A Machine Learning Approach to Driver Behavior Classification Using Telematics Data

Abstract Recent advancements in vehicle telematics and machine learning have enabled data-driven analysis of driver behavior, with the goal of improving road safety and transportation efficiency. This research paper presents a machine learning approach for classifying driver behaviour (such as normal, aggressive, or drowsy driving) using telematics sensor data collected from vehicles. This proposes a system architecture that integrates both traditional machine learning algorithms and deep learning models to detect unsafe driving patterns in real time. Four classification techniques namely Random Forest, Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) are implemented and evaluated on real telematics datasets. The highlight the motivation and challenges in modelling driver behavior, review the relevant literature, and discuss the design of our proposed system including data preprocessing and feature extraction. The performance of the models is assessed using standard evaluation metrics (accuracy, precision, recall, F1-score), and potential applications of driver behavior classification are outlined (such as usage based insurance, fleet management, and advanced driver assistance systems). This also examines the technical challenges and limitations of current approaches including data quality, model interpretability, generalization across drivers, and class imbalance and suggest directions for future work to enhance the robustness and reliability of driver behavior classification systems. Keywords- Driver Behavior Classification, Telematics, Machine Learning, Deep Learning, Random Forest, SVM, CNN, LSTM, Intelligent Transportation Systems.