Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data

Road event detection is critical for tasks such as monitoring, anomaly detection, and optimization. Traditional approaches often require complex feature engineering or the use of machine learning models, which can be computationally intensive, especially when dealing with real-time data from high-frequency vibration and acceleration sensors. In this work, we propose a Random Forest-based event classification algorithm designed to handle the unique patterns of vibration and acceleration data in road event detection for an urban traffic scenario. Our method utilizes vibration and acceleration data in three axes (x, y, z) to classify events in a robust and scalable manner. The Random Forest model is trained to identify patterns in the sensor data and assign them to predefined event categories, providing an efficient and accurate classification mechanism. Experimental results prove the effectiveness of our approach: it reaches an accuracy of 91.99%, with a precision of 80% and a recall of 75%, demonstrating reliable event classification. Additionally, the Area Under the Curve (AUC) score of 0.9468 confirms the model’s strong discriminative capability. Further, compared to a rule-based approach, our method offers greater generalization and adaptability, reducing the need for manual parameter tuning. While the rule-based approach attains a higher precision of 92%, it requires frequent adjustments for each dataset and lacks robustness across different road conditions.