Abstract
Road anomaly detection is essential in road maintenance and management; however, continuously monitoring road anomalies (such as bumps and potholes) with a low-cost and high-efficiency solution remains a challenging research question. In this study, we put forward an enhanced mobile sensing solution to detect road anomalies using mobile sensed data. We first create a smartphone app to detect irregular vehicle vibrations that usually imply road anomalies. Then, the mobile sensed signals are analyzed through continuous wavelet transform to identify road anomalies and estimate their sizes. Next, we innovatively utilize a spatial clustering method to group multiple driving tests’ results into clusters based on their spatial density patterns. Finally, the optimized detection results are obtained by synthesizing each cluster’s member points. Results demonstrate that our proposed solution can accurately detect road surface anomalies (94.44%) with a high positioning accuracy (within 3.29 meters in average) and an acceptable size estimation error (with a mean error of 14 cm). This study suggests that implementing a crowdsensing solution could substantially improve the effectiveness of traditional road monitoring systems.
Highlights
“No one knows how many potholes are out there, but we all agree there are a ton of them.” The U.S Federal Highway Administration (FHWA) estimates that about 52% of the U.S highways are in a miserable condition [1]
Traditional road anomaly detections were conducted through three main types of approaches, including 3D laser scanning, vision-based image processing, and vehicular vibration-based analysis [5]
It is worth noting that road surface conditions can vary day by day
Summary
“No one knows how many potholes are out there, but we all agree there are a ton of them.” The U.S Federal Highway Administration (FHWA) estimates that about 52% of the U.S highways are in a miserable condition [1]. Mobile crowd sensed data sources are transforming our life They have been proven to be extremely efficient and have been successfully deployed to solve real-world issues, such as noise monitoring, traffic density estimation, route planning, among others [12,13]. Studies have proven that smartphone accelerometers can effectively capture the vehicle vibrations caused by the unevenness of the road surface [14,15,16]. Through analyzing these mobile sensors’ signals, we can potentially identify road anomalies
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