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Driver behaviour classification: a research using OBD-II data and machine learning

Classification of driver behaviour has gained much attention due to its potential in a variety of applications, and On-Board Diagnostic (OBD) real-time data is often underutilised. Hence, using On-board Diagnostic-II (OBD-II) data by categorising drivers based on their driving behaviour can be an e...

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Main Authors: Muhamad Fadzil, Nur Farisya Aqilah, Mohd Fadzir, Hilda, Mansor, Hafizah, Rahardja, Untung
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2024
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/114917/7/114917_Driver%20behaviour%20classification.pdf
http://irep.iium.edu.my/114917/
https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/5714/6415
https://doi.org/10.37934/araset.56.2.5161
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spelling my.iium.irep.1149172024-12-04T01:10:38Z http://irep.iium.edu.my/114917/ Driver behaviour classification: a research using OBD-II data and machine learning Muhamad Fadzil, Nur Farisya Aqilah Mohd Fadzir, Hilda Mansor, Hafizah Rahardja, Untung T Technology (General) Classification of driver behaviour has gained much attention due to its potential in a variety of applications, and On-Board Diagnostic (OBD) real-time data is often underutilised. Hence, using On-board Diagnostic-II (OBD-II) data by categorising drivers based on their driving behaviour can be an efficient method. The objective of this study is to identify groups of drivers based on their driving styles using the collected OBD-II data. This study uses a Kaggle-obtained online dataset of OBD-II. The suggested model in this study analyses driving behaviour using both supervised and unsupervised methods. The relationship between all features and engine speed is analysed to select the optimal features, which include engine speed, vehicle speed, throttle position, and calculated engine load. Then, the proposed model makes use of the K-Means algorithm to create driving behaviour labels whether belong to safe or aggressive - validated by the safety score criteria. Different machine learning models including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), AdaBoost (AB), Linear Combination (LC) and Weighted Linear Combination (WLC) are used, customised, and compared to get the most accurate prediction of driver behaviour. Experimental results indicate that the suggested driving behaviour analysis can reach an average rate of 98.72% accuracy using DT. However, implementing the ensemble method AB has improved the accuracy to 99.48%. Semarak Ilmu Publishing 2024-10-08 Article PeerReviewed application/pdf en http://irep.iium.edu.my/114917/7/114917_Driver%20behaviour%20classification.pdf Muhamad Fadzil, Nur Farisya Aqilah and Mohd Fadzir, Hilda and Mansor, Hafizah and Rahardja, Untung (2024) Driver behaviour classification: a research using OBD-II data and machine learning. Journal of Advanced Research in Applied Sciences and Engineering Technology, 56 (2). pp. 51-61. E-ISSN 2462-1943 https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/5714/6415 https://doi.org/10.37934/araset.56.2.5161
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Muhamad Fadzil, Nur Farisya Aqilah
Mohd Fadzir, Hilda
Mansor, Hafizah
Rahardja, Untung
Driver behaviour classification: a research using OBD-II data and machine learning
description Classification of driver behaviour has gained much attention due to its potential in a variety of applications, and On-Board Diagnostic (OBD) real-time data is often underutilised. Hence, using On-board Diagnostic-II (OBD-II) data by categorising drivers based on their driving behaviour can be an efficient method. The objective of this study is to identify groups of drivers based on their driving styles using the collected OBD-II data. This study uses a Kaggle-obtained online dataset of OBD-II. The suggested model in this study analyses driving behaviour using both supervised and unsupervised methods. The relationship between all features and engine speed is analysed to select the optimal features, which include engine speed, vehicle speed, throttle position, and calculated engine load. Then, the proposed model makes use of the K-Means algorithm to create driving behaviour labels whether belong to safe or aggressive - validated by the safety score criteria. Different machine learning models including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), AdaBoost (AB), Linear Combination (LC) and Weighted Linear Combination (WLC) are used, customised, and compared to get the most accurate prediction of driver behaviour. Experimental results indicate that the suggested driving behaviour analysis can reach an average rate of 98.72% accuracy using DT. However, implementing the ensemble method AB has improved the accuracy to 99.48%.
format Article
author Muhamad Fadzil, Nur Farisya Aqilah
Mohd Fadzir, Hilda
Mansor, Hafizah
Rahardja, Untung
author_facet Muhamad Fadzil, Nur Farisya Aqilah
Mohd Fadzir, Hilda
Mansor, Hafizah
Rahardja, Untung
author_sort Muhamad Fadzil, Nur Farisya Aqilah
title Driver behaviour classification: a research using OBD-II data and machine learning
title_short Driver behaviour classification: a research using OBD-II data and machine learning
title_full Driver behaviour classification: a research using OBD-II data and machine learning
title_fullStr Driver behaviour classification: a research using OBD-II data and machine learning
title_full_unstemmed Driver behaviour classification: a research using OBD-II data and machine learning
title_sort driver behaviour classification: a research using obd-ii data and machine learning
publisher Semarak Ilmu Publishing
publishDate 2024
url http://irep.iium.edu.my/114917/7/114917_Driver%20behaviour%20classification.pdf
http://irep.iium.edu.my/114917/
https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/5714/6415
https://doi.org/10.37934/araset.56.2.5161
_version_ 1817841070404272128
score 13.223943

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