and driving research is critical for the development of the evidence base that will inform clinical practice and road safety policy.

Driving Behavior Based Relative Risk Evaluation Using a Nonparametric Optimization Method

Evaluating risks when driving is a valuable method by which to make people better understand their driving behavior, and also provides the basis for improving driving performance. In many existing risk evaluation studies, however, most of the time only the occurrence frequency of risky driving events is considered in the time dimension and fixed weights allocation is adopted when constructing a risk evaluation model. In this study, we develop a driving behavior-based relative risk evaluation model using a nonparametric optimization method, in which both the frequency and the severity level of different risky driving behaviors are taken into account, and the concept of relative risk instead of absolute risk is proposed. In the case study, based on the data from a naturalistic driving experiment, various risky driving behaviors are identified, and the proposed model is applied to assess the overall risk related to the distance travelled by an individual driver during a specific driving segment, relative to other drivers on other segments, and it is further compared with an absolute risk evaluation. The results show that the proposed model is superior in avoiding the absolute risk quantification of all kinds of risky driving behaviors, and meanwhile, a prior knowledge on the contribution of different risky driving behaviors to the overall risk is not required. Such a model has a wide range of application scenarios, and is valuable for feedback research relating to safe driving, for a personalized insurance assessment based on drivers’ behavior, and for the safety evaluation of professional drivers such as ride-hailing drivers.

Drivers’ Risk Perception and Risky Driving Behavior under Low Illumination Conditions: Modified Driver Behavior Questionnaire (DBQ) and Driver Skill Inventory (DSI)

Most road crashes are caused by human factors. Risky behaviors and lack of driving skills are two human factors that contribute to crashes. Considering the existing evidence, risky driving behaviors and driving skills have been regarded as potential decisive factors explaining and preventing crashes. Nighttime accidents are relatively frequent and serious compared with daytime accidents. Therefore, it is important to focus on driving behaviors and skills to reduce traffic accidents and enhance safe driving in low illumination conditions. In this paper, we examined the relation between drivers’ risk perception and propensity for risky driving behavior and conducted a comparative analysis of the associations between risk perception, propensity for risky driving behavior, and other factors in the presence and absence of streetlights. Participants in Hefei city, China, were asked to complete a demographic questionnaire, the Driver Behavior Questionnaire (DBQ), and the Driver Skill Inventory (DSI). Multiple linear regression analyses identified some predictors of driver behavior. The results indicated that both the DBQ and DSI are valuable instruments in traffic safety analysis in low illumination conditions and indicated that errors, lapses, and risk perception were significantly different between with and without streetlight conditions. Pearson’s correlation test found that elderly and experienced drivers had a lower likelihood of risky driving behaviors when driving in low illumination conditions, and crash involvement was positively related to risky driving behaviors. Regarding the relationship between study variables and driving skills, the research suggested that age, driving experience, and annual distance were positively associated with driving skills, while myopia, penalty points, and driving self-assessment were negatively related to driving skills. Furthermore, the differences across age groups in errors, lapses, violations, and risk perception in the presence of streetlights were remarkable, and the driving performance of drivers aged 45–55 years was superior to that of drivers in other age groups. Finally, multiple linear regression analyses showed that education background and crash involvement had a positive influence on error, whereas risk perception had a negative effect on errors; crash involvement had a positive influence, while risk perception had a negative effect on lapse; driving experience and crash involvement had a positive influence on violation; and age had a negative influence on it.

ConTEXT: Context-aware Adaptive SMS Client for Drivers to Reduce Risky Driving Behaviors

Text messaging while driving has been considered a dangerous activity that may lead to serious injuries and traffic fatalities. Several assistive technologies and solutions have been developed to simplify texting activity. However, due to inconsistent and complex interface design, lack of logical navigational order, lack of context, complicated text-entry layouts, and laborious activities, the existing texting-related activities can lead to accidents. This paper recognized the risky driving patterns using the real-time AutoLog application. Based on this risky driving behavior, we have proposed ConTEXT, a usable SMS client, to overcome the issues pertaining to the usability of textual activities on smartphones while driving. ConTEXT application is evaluated both empirically as well as through real-time AutoLog application. We have collected data from 117 drivers through a questionnaire. The results show that the data is found reliable also alpha scores for all factors seem internally consistent as it ranges from 0.70 to 0.79 which is good. Similarly, we have reported Principal Component Factor Analysis (PCFA), which was found satisfied and appropriate as the Eigenvalue for all the factors is greater than zero. Furthermore, results obtained from the AutoLog dataset show an improved user experience, better control over the touch screen with minimum visual, physical, and mental load.

Self-reported Motorcycle Riding Behavior in Southeast of Iran

Background: Motorcyclists are among the greatest vulnerable individuals of road accident victims. Their behavior has a significant correlation with increased injury and mortality rate. Determining the risky and unsafe behaviors of motorcycle drivers is necessary for preventing riders and other citizen from potential accident risks. Objectives: The aim of this study was to determine the risky driving behaviors of motorcyclists in Iran. Methods: A cross-sectional study was done in 2019 in Sistan and Baluchestan Province as the second widest province of Iran. Using randomized sampling method, we included 613 motorcyclists from the province. To collect data, the Persian version of Motorcycle Riding Behavior Questionnaire (MRBQ), as a standard questionnaire, was used. For data analysis, descriptive and analytical statistics such as one-way analysis of variance (ANOVA), t-test, and linear regression were used by SPSS software version 21. Results: The age range of 57% of the motor riders was 15 - 30 years, and 50% of them did not use any safety equipment. About 58% of the subjects had started motorcycle riding under 18 years old, and 73% of them did not have a motorcycle riding license. Moreover, more than 50% of motorcyclists used mobile phones while driving. The mean score of driving behavior (106 ± 22) was desirable. Based on multivariate analysis, job, average amount of riding, lacking a riding license, type of motor, alert from police, non-fasting helmet band, exceeding speed limits, fatigue, and hand-free riding were the main predictors of risky riding score (P < 0.05). Conclusions: According to our results, the riding behavior of motorcyclists was desirable; however, many people used motorcycles without a license and safety equipment, which increases high-risk behaviors. Considering the potential dangers of motor riders, it seems necessary to hold training courses to obtain motorcycle certification and how to use safety equipment.

Representing Route Familiarity Using the Abstraction Hierarchy Framework

Familiarity with a route is influenced by levels of dynamic and static knowledge about the route and the route network such as type of roads, infrastructure, traffic conditions, purpose of travel, weather, departure time, etc. To better understand and develop route choice models that can incorporate more meaningful representations of route familiarity, OBDII devices were installed in the vehicles of 32 drivers, 65 years and older, for a period of three months. Personalized web-based trip diaries were used to provide older drivers with post-trip feedback reports about their risky driving behaviors, and collect feedback about their route familiarity, preferences, and reasons for choosing the route driven vs. an alternate low-risk route. Feedback responses were analyzed and mapped onto an abstraction hierarchy framework, which showed that among older drivers, route familiarity depends not only on higher abstraction levels such as trip goals, purpose, and driving strategies, but also on the lower levels of demand on driving skills, and characteristics of road type. Additionally, gender differences were identified at the lower levels of the familiarity abstraction model, especially for driving challenges and the driving environment. Results from the analyses helped highlight the multi-faceted nature of route familiarity, which can be used to build the necessary levels of granularity for modelling and interpretation of spatial and contextual route choice recommendation systems for specific population groups such as older drivers.