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Type :thesis
Subject :Q Science
Main Author :Alaa Zaidan, Ruqayah
Title :Classification of driver behaviours using machine learning
Place of Production :Tanjong Malim
Publisher :Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :2021
Corporate Name :Universiti Pendidikan Sultan Idris
PDF Guest :Click to view PDF file

Abstract : Universiti Pendidikan Sultan Idris
According to the Malaysian Institute of Road Safety Research (MIROS), over 500,000 car accidents occurred in 2016, making cars an unsafe means of transportation. This research aimed to collect driver behaviour-related data for Malaysian drivers to provide useful insights for Malaysian driving profile and to modulate machine learning for classification tasks. Twenty-one drivers (11 male and 10 female) were studied and compared for their driving style in Lebuhraya Behrang Stesen-tg malim (11 km per driver). Drivers were asked to drive naturally while considering their safety. Two analysis techniques were utilized (i.e. Statistical and Machine Learning-Based). Different conclusions were drawn from each analysis. The number of driving events for each driver was calculated (i.e. aggressive, normal and safe) and statistical tests (i.e. Mean, Standard Deviation, Correlation analysis, Oneway ANOVA and T-test) presented significant differences between each driver from the same gender versus their peers from the opposite gender. The statistics were presented per driver, his/her group and a comparison with their peers. For a driver to be considered as aggressive or normal, a challenge was presented because no identification measure existed (i.e. threshold for driving event number to be considered aggressive or normal). However, each driving event was identified based on literature. Finally, it was determined that classifying drivers was possible through their gender but not based on their aggressiveness level. One R Machine learning classifier presented good accuracy at 95.24 % in comparison with j48DecisionTree, Naive Bayes, One R, and SMO-SVM. The implications of the findings of this study suggest male and female drivers tend to drive aggressively. A reason for such mortality can be because of the cadence of front-end car accidents, which is a clear outcome of aggressive driving behaviour (i.e. speeding, braking, etc.). Identifying such behaviour using ML will save lives domestically and internationally

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