Classification of driver behaviours using machine learning

Classification of driver behaviours using machine learning

<p>According to the Malaysian Institute of Road Safety Research (MIROS), over</p><p>500,000 car accidents occurred in 2016, making cars an unsafe means of</p><p>transportation. This research aimed to collect driver behaviour-relat...

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Main Author: Alaa Zaidan, Ruqayah
Format: thesis
Language:eng
Published: 2021
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Q Science
Online Access:https://ir.upsi.edu.my/detailsg.php?det=7127
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institution Universiti Pendidikan Sultan Idris
collection UPSI Digital Repository
language eng
topic Q Science
spellingShingle Q Science
Alaa Zaidan, Ruqayah
Classification of driver behaviours using machine learning
description <p>According to the Malaysian Institute of Road Safety Research (MIROS), over</p><p>500,000 car accidents occurred in 2016, making cars an unsafe means of</p><p>transportation. This research aimed to collect driver behaviour-related data for</p><p>Malaysian drivers to provide useful insights for Malaysian driving profile and to</p><p>modulate machine learning for classification tasks. Twenty-one drivers (11 male and</p><p>10 female) were studied and compared for their driving style in Lebuhraya Behrang</p><p>Stesen-tg malim (11 km per driver). Drivers were asked to drive naturally while</p><p>considering their safety. Two analysis techniques were utilized (i.e. Statistical and</p><p>Machine Learning-Based). Different conclusions were drawn from each analysis. The</p><p>number of driving events for each driver was calculated (i.e. aggressive, normal and</p><p>safe) and statistical tests (i.e. Mean, Standard Deviation, Correlation analysis, Oneway</p><p>ANOVA and T-test) presented significant differences between each driver from</p><p>the same gender versus their peers from the opposite gender. The statistics were</p><p>presented per driver, his/her group and a comparison with their peers. For a driver to</p><p>be considered as aggressive or normal, a challenge was presented because no</p><p>identification measure existed (i.e. threshold for driving event number to be</p><p>considered aggressive or normal). However, each driving event was identified based</p><p>on literature. Finally, it was determined that classifying drivers was possible through</p><p>their gender but not based on their aggressiveness level. One R Machine learning</p><p>classifier presented good accuracy at 95.24 % in comparison with j48DecisionTree,</p><p>Naive Bayes, One R, and SMO-SVM. The implications of the findings of this study</p><p>suggest male and female drivers tend to drive aggressively. A reason for such</p><p>mortality can be because of the cadence of front-end car accidents, which is a clear</p><p>outcome of aggressive driving behaviour (i.e. speeding, braking, etc.). Identifying</p><p>such behaviour using ML will save lives domestically and internationally</p>
format thesis
qualification_name
qualification_level Master's degree
author Alaa Zaidan, Ruqayah
author_facet Alaa Zaidan, Ruqayah
author_sort Alaa Zaidan, Ruqayah
title Classification of driver behaviours using machine learning
title_short Classification of driver behaviours using machine learning
title_full Classification of driver behaviours using machine learning
title_fullStr Classification of driver behaviours using machine learning
title_full_unstemmed Classification of driver behaviours using machine learning
title_sort classification of driver behaviours using machine learning
granting_institution Universiti Pendidikan Sultan Idris
granting_department Fakulti Seni, Komputeran dan Industri Kreatif
publishDate 2021
url https://ir.upsi.edu.my/detailsg.php?det=7127
_version_ 1747833357897564160
spelling oai:ir.upsi.edu.my:71272022-06-13 Classification of driver behaviours using machine learning 2021 Alaa Zaidan, Ruqayah Q Science <p>According to the Malaysian Institute of Road Safety Research (MIROS), over</p><p>500,000 car accidents occurred in 2016, making cars an unsafe means of</p><p>transportation. This research aimed to collect driver behaviour-related data for</p><p>Malaysian drivers to provide useful insights for Malaysian driving profile and to</p><p>modulate machine learning for classification tasks. Twenty-one drivers (11 male and</p><p>10 female) were studied and compared for their driving style in Lebuhraya Behrang</p><p>Stesen-tg malim (11 km per driver). Drivers were asked to drive naturally while</p><p>considering their safety. Two analysis techniques were utilized (i.e. Statistical and</p><p>Machine Learning-Based). Different conclusions were drawn from each analysis. The</p><p>number of driving events for each driver was calculated (i.e. aggressive, normal and</p><p>safe) and statistical tests (i.e. Mean, Standard Deviation, Correlation analysis, Oneway</p><p>ANOVA and T-test) presented significant differences between each driver from</p><p>the same gender versus their peers from the opposite gender. The statistics were</p><p>presented per driver, his/her group and a comparison with their peers. For a driver to</p><p>be considered as aggressive or normal, a challenge was presented because no</p><p>identification measure existed (i.e. threshold for driving event number to be</p><p>considered aggressive or normal). However, each driving event was identified based</p><p>on literature. Finally, it was determined that classifying drivers was possible through</p><p>their gender but not based on their aggressiveness level. One R Machine learning</p><p>classifier presented good accuracy at 95.24 % in comparison with j48DecisionTree,</p><p>Naive Bayes, One R, and SMO-SVM. The implications of the findings of this study</p><p>suggest male and female drivers tend to drive aggressively. A reason for such</p><p>mortality can be because of the cadence of front-end car accidents, which is a clear</p><p>outcome of aggressive driving behaviour (i.e. speeding, braking, etc.). Identifying</p><p>such behaviour using ML will save lives domestically and internationally</p> 2021 thesis https://ir.upsi.edu.my/detailsg.php?det=7127 https://ir.upsi.edu.my/detailsg.php?det=7127 text eng closedAccess Masters Universiti Pendidikan Sultan Idris Fakulti Seni, Komputeran dan Industri Kreatif <p>A, P., rez, Garc, M. I., Nieto, M., Pedraza, J. L., Rodr, S., . . . Zamorano, J. (2010). Argos: An</p><p>Advanced In-Vehicle Data Recorder on a Massively Sensorized Vehicle for Car Driver</p><p>Behavior Experimentation. 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