# Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data
## 1 Introduction  
Detection of road events in urban traffic is essential for tasks such as safety monitoring, anomaly detection, and operational optimization [1, 2, 3, 4, 5]. Traditional classification methods often rely on direct feature extraction or complex models, which can be computationally intensive due to the need for high-resolution processing of large volumes of high-frequency vibration and acceleration data. This includes tasks such as filtering, transforming, and extracting features from multivariate time series data, which requires significant memory and computational power. In scenarios where input is restricted to specific vibration and acceleration patterns, achieving accurate and efficient classifications becomes a significant challenge [5, 6].  
Recent advances in machine learning, particularly Random Forest, have shown promise in improving classification accuracy without the need for extensive labeled datasets. Unlike deep learning models, which often require large amounts of training data, Random Forest can efficiently classify events using fewer labeled examples and can handle high-dimensional data with relative ease [7, 8]. However, in urban traffic applications where data are dynamic and diverse, traditional Random Forest implementations may struggle toaccount for all possible event scenarios due to their dependence on static feature sets and the difficulty of capturing evolving patterns in vibration and acceleration data [9].  
To address these challenges, we propose a novel Random Forest-based event classification algorithm that leverages vibration and acceleration data from three axes (x, y, z). Our method includes dynamic feature extraction tailored to road events, incorporating both statistical and frequency-domain features, which enhance the model's ability to distinguish between diverse event types. By preprocessing the data with normalization and filtering, we reduce noise and ensure consistency, while the inclusion of windowed feature extraction captures temporal dependencies often missed by static implementations. This approach allows the model to adapt to varying urban traffic conditions, providing a robust classification of events such as vehicle movements, sudden stops, or irregularities in road conditions. Additionally, the model's computational efficiency and simplicity make it suitable for real-time applications, directly addressing the limitations of traditional Random Forest methods in dynamic and diverse environments.  
Our primary contribution lies in the effective integration of Random Forest for event classification, enabling robust, real-time detection with minimal computational overhead. Unlike traditional methods that struggle with high-dimensional sensor data, our approach efficiently processes vibration and acceleration signals without requiring large labeled datasets or complex preprocessing steps. Additionally, we demonstrate that Random Forest, despite being a simpler model compared to deep learning, offers high accuracy by handling data from multiple axes simultaneously. The experimental results validate the effectiveness of our method, showing that it outperforms other common classification techniques in terms of both classification accuracy and computational efficiency, offering a promising solution for road event detection in urban traffic and real-time event detection.