RoadSense: A Framework for Road Condition Monitoring using Sensors and Machine Learning

The escalation of road accidents and associated fatalities presents a pressing challenge, particularly in developing nations like Bangladesh. This study addresses the critical need for enhanced road safety by introducing a sophisticated “RoadSense” device that integrates sensor technology and machine learning to predict road conditions in real time. The system employs an MPU-6050 IMU module along with GPS, temperature and humidity sensors to collect data in real-time during vehicle operation. The data collection of the device is complemented by training of tree-based ensemble supervised models which are refined with the SMOTE-ENN technique for class imbalance of data. Post-hyperparameter tuning, the Extra Trees Classifier achieved an accuracy of 99.07%, with a recall of 97.17% and precision of 98.66% for pothole detection, and similarly high metrics for detecting speed breakers and hard braking events. In real-world testing, the model maintained a high F1 score of 97.57% across diverse road conditions. Unlike existing solutions, the RoadSense device uniquely combines low-cost components, multi-sensor hardware development tailored for developing countries with advanced algorithm-level data processing techniques. This integration delivers a scalable, real-time road monitoring solution that supports data-driven infrastructure maintenance and policy decisions in. A key distinguishing feature of this work is that the entire framework is specifically designed to be both implementable and scalable within the context of developing countries such as Bangladesh.