Modeling Motorcyclists� Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents
accident 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; motorcyc...
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2023
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my.uniten.dspace-268402023-05-29T17:37:07Z Modeling Motorcyclists� Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents Abdulwahid S.N. Mahmoud M.A. Ibrahim N. Zaidan B.B. Ameen H.A. 57361650900 55247787300 9337335600 35070872100 57211977266 accident 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; 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 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. Final 2023-05-29T09:37:07Z 2023-05-29T09:37:07Z 2022 Article 10.3390/ijerph19137704 2-s2.0-85132410098 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132410098&doi=10.3390%2fijerph19137704&partnerID=40&md5=59e766efae0a1eaf33494b00569400fc https://irepository.uniten.edu.my/handle/123456789/26840 19 13 7704 All Open Access, Gold, Green MDPI Scopus |
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| description |
accident 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; Safety |
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57361650900 |
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57361650900 Abdulwahid S.N. Mahmoud M.A. Ibrahim N. Zaidan B.B. Ameen H.A. |
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Article |
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Abdulwahid S.N. Mahmoud M.A. Ibrahim N. Zaidan B.B. Ameen H.A. |
| spellingShingle |
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 |
| author_sort |
Abdulwahid S.N. |
| title |
Modeling Motorcyclists� Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents |
| title_short |
Modeling Motorcyclists� Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents |
| title_full |
Modeling Motorcyclists� Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents |
| title_fullStr |
Modeling Motorcyclists� Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents |
| title_full_unstemmed |
Modeling Motorcyclists� Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents |
| title_sort |
modeling motorcyclists� aggressive driving behavior using computational and statistical analysis of real-time driving data to improve road safety and reduce accidents |
| publisher |
MDPI |
| publishDate |
2023 |
| _version_ |
1806428175211692032 |
| score |
13.222552 |