Smart patrolling: An efficient road surface monitoring using smartphone sensors and crowdsourcing

Singh, Gurdit; Bansal, Divya; Sofat, Sanjeev; Aggarwal, Naveen

doi:10.1016/j.pmcj.2017.06.002

Cited by 127 publications

(65 citation statements)

References 6 publications

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“…With regards to vibration-based methods for the assessment of roadway pavements, relevant work can be found in the works, among other, by Chen et al (Chen, Tan et al 2016), Jang et al (Jang, Yang et al 2016), Sigh et al (Singh, Bansal et al 2017) and Allouch et al (Allouch, Koubâa et al 2017).…”

Section: Vibration-based Methodsmentioning

confidence: 99%

Do vehicles sense pavement surface anomalies?

Kyriakou¹,

Christodoulou²,

Dimitriou³

2019

Proceedings of the 2019 European Conference on Computing in Construction

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Nowadays, pavement monitoring agencies typically assess pavement quality approximately only once per year. The main reason for this low frequency of inspections is the fact that current methods are expensive and laborious. The paper presents a data-driven framework and related field studies on the use of pattern recognition techniques and smartphone sensor technologies for the detection, classification and georeferencing of roadway pavement surface anomalies. The proposed system provides continuous and reliable information about the five most common roadway pavement surface anomalies which are valuable for pavement management systems and public safety.

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Section: Vibration-based Methodsmentioning

confidence: 99%

Do vehicles sense pavement surface anomalies?

Kyriakou¹,

Christodoulou²,

Dimitriou³

2019

Proceedings of the 2019 European Conference on Computing in Construction

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“…Frequency filter, speed filter and small peaks filter were used to develop the vertical acceleration impulse that corresponds to a "high-energy event" on the road surface in UNIquALroad [78,79]. Dynamic Time Warping -DTW detects pothole by using the pattern-matching technique independent of time and speed [80]. Similar to DTW, the Smartphone Probe Car system was developed using a new road anomaly indexing heuristic which is adaptive to vehicle dynamics [81].…”

Section: Signal Processingmentioning

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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“…Smart patrolling is another proposal that uses smartphones to gather accelerometer and GPS data from several users [21]. However, smart patrolling focused on increasing the accuracy for detecting potholes and bumps using Dynamic Time Warping (DTW) technique.…”

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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Smart patrolling: An efficient road surface monitoring using smartphone sensors and crowdsourcing

Cited by 127 publications

References 6 publications

Do vehicles sense pavement surface anomalies?

Do vehicles sense pavement surface anomalies?

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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