Monitoring of road surface conditions is a critical activity in transport infrastructure management. Many research solutions have been proposed in order to automatically control and check the quality of road surfaces. Most of them make use of expensive sensors embedded in vehicles or mainly focus on detection of specific anomalies during monitoring activity.In this paper, we describe the design of a system for collaborative monitoring of road surface quality. The overall architecture encompasses the integration of a custom mobile application, a georeferenced database system and a visualization front-end. Road surface condition is summarized through a roughness parameter computed using signal processing algorithms running on mobile devices. The roughness values computed are subsequently transmitted and stored into a back-end geographic information system enabling processing of aggregated traces and visualization of road conditions. The proposed approach introduces a thoroughly integrated system suitable for monitoring applications in a scalable, crowdsourcing collaborative setting.

SmartRoadSense: Collaborative Road Surface Condition Monitoring

KLOPFENSTEIN, CUNO LORENZ;DELPRIORI, SAVERIO;SERAGHITI, ANDREA;LATTANZI, EMANUELE;FRESCHI, VALERIO;CARINI, ALBERTO;BOGLIOLO, ALESSANDRO
2014

Abstract

Monitoring of road surface conditions is a critical activity in transport infrastructure management. Many research solutions have been proposed in order to automatically control and check the quality of road surfaces. Most of them make use of expensive sensors embedded in vehicles or mainly focus on detection of specific anomalies during monitoring activity.In this paper, we describe the design of a system for collaborative monitoring of road surface quality. The overall architecture encompasses the integration of a custom mobile application, a georeferenced database system and a visualization front-end. Road surface condition is summarized through a roughness parameter computed using signal processing algorithms running on mobile devices. The roughness values computed are subsequently transmitted and stored into a back-end geographic information system enabling processing of aggregated traces and visualization of road conditions. The proposed approach introduces a thoroughly integrated system suitable for monitoring applications in a scalable, crowdsourcing collaborative setting.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11576/2602267
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