Comparative Analysis of Machine Learning Models for Detecting Abnormal Driving Behaviour in Two-Wheeler Kinematics

Road accidents are a major global concern, particularly affecting vulnerable two-wheeler riders. This research investigates driving behaviour using time-series data from real-world scenarios to de- velop machine learning models for anomaly detection and risk as- sessment. The purpose of this study is to explore the performance of five predictive models—Long Short-Term Memory-Residual (LSTM- R), Gated Recurrent Unit (GRU), Adaboost, XGBoost, and a Mul- tilayer Perceptron (MLP) with Random Forest (RF) ensemble—are evaluated based on nine parameters: accelerometer (X, Y, Z), gyro rotation (X, Y, Z), motion yaw, pitch, and roll. A threshold rule-based approach using multi-output regression is employed for predictive modeling. The study makes three key contributions: First, it assesses the predictive accuracy of the five models, with the MLP-RF ensem- ble achieving a low MAE of 0.1157 on test data. Second, it identi- fies optimal hyperparameters for each model through systematic tuning. Third, it introduces a threshold-based anomaly detection method using residuals, which reduces overfitting and requires fewer instances of abnormal behaviour. The results indicate that these models, especially the MLP-RF ensemble and GRU, effectively predict and detect abnormal driving behaviours, enhancing safety for two-wheeler riders and contributing to sustainable urban devel- opment.