Intelligent Pothole Detection and Road Condition Assessment

TL;DR

This paper presents a real-time mobile system using sensor data and machine learning to detect potholes and assess road conditions, enabling scalable reporting and city maintenance efforts.

Contribution

The authors developed a mobile app with SVM models achieving over 92% accuracy for pothole detection and road condition classification, facilitating crowdsourced infrastructure monitoring.

Findings

SVM models achieved 93% accuracy in classifying road conditions.

The system enables real-time pothole detection using smartphone sensors.

Data-driven maps help cities identify and prioritize road repairs.

Abstract

Poor road conditions are a public nuisance, causing passenger discomfort, damage to vehicles, and accidents. In the U.S., road-related conditions are a factor in 22,000 of the 42,000 traffic fatalities each year. Although we often complain about bad roads, we have no way to detect or report them at scale. To address this issue, we developed a system to detect potholes and assess road conditions in real-time. Our solution is a mobile application that captures data on a car's movement from gyroscope and accelerometer sensors in the phone. To assess roads using this sensor data, we trained SVM models to classify road conditions with 93% accuracy and potholes with 92% accuracy, beating the base rate for both problems. As the user drives, the models use the sensor data to classify whether the road is good or bad, and whether it contains potholes. Then, the classification results are used to create data-rich maps that illustrate road conditions across the city. Our system will empower civic officials to identify and repair damaged roads which inconvenience passengers and cause accidents. This paper details our data science process for collecting training data on real roads, transforming noisy sensor data into useful signals, training and evaluating machine learning models, and deploying those models to production through a real-time classification app. It also highlights how cities can use our system to crowdsource data and deliver road repair resources to areas in need.