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

Alhomoud, Mohsen Ali; AlSaleh, Essa; Alzaher, Bushra

doi:10.1080/15389588.2022.2113392

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…Moreover, emphasis should be placed on developing the necessary skills for critical driving maneuvers at road junctions and roundabouts to ensure safe and efficient driving(Anstey et al, 2005). Previous studies have identified a range of factors of risky driving, including age, gender, and experience(Alhomoud et al, 2022;Song et al, 2021), consistent with our findings. Likewise, previous studies have reported associations between experience of driving and driving behavior (Al-Tit, 2020; Fu and Lee, 2022).…”

supporting

confidence: 88%

Patterns of risky driving behaviors among Preventive Medicine Residents in the Kingdom of Saudi Arabia, 2023

Azhar,

Alghamdi

2023

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Introduction: Risky driving behavior is a significant public health problem that is responsible for the majority of fatal traffic accidents. This study evaluated the prevalence of risky driving behaviors among Preventive Medicine Residents across Saudi Arabia. Subjects and Methods: A cross-sectional study targeted a sample of 276 preventive medicine residents of all 10 programs in Saudi Arabia cities. A self-reported Manchester Driver Behavior Questionnaire reflecting risky driving behavior was used. Main Results: A total of 276 participants were included, with (60.9%) being male, almost half of the participants were within the 26-30 age group (47.8%). Regarding driving experience, the majority of participants (53.6%) had been driving for more than 9 years since obtaining their driving license. Of all participants, 14.5% reported racing for a one-car gap and 4.3% engaged in unofficial races and exhibited risky driving behaviors, 84.1% never participated in unofficial races and 43.5% never disregarded speed limits. The distance driven per day (RR=1.02, 95% CI: 1.01-1.03, p<0.001), males (RR=4.1, 95% CI: 3.1-, p<0.001), residency level (R1, R2, R3) (RR=0.7, 95% CI: 0.6-0.8, p<0.001), and years of driving experience (RR=1.8, 95% CI: 1.4-2.3, p=0.018) significantly contribute to the occurrence of dangerous driving. Conclusion and Recommendations: A substantial proportion of study participants admitted engaging in risky driving behaviors indicating a propensity for dangerous driving habits.Implementing effective strategies can improve road safety and reduce the incidence of risky driving behaviors among medical residents and the general public.

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confidence: 88%

Patterns of risky driving behaviors among Preventive Medicine Residents in the Kingdom of Saudi Arabia, 2023

Azhar,

Alghamdi

2023

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“…This is especially true concerning the complex needs of these vulnerable groups. Despite this, there is a disturbing lack of data and documentation in countries with lower and intermediate incomes, such as Saudi Arabia [ 18 , 19 ]. This is especially alarming considering the distinct road conditions and regulatory regimes distinctive to these places.…”

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confidence: 99%

Comparing fatal crash risk factors by age and crash type by using machine learning techniques

Alshehri,

Alanazi,

Yosri

et al. 2024

PLoS ONE

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This study aims to use machine learning methods to examine the causative factors of significant crashes, focusing on accident type and driver’s age. In this study, a wide-ranging data set from Jeddah city is employed to look into various factors, such as whether the driver was male or female, where the vehicle was situated, the prevailing weather conditions, and the efficiency of four machine learning algorithms, specifically XGBoost, Catboost, LightGBM and RandomForest. The results show that the XGBoost Model (accuracy of 95.4%), the CatBoost model (94% accuracy), and the LightGBM model (94.9% accuracy) were superior to the random forest model with 89.1% accuracy. It is worth noting that the LightGBM had the highest accuracy of all models. This shows various subtle changes in models, illustrating the need for more analyses while assessing vehicle accidents. Machine learning is also a transforming tool in traffic safety analysis while providing vital guidelines for developing accurate traffic safety regulations.

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“…One issue with the TPB model is that it presupposes that human action is reasonable; it fails to consider perceptual or unconscious reasoning (2,69). Previous studies have also found that perceived risk (70), descriptive norms (i.e., what the typical person might do) (71), crash history (72), prototype favorability (i.e., the degree to which an individual favors (or disfavors) the prototype), and prototype similarity (i.e., the degree of perceived similarity of an individual to the prototype) (69) may affect risky traffic behaviors/behavioral intentions.…”

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confidence: 99%

Psychological Factors Influencing College Students’ Mobile Phone Use While Walking

Liu,

Wei,

Zhu

et al. 2023

Transportation Research Record: Journal of the Transportation R

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The likelihood of pedestrians exhibiting aberrant walking behaviors and being involved in traffic collisions increases substantially if they are distracted while walking because of using their mobile phones. To reduce the frequency with which pedestrians use their phones while walking, a detailed analysis of the elements that influence this behavior is required. This research studied people’s behavior and behavioral intentions to use mobile phones while walking, using an expanded version of the theory of planned behavior. Four hundred sixty-three college students completed an online survey that assessed attitudes and behaviors related to mobile phone use. The results showed that attitude, perceived behavioral control, mobile phone addiction, crash history, and prototype similarity significantly affected behavioral intentions, whereas subjective norms, descriptive norms, perceived risk, and prototype favorability did not. In addition, behavioral intentions, perceived behavioral control, mobile phone addiction, and crash history had significant impacts on behavior, whereas perceived risk did not. The results from this study could be utilized to develop interventions to reduce the frequency with which college students use their mobile phones while walking, which is essential for improving road safety.

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

Cited by 7 publications

References 17 publications

Patterns of risky driving behaviors among Preventive Medicine Residents in the Kingdom of Saudi Arabia, 2023

Patterns of risky driving behaviors among Preventive Medicine Residents in the Kingdom of Saudi Arabia, 2023

Comparing fatal crash risk factors by age and crash type by using machine learning techniques

Psychological Factors Influencing College Students’ Mobile Phone Use While Walking

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