Driving behavior classification from smartphone sensor data using graph attention network

Darwis, Nur Azshimadathul Asyqin and Morshidi, Malik Arman and Zainal Abidin, Zulkifli and Hasbullah, Muhammad Hariz Danial (2025) Driving behavior classification from smartphone sensor data using graph attention network. In: 2025 10th International Conference on Computer and Communication Engineering (ICCCE), 26-27 August 2025, KOE, IIUM.

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Abstract

Driving behavior analysis is essential for enhancing road safety and enabling intelligent transportation systems. However, traditional models often fail to capture complex dependencies between sensor features, limiting their effectiveness. In this study, we propose a Graph Attention Network (GAT)-based approach to classify driving behavior into predefined categories using smartphone sensor data. GAT leverages attention mechanisms to model relationships between input features, providing a more context-aware representation. We evaluate the model on a public driving behavior dataset and compare it against classical classifiers such as Random Forest, SVM, XGBoost, LSTM and CNN. The proposed GAT model achieved an accuracy of 80% and a macro F1-score of 0.78, showing promising performance surpassing baseline models. These results highlight the potential of graph-based learning in behavior-level classification for telematics applications.

Item Type:	Proceeding Paper (Other)
Uncontrolled Keywords:	Driving Behavior Analysis, Graph Attention Network, Classification, Deep Learning
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User:	Dr Malik Arman Morshidi
Date Deposited:	30 Jul 2026 09:59
Last Update:	30 Jul 2026 09:59
Queue Number:	2026-07-Q4333
URI:	http://irep.iium.edu.my/id/eprint/130291

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