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

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dc.citedby4
dc.contributor.authorAbdulwahid S.N.en_US
dc.contributor.authorMahmoud M.A.en_US
dc.contributor.authorIbrahim N.en_US
dc.contributor.authorZaidan B.B.en_US
dc.contributor.authorAmeen H.A.en_US
dc.contributor.authorid57361650900en_US
dc.contributor.authorid55247787300en_US
dc.contributor.authorid9337335600en_US
dc.contributor.authorid35070872100en_US
dc.contributor.authorid57211977266en_US
dc.date.accessioned2023-05-29T09:37:07Z
dc.date.available2023-05-29T09:37:07Z
dc.date.issued2022
dc.descriptionaccident prevention; aggression; cycle transport; modeling; real time; road traffic; statistical analysis; transportation safety; accident; aggression; aggressive driving; Article; data analysis; decision tree; driving ability; global positioning system; human; machine learning; motorcycle; motorcyclist; road safety; support vector machine; car driving; prevention and control; safety; traffic accident; Accidents, Traffic; Aggressive Driving; Automobile Driving; Humans; Motorcycles; Safetyen_US
dc.description.abstractDriving 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 nonaggressive 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. Copyright: � 2022 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7704
dc.identifier.doi10.3390/ijerph19137704
dc.identifier.issue13
dc.identifier.scopus2-s2.0-85132410098
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85132410098&doi=10.3390%2fijerph19137704&partnerID=40&md5=59e766efae0a1eaf33494b00569400fc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26840
dc.identifier.volume19
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleInternational Journal of Environmental Research and Public Health
dc.titleModeling Motorcyclists� Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidentsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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