Embracing crowdsensing: an Enhanced mobile sensing solution for road anomaly detection
Date
Authors
ORCID
https://orcid.org/0000-0001-9767-2855
https://orcid.org/0000-0001-7199-5517
https://orcid.org/0000-0001-7272-0507
https://orcid.org/0000-0002-6762-2475
https://orcid.org/0000-0001-9767-2855
https://orcid.org/0000-0001-7199-5517
https://orcid.org/0000-0001-7272-0507
https://orcid.org/0000-0002-6762-2475
https://orcid.org/0000-0001-9767-2855
https://orcid.org/0000-0001-7199-5517
https://orcid.org/0000-0001-7272-0507https://orcid.org/0000-0002-6762-2475
https://orcid.org/0000-0001-9767-2855
https://orcid.org/0000-0001-7199-5517
https://orcid.org/0000-0001-7272-0507
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MDPI
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.
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.
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Li, X., Huo, D., Goldberg, D.W., Chu, T., Yin, Z. and Hammond, T., 2019. Embracing crowdsensing: An enhanced mobile sensing solution for road anomaly detection. ISPRS International Journal of Geo-Information, 8(9), p.412.