Abstract
Abstract: This work presents a smartphone-based system that uses orientation and acceleration data to monitor and classify road anomalies in real time. Three severity levels—mild, moderate, and severe—are assigned to irregularities by the system, which also handles their identification, classification, and geotagging. Robust approaches are employed to acknowledge and mitigate challenges such as threshold determination, vehicle-specific vibrations, and smartphone orientation normalization. To ensure a thorough dataset, data collecting entails systematic trials for different types of irregularities. Kalman filtering for noise reduction and a consistent 200 Hz sample rate are used in further preprocessing. A variety of indicators from the Y-axis data are included in feature extraction, which produces a complex dataset for model training. The decision tree approach is used to identify road segments based on extracted data because of its interpretability and transparency
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