Interpolation of Quantile Regression to Estimate Driver’s Risk of Traffic Accident Based on Excess Speed

Quantile regression provides a way to estimate a driver’s risk of a traffic accident by means of predicting the percentile of observed distance driven above the legal speed limits over a one year time interval, conditional on some given characteristics such as total distance driven, age, gender, percent of urban zone driving and night time driving. This study proposes an approximation of quantile regression coefficients by interpolating only a few quantile levels, which can be chosen carefully from the unconditional empirical distribution function of the response. Choosing the levels before interpolation improves accuracy. This approximation method is convenient for real-time implementation of risky driving identification and provides a fast approximate calculation of a risk score. We illustrate our results with data on 9614 drivers observed over one year.

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.

Traffic Crimes and risky driving: The role of personality and driving anger

The current study aims to examine the influence of personality traits (alternative Zuckerman model) and driving anger in the explanation of risky driving style in individuals convicted for road safety offences (N = 245), using as a basis an adaptation of the context-mediated model. This is a transversal, descriptive study designed to be implemented by means of surveys, in which took part 245 men convicted of road safety offences from five prisons in Galicia (a region in northwestern Spain) took part. The average age of the participants was 38.73 years (Sx-9.61), with a range between 18 and 64 years. All participants had three or more years of driving experience. Our data shows that the Impulsive-Sensation Seeking (Imp-SS) personality trait had a direct and positive effect on dangerous driving, while the Activity (Act) trait had a direct but negative effect. The Aggression-Hostility (Agg-Host) trait, in turn, influenced the risky driving style, but not directly, but by raising driving anger levels, so it acted as a powerful mediator between the Aggression-Hostility (Agg-Hos) trait and the risky driving style. In general, our research partially replicates and expands previous findings regarding the model used, the aggression-hostility personality trait (Agg-Host) was placed in the distal context, driving anger in the proximal context, while age and personality traits Activity (Act) and Impulsive-Sensation Seeking (Imp-SS) were direct predictors. The results of this study may have practical implications for the detection and rehabilitation of offenders and penalties for road safety offences.

Cognitive Biases, Risk Perception, and Risky Driving Behaviour

This study evaluated the relationship between drivers’ cognitive biases (i.e., optimism bias, illusion of control) and risky driving behaviour. It also investigated the mediational role of risk perception in the relationship between cognitive biases and self-reported risky driving. The sample included 366 drivers (Mage = 39.13, SD = 13.63 years) who completed scales measuring optimism bias, illusion of control, risk perception, and risky driving behaviour, as well as demographic information. The results showed that risky driving behaviour was negatively predicted by optimism bias and positively predicted by the illusion of control. Further, risk perception negatively correlated with risky behaviour and also mediated the relation between both optimism bias and illusion of control with risky driving. The practical implications of these results for traffic safety and future research are discussed.

LDA-CBOW-Based Mining Model for Risky Driving Behavior in Traffic Accidents

Traffic accident data of traffic management department is recorded in unstructured text form, which contains a large number of characteristic descriptions related to risky driving behavior. However, such data has short text length and abundant professional vocabulary. Many text mining techniques cannot effectively analyze such text data. This paper proposes an improved LDA algorithm based on CBOW—LDA-CBOW model for the study of traffic accident text data containing illegal behaviors. This model can better extract the topics of traffic accident data and filter the keywords under the corresponding topics, which provides a better way to study the dependence relationship between traffic data and illegal behaviors. Experiments show that compared to other models, this model can better extract related topics of traffic accident data with higher model efficiency and better robustness.