UPSI Digital Repository (UDRep)
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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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