Assessing the Road Traffic Crashes among Novice Female Drivers in Saudi Arabia

Modeling Driver Behavior in Road Traffic Simulation

Driver behavior models are an important part of road traffic simulation modeling. They encompass characteristics such as mood, fatigue, and response to distracting conditions. The relationships between external factors and the way drivers perform tasks can also be represented in models. This article proposes a methodology for establishing parameters of driver behavior models. The methodology is based on road traffic data and determines the car-following model and routing algorithm and their parameters that best describe driving habits. Sequential and parallel implementation of the methodology through the urban mobility simulator SUMO and Python are proposed. Four car-following models and three routing algorithms and their parameters are investigated. The results of the performed simulations prove the applicability of the methodology. Based on more than 7000 simulations performed, it is concluded that in future experiments of the traffic in Plovdiv it is appropriate to use a Contraction Hierarchies routing algorithm with the default routing step and the Krauss car-following model with the default configuration parameters.

Car accidents and risky driving behaviors among young drivers from the Eastern Province, Saudi Arabia

Objective: In 2019, road traffic injuries were the second leading cause of death in Saudi Arabia, affecting people of all ages. While multiple factors contribute to crashes, studies highlight the role of risky driving in increasing the chances of car crashes occurring. This study examined the one-year frequency of car crashes and risky driving behaviors among young drivers of both sexes in the Saudi Arabia's Eastern Province, as well as the possible related factors and predictors that contribute toward high car crash rates.Methods: A cross-sectional study was conducted between November 2021 and February 2022 at AlAsala Colleges among young drivers aged 18 years and above. A total of 424 students were selected using stratified random sampling and answered an anonymous, self-administered, structured web-based questionnaire and the Arabic version of the Driver Behavior Questionnaire (DBQ).Results: Car crashes were reported by 26.4% of young male and 19.3% of young female drivers. Male drivers were more aggressive and exhibited more speeding-related behaviors than female drivers, regardless of slight sex differences in unsafe driving behavior. Driving experience, along with other driving characteristics and DBQ items, was found to be significantly associated with road crashes. Furthermore, the type of car, traffic violations, using a phone while driving via headphones, and a distraction-related factor, were all predictive of crash involvement.Conclusions: Young Saudi male drivers were slightly more likely to have car crashes and engage in risky driving practices than young female drivers. Various factors that are related with and predict crashes should be addressed by a collaborative multi-layered strategic plan, with interventions at several levels.

Involvement of Road Users from the Productive Age Group in Traffic Crashes in Saudi Arabia: An Investigative Study Using Statistical and Machine Learning Techniques

Road traffic crashes (RTCs) are a major problem for authorities and governments worldwide. They incur losses of property, human lives, and productivity. The involvement of teenage drivers and road users is alarmingly prevalent in RTCs since traffic injuries unduly impact the working-age group (15–44 years). Therefore, research on young people’s engagement in RTCs is vital due to its relevance and widespread frequency. Thus, this study focused on evaluating the factors that influence the frequency and severity of RTCs involving adolescent road users aged 15 to 44 in fatal and significant injury RTCs in Al-Ahsa, Saudi Arabia. In this study, firstly, descriptive analyses were performed to justify the target age group analysis. Then, prediction models employing logistic regression and CART were created to study the RTC characteristics impacting the target age group participation in RTCs. The most commonly observed types of crashes are vehicle collisions, followed by multiple-vehicle and pedestrian crashes. Despite its low frequency, the study area has a high severity index for RTCs, where 73% of severe RTCs include individuals aged 15 to 44. Crash events with a large number of injured victims and fatalities are more likely to involve people in the target age range, according to logistic regression and CART models. The CART model also suggests that vehicle overturn RTCs involving victims in the target age range are more likely to occur as a result of driver distraction, speeding, not giving way, or rapid turning. As compared with the logistic regression model, the CART model was more convenient and accurate for understanding the trends and predicting the involvement probability of the target age group in RTCs; however, this model requires a higher processing time for its development.

Insights into Factors Affecting Traffic Accident Severity of Novice and Experienced Drivers: A Machine Learning Approach

Traffic accidents have significant financial and social impacts. Reducing the losses caused by traffic accidents has always been one of the most important issues. This paper presents an effort to investigate the factors affecting the accident severity of drivers with different driving experience. Special focus was placed on the combined effect of driving experience and age. Based on our dataset (traffic accidents that occurred between 2005 and 2021 in Shaanxi, China), CatBoost model was applied to deal with categorical feature, and SHAP (Shapley Additive exPlanations) model was used to interpret the output. Results show that accident cause, age, visibility, light condition, season, road alignment, and terrain are the key factors affecting accident severity for both novice and experienced drivers. Age has the opposite impact on fatal accident for novice and experienced drivers. Novice drivers younger than 30 or older than 55 are prone to suffer fatal accident, but for experienced drivers, the risk of fatal accident decreases when they are young and increases when they are old. These findings fill the research gap of the combined effect of driving experience and age on accident severity. Meanwhile, it can provide useful insights for practitioners to improve traffic safety for novice and experienced drivers.