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Towards Sustainable Road Safety in Saudi Arabia: Exploring Traffic Accident Causes Associated with Driving Behavior Using a Bayesian Belief Network

Author

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  • Muhammad Muhitur Rahman

    (Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

  • Md Kamrul Islam

    (Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

  • Ammar Al-Shayeb

    (Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

  • Md Arifuzzaman

    (Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

Abstract

Understanding the causes and effects of road accidents is critical for developing road and action plans in a country. The causation hypothesis elucidates how accidents occur and may be applied to accident analysis to more precisely anticipate, prevent, and manage road safety programs. Driving behavior is a critical factor to consider when determining the causes of traffic accidents. Inappropriate driving behaviors are a set of acts taken on the roadway that can result in aberrant conditions that may result in road accidents. In this study, using Al-Ahsa city in Saudi Arabia’s Eastern Province as a case study, a Bayesian belief network (BBN) model was established by incorporating an expectation–maximization algorithm. The model examines the relationships between indicator variables with a special focus on driving behavior to measure the uncertainty associated with accident outcomes. The BBN was devised to analyze intentional and unintentional driving behaviors that cause different types of accidents and accident severities. The results showed when considering speeding alone, there is a 26% likelihood that collision will occur; this is a 63% increase over the initial estimate. When brake failure was considered in addition to speeding, the likelihood of a collision jumps from 26% to 33%, more than doubling the chance of a collision when compared to the initial value. These findings demonstrated that the BBN model was capable of efficiently investigating the complex linkages between driver behavior and the accident causes that are inherent in road accidents.

Suggested Citation

  • Muhammad Muhitur Rahman & Md Kamrul Islam & Ammar Al-Shayeb & Md Arifuzzaman, 2022. "Towards Sustainable Road Safety in Saudi Arabia: Exploring Traffic Accident Causes Associated with Driving Behavior Using a Bayesian Belief Network," Sustainability, MDPI, vol. 14(10), pages 1-22, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6315-:d:821205
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    References listed on IDEAS

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    1. Kuhnert, Petra M. & Do, Kim-Anh & McClure, Rod, 2000. "Combining non-parametric models with logistic regression: an application to motor vehicle injury data," Computational Statistics & Data Analysis, Elsevier, vol. 34(3), pages 371-386, September.
    2. Trucco, P. & Cagno, E. & Ruggeri, F. & Grande, O., 2008. "A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 845-856.
    3. Yichuan Peng & Leyi Cheng & Yuming Jiang & Shengxue Zhu, 2021. "Examining Bayesian network modeling in identification of dangerous driving behavior," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-20, August.
    4. Rahman, Muhammad Muhitur & Hagare, Dharma & Maheshwari, Basant, 2016. "Bayesian Belief Network analysis of soil salinity in a peri-urban agricultural field irrigated with recycled water," Agricultural Water Management, Elsevier, vol. 176(C), pages 280-296.
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    Citations

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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. Mohd Anjum & Sana Shahab, 2023. "Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    3. Huiqin Chen & Hao Liu & Hailong Chen & Jing Huang, 2023. "Towards Sustainable Safe Driving: A Multimodal Fusion Method for Risk Level Recognition in Distracted Driving Status," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
    4. Mohd Anjum & Sana Shahab, 2023. "Improving Autonomous Vehicle Controls and Quality Using Natural Language Processing-Based Input Recognition Model," Sustainability, MDPI, vol. 15(7), pages 1-21, March.
    5. Shujaat Abbas & Hazrat Yousaf & Shabeer Khan & Mohd Ziaur Rehman & Dmitri Blueschke, 2023. "Analysis and Projection of Transport Sector Demand for Energy and Carbon Emission: An Application of the Grey Model in Pakistan," Mathematics, MDPI, vol. 11(6), pages 1-14, March.
    6. Nattawut Pumpugsri & Wanchai Rattanawong & Varin Vongmanee, 2023. "Development of a Safety Heavy-Duty Vehicle Model Considering Unsafe Acts, Unsafe Conditions and Near-Miss Events Using Structural Equation Model," Sustainability, MDPI, vol. 15(16), pages 1-20, August.
    7. Afaq Khattak & Hamad Almujibah & Ahmed Elamary & Caroline Mongina Matara, 2022. "Interpretable Dynamic Ensemble Selection Approach for the Prediction of Road Traffic Injury Severity: A Case Study of Pakistan’s National Highway N-5," Sustainability, MDPI, vol. 14(19), pages 1-18, September.

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