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Article

Modeling Motorcyclists’ Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents

1
College of Graduate Studies, Universiti Tenaga Nasional, Kajang 43000, Malaysia
2
Institute of Informatics and Computing in Energy, Department of Computing, College of Computing and Informatics, Universiti Tenaga Nasional, Kajang 43000, Malaysia
3
Future Technology Research Center, National Yunlin University of Science and Technology, Douliu 64002, Taiwan
4
Department of Computer Techniques Engineering, Al-Mustaqbal University College, Hillah 51001, Iraq
*
Authors to whom correspondence should be addressed.
Academic Editor: Paul B. Tchounwou
Int. J. Environ. Res. Public Health 2022, 19(13), 7704; https://doi.org/10.3390/ijerph19137704
Received: 30 April 2022 / Revised: 11 June 2022 / Accepted: 15 June 2022 / Published: 23 June 2022
(This article belongs to the Special Issue Driving Behavior and Traffic Safety)
Driving behavior is considered one of the most important factors in all road crashes, accounting for 40% of all fatal and serious accidents. Moreover, aggressive driving is the leading cause of traffic accidents that jeopardize human life and property. By evaluating data collected by various collection devices, it is possible to detect dangerous and aggressive driving, which is a huge step toward altering the situation. The utilization of driving data, which has arisen as a new tool for assessing the style of driving, has lately moved the concentration of aggressive recognition research. The goal of this study is to detect dangerous and aggressive driving profiles utilizing data gathered from motorcyclists and smartphone APPs that run on the Android operating system. A two-stage method is used: first, determine driver profile thresholds (rules), then differentiate between non-aggressive and aggressive driving and show the harmful conduct for producing the needed outcome. The data were collected from motorcycles using -Speedometer GPS-, an application based on the Android system, supplemented with spatiotemporal information. After the completion of data collection, preprocessing of the raw data was conducted to make them ready for use. The next steps were extracting the relevant features and developing the classification model, which consists of the transformation of patterns into features that are considered a compressed representation. Lastly, this study discovered a collection of key characteristics which might be used to categorize driving behavior as aggressive, normal, or dangerous. The results also revealed major safety issues related to driving behavior while riding a motorcycle, providing valuable insight into improving road safety and reducing accidents. View Full-Text
Keywords: aggressive behavior modeling; real-time data analysis; traffic violation; motorcyclists; road safety aggressive behavior modeling; real-time data analysis; traffic violation; motorcyclists; road safety
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MDPI and ACS Style

Abdulwahid, S.N.; Mahmoud, M.A.; Ibrahim, N.; Zaidan, B.B.; Ameen, H.A. Modeling Motorcyclists’ Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents. Int. J. Environ. Res. Public Health 2022, 19, 7704. https://doi.org/10.3390/ijerph19137704

AMA Style

Abdulwahid SN, Mahmoud MA, Ibrahim N, Zaidan BB, Ameen HA. Modeling Motorcyclists’ Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents. International Journal of Environmental Research and Public Health. 2022; 19(13):7704. https://doi.org/10.3390/ijerph19137704

Chicago/Turabian Style

Abdulwahid, Sarah N., Moamin A. Mahmoud, Nazrita Ibrahim, Bilal B. Zaidan, and Hussein A. Ameen. 2022. "Modeling Motorcyclists’ Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents" International Journal of Environmental Research and Public Health 19, no. 13: 7704. https://doi.org/10.3390/ijerph19137704

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