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An Automatic Method for Detection and Update of Additive Changes in Road Network with GPS Trajectory Data
Open AccessArticle

Embracing Crowdsensing: An Enhanced Mobile Sensing Solution for Road Anomaly Detection

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Department of Geography, Texas A&M University, 3147 TAMU, College Station, TX 77843-3147, USA
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Department of Computer Science & Engineering, Texas A&M University, 3112 TAMU, College Station, TX 77843-3112, USA
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Department of Computing Sciences, Texas A&M University-Corpus Christi, 6300 Ocean Drive, Corpus Christi, TX 78412-5799, USA
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Conrad Blucher Institute for Surveying and Science, Texas A&M University-Corpus Christi, 6300 Ocean Drive, Corpus Christi, TX 74812-5799, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(9), 412; https://doi.org/10.3390/ijgi8090412
Received: 18 August 2019 / Revised: 6 September 2019 / Accepted: 8 September 2019 / Published: 13 September 2019
(This article belongs to the Special Issue Crowdsourced Geographic Information in Citizen Science)
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. View Full-Text
Keywords: Mobile Crowdsensing; Road Anomaly Detection; Continuous Wavelet Transform; Spatial Clustering; Smartphone Sensors Mobile Crowdsensing; Road Anomaly Detection; Continuous Wavelet Transform; Spatial Clustering; Smartphone Sensors
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Li, X.; Huo, D.; Goldberg, D.W.; Chu, T.; Yin, Z.; Hammond, T. Embracing Crowdsensing: An Enhanced Mobile Sensing Solution for Road Anomaly Detection. ISPRS Int. J. Geo-Inf. 2019, 8, 412.

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