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Understanding Traffic Accidents among Young Drivers in Qatar

Author

Listed:
  • Faris Tarlochan

    (Department of Mechanical and Industrial Engineering, Qatar University, Doha P.O. Box 2713, Qatar)

  • Mohamed Izham Mohamed Ibrahim

    (Department of Clinical Pharmacy and Practice, College of Pharmacy, QU Health, Qatar University, Doha P.O. Box 2713, Qatar)

  • Batool Gaben

    (Department of Mechanical and Industrial Engineering, Qatar University, Doha P.O. Box 2713, Qatar)

Abstract

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.

Suggested Citation

  • Faris Tarlochan & Mohamed Izham Mohamed Ibrahim & Batool Gaben, 2022. "Understanding Traffic Accidents among Young Drivers in Qatar," IJERPH, MDPI, vol. 19(1), pages 1-13, January.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:1:p:514-:d:716983
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    References listed on IDEAS

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    1. Melissa R. Freire & Cassandra Gauld & Angus McKerral & Kristen Pammer, 2021. "Identifying Interactive Factors That May Increase Crash Risk between Young Drivers and Trucks: A Narrative Review," IJERPH, MDPI, vol. 18(12), pages 1-20, June.
    2. Anna Tzortzi & Melpo Kapetanstrataki & Vaso Evangelopoulou & Panagiotis Behrakis, 2021. "Driving Behavior That Limits Concentration: A Nationwide Survey in Greece," IJERPH, MDPI, vol. 18(8), pages 1-16, April.
    3. Natalia Casado-Sanz & Begoña Guirao & Antonio Lara Galera & Maria Attard, 2019. "Investigating the Risk Factors Associated with the Severity of the Pedestrians Injured on Spanish Crosstown Roads," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    4. Danish Farooq & Sarbast Moslem & Rana Faisal Tufail & Omid Ghorbanzadeh & Szabolcs Duleba & Ahsen Maqsoom & Thomas Blaschke, 2020. "Analyzing the Importance of Driver Behavior Criteria Related to Road Safety for Different Driving Cultures," IJERPH, MDPI, vol. 17(6), pages 1-14, March.
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    Cited by:

    1. Darcin Akin & Virginia P. Sisiopiku & Ali H. Alateah & Ali O. Almonbhi & Mohammed M. H. Al-Tholaia & Khaled A. Alawi Al-Sodani, 2022. "Identifying Causes of Traffic Crashes Associated with Driver Behavior Using Supervised Machine Learning Methods: Case of Highway 15 in Saudi Arabia," Sustainability, MDPI, vol. 14(24), pages 1-36, December.
    2. Wei Zheng & Hongliang Qiu & Alastair M. Morrison, 2023. "Applying a Combination of SEM and fsQCA to Predict Tourist Resource-Saving Behavioral Intentions in Rural Tourism: An Extension of the Theory of Planned Behavior," IJERPH, MDPI, vol. 20(2), pages 1-23, January.
    3. Mireille Megnidio-Tchoukouegno & Jacob Adedayo Adedeji, 2023. "Machine Learning for Road Traffic Accident Improvement and Environmental Resource Management in the Transportation Sector," Sustainability, MDPI, vol. 15(3), pages 1-19, January.

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