RoadMonitor: An Intelligent Road Surface Condition Monitoring System

Mohamed, Adham; Fouad, Mohamed Mostafa; Elhariri, Esraa; El-Bendary, Nashwa; Zawbaa, Hossam M.; Tahoun, Mohamed; Hassanien, Aboul Ella

doi:10.1007/978-3-319-11310-4_33

Cited by 52 publications

(23 citation statements)

References 9 publications

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“…Moreover, WSNs are appropriate for time-sensitive applications; for instance, the traffic management and vehicular safety. That it could collaborate with another cloud tier to alert drivers about road anomalies through their smartphones in real-time 6,7 .…”

Section: Wireless Sensor Networkmentioning

confidence: 99%

Data Mining and Fusion Techniques for WSNs as a Source of the Big Data

Fouad

Oweis

Gaber

et al. 2015

Procedia Computer Science

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Section: Wireless Sensor Networkmentioning

confidence: 99%

Data Mining and Fusion Techniques for WSNs as a Source of the Big Data

Fouad

Oweis

Gaber

et al. 2015

Procedia Computer Science

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“…Recently, SVM and Dynamic Time Warping algorithm were developed in [115] to identify aggressive driving events, road bumps and potholes for cycling. Another improvement was included in [116] where the gyroscope around gravity rotation was used. SVM and wavelet analysis were also used in RoADS [117] to classify the road anomaly into three event classes: severe, mild and span, and in [118] to detect road anomaly based on driver attitudes toward the speeds and turnings.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Response-based methods to measure road surface irregularity: a state-of-the-art review

Nguyen

Lechner

Wong

2019

Eur. Transp. Res. Rev.

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Purpose: With the development of smart technologies, Internet of Things and inexpensive onboard sensors, many response-based methods to evaluate road surface conditions have emerged in the recent decade. Various techniques and systems have been developed to measure road profiles and detect road anomalies for multiple purposes such as expedient maintenance of pavements and adaptive control of vehicle dynamics to improve ride comfort and ride handling. A holistic review of studies into modern response-based techniques for road pavement applications is found to be lacking. Herein, the focus of this article is threefold: to provide an overview of the stateof-the-art response-based methods, to highlight key differences between methods and thereby to propose key focus areas for future research. Methods: Available articles regarding response-based methods to measure road surface condition were collected mainly from "Scopus" database and partially from "Google Scholar". The search period is limited to the recent 15 years. Among the 130 reviewed documents, 37% are for road profile reconstruction, 39% for pothole detection and the remaining 24% for roughness index estimation. Results: The results show that machine-learning techniques/data-driven methods have been used intensively with promising results but the disadvantages on data dependence have limited its application in some instances as compared to analytical/data processing methods. Recent algorithms to reconstruct/estimate road profiles are based mainly on passive suspension and quarter-vehicle-model, utilise fewer key parameters, being independent on speed variation and less computation for real-time/online applications. On the other hand, algorithms for pothole detection and road roughness index estimation are increasingly focusing on GPS accuracy, data aggregation and crowdsourcing platform for large-scale application. However, a novel and comprehensive system that is comparable to existing International Roughness Index and conventional Pavement Management System is still lacking.

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“…Streetcheck differs from [23] due to the ratings about the road conditions provided by users. A similar solution, RoadMonitor, as proposed by [24], used the gyroscope of smartphones to detect anomalies in the road. Authors cross-validated gyroscope and accelerometer to improve the accuracy of RoadMonitor.…”

Section: Road Surface Conditions Based On Accelerometers Readingsmentioning

confidence: 99%

A participatory sensing framework to classify road surface quality

Nunes

Mota

2019

J Internet Serv Appl

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Participatory sensing networks rely on gathering personal data from mobile devices to infer global knowledge. Participatory sensing has been used for real-time traffic monitoring, where the global traffic conditions are based on information provided by individual devices. However, fewer initiatives address asphalt quality conditions, which is an essential aspect of the route decision process. This article proposes Streetcheck, a framework to classify road surface quality through participatory sensing. Streetcheck gathers mobile devices' sensors such as Global Positioning System (GPS) and accelerometer, as well as users' ratings on road surface quality. A classification system aggregates the data, filters them, and extracts a set of features as input for supervised learning algorithms. Twenty volunteers carried out tests using Streetcheck on 1,200 km of urban roads of Minas Gerais (Brazil). Streetcheck reached up to 90.64% of accuracy on classifying road surface quality.

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RoadMonitor: An Intelligent Road Surface Condition Monitoring System

Cited by 52 publications

References 9 publications

Data Mining and Fusion Techniques for WSNs as a Source of the Big Data

Data Mining and Fusion Techniques for WSNs as a Source of the Big Data

Response-based methods to measure road surface irregularity: a state-of-the-art review

A participatory sensing framework to classify road surface quality

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