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

Al-Garawi, Najah; Dalhat, M.A.; Aga, Omer

doi:10.3390/su13158613

Cited by 9 publications

(9 citation statements)

References 25 publications

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“…In 2018, Saudi Arabia allowed female citizens to have a license, which increased the number of young drivers in the country. Car ownership and RTCs are also expected to increase [49]. Hence, it is crucial to proactively mitigate RTCs involving young road users in Saudi Arabia.…”

Section: Yang Zeyin Et Al [39] 18-25 Years Chinamentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Yang Zeyin Et Al [39] 18-25 Years Chinamentioning

confidence: 99%

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

et al. 2022

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“…"Assessing the road traffic crashes among novice female drivers in Saudi Arabia" [14] To In articles [7,12,13], the importance of advanced real-time driver information systems is emphasized. The authors of [7] provided the results of a field experiment that confirms the advances of the presented application: increased driver attention and pursuit of safe driving.…”

Section: Title Objective Type Of Analyzed Data Conclusionmentioning

confidence: 99%

“…The authors of [14][15][16] examine the factors of driver behavior that influence traffic accidents in different cultures. Based on the three studies, it can be concluded that the factors that affect traffic safety vary widely among different cultures.…”

Section: Title Objective Type Of Analyzed Data Conclusionmentioning

confidence: 99%

Modeling Driver Behavior in Road Traffic Simulation

Mecheva¹,

Furnadzhiev²,

Kakanakov³

2022

Sensors

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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.

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“…Al-Garawi et al [18] recently assessed traffic crashes among novice female drivers in the KSA. Their study concluded that single, divorced/widowed, employed, and highincome female novice drivers were at a higher danger of being involved in road-traffic crashes, whether they hired personal trainers or not.…”

Section: Introductionmentioning

confidence: 99%

Geographical Distribution and Modeling of the Impact of Women Driving Cars on the Sustainable Development of Saudi Arabia

Al-Garawi

Anıl

2021

Sustainability

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Background: This work investigated attitudes and public perception regarding the impact of allowing women to drive on social, environmental, and economic aspects of the sustainable development in the Kingdom of Saudi Arabia (KSA). The study includes the perspectives of both women and men towards the potential implications of this decree on society in general and women’s well-being in particular. Methods: The methodology consisted of an online survey that was conducted before and after the decree was activated in 2017, where 62,065 individuals participated from thirteen provinces of the KSA. Geographic information systems (GISs) and statistical methods were applied to the obtained datasets to examine the geographical distribution and modeling of the effect of women driving on sustainable development of the KSA. Results: The results show that the attitudes towards allowing women to drive are geographically and statistically diverse. The study revealed that the economic impact of women driving was the highest significance, especially in increasing employment opportunities for women and decreasing household travel expenses for women. Conclusion: Overall, 70.4% of respondents agreed that women driving will positively affect the sustainable development of the KSA in terms of social and economic impact. The study also revealed that geographic location is one of the dominating factors on the attitudes towards the social impact of women driving within 95% confidence interval. Additionally, participants strongly believe (with an agreement rate of 85.1%) that Saudi women driving will improve economic development and the female job market.

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

Cited by 9 publications

References 25 publications

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

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

Modeling Driver Behavior in Road Traffic Simulation

Geographical Distribution and Modeling of the Impact of Women Driving Cars on the Sustainable Development of Saudi Arabia

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