Community Sensor Network for Monitoring Road Roughness Using Smartphones

Kumar, Rajiv; Mukherjee, Abhijit; Singh, Vishal

doi:10.1061/(asce)cp.1943-5487.0000624

Cited by 55 publications

(35 citation statements)

References 17 publications

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“…Another researcher [134] applied NARX ANN to estimate IRI from the connected vehicle after investigating vehicle suspension characteristics and its speed in [8]. In [7], the mean-absolute-value of the Z-acc for every 100 m was sensed by a smartphone on a motorbike, and a fuzzy classifier from a server was used for RE.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“…The advantages and disadvantages of these contact and non-contact measurements are discussed in [3][4][5][6]. In recent years, road surface monitoring instruments have transcended from dedicated vehicles with special sensors to dedicated sensors mounted on public transport vehicles, and generalpurpose sensors on privately-owned vehicles, and most recently, smartphone-enabled automated monitoring of road infrastructure [7]. This development is driven by response-based methods to indirectly assess road roughness condition using measurements of displacements, velocities, and accelerations of vehicle components, resulting in cost reduction for labour and equipment as compared with direct contact/non-contact profiling [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Machine Learning Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

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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“…In experiments, the data were collected by Motorola Moto G smartphones fixed in public bus cabins in Italy; road quality indices were computed for 275 km total. Road surface conditions in India were monitored by Kumar et al (2016) using data from smartphones mounted on motorbikes, exemplifying a highly technological impact in a country with an emerging economy and a low level of individual smartphone ownership (Pew Research Center, 2016).…”

Section: Toward the Acquisition And Analysis Of Crowdsourced Infrastrmentioning

confidence: 99%

“…Road surface conditions in India were monitored by Kumar et al. () using data from smartphones mounted on motorbikes, exemplifying a highly technological impact in a country with an emerging economy and a low level of individual smartphone ownership (Pew Research Center, ).…”

Section: Introductionmentioning

confidence: 99%

Scalable Structural Modal Identification Using Dynamic Sensor Network Data with STRIDEX

Matarazzo

Pakzad

2017

Computer aided Civil Eng

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“…In [31], the mean-absolute-value of the vertical acceleration sensed by a smartphone travelling on a motorbike is computed every 100 meters. The data are sent to a server where a fuzzy classifier deduces the road roughness.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Influence of Speed on Road Roughness Sensing: The SmartRoadSense Case

Alessandroni

Carini

Lattanzi

et al. 2017

Sensors

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SmartRoadSense is a crowdsensing project aimed at monitoring the conditions of the road surface. Using the sensors of a smartphone, SmartRoadSense monitors the vertical accelerations inside a vehicle traveling the road and extracts a roughness index conveying information about the road conditions. The roughness index and the smartphone GPS data are periodically sent to a central server where they are processed, associated with the specific road, and aggregated with data measured by other smartphones. This paper studies how the smartphone vertical accelerations and the roughness index are related to the vehicle speed. It is shown that the dependence can be locally approximated with a gamma (power) law. Extensive experimental results using data extracted from SmartRoadSense database confirm the gamma law relationship between the roughness index and the vehicle speed. The gamma law is then used for improving the SmartRoadSense data aggregation accounting for the effect of vehicle speed.

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Community Sensor Network for Monitoring Road Roughness Using Smartphones

Cited by 55 publications

References 17 publications

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

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

Scalable Structural Modal Identification Using Dynamic Sensor Network Data with STRIDEX

A Study on the Influence of Speed on Road Roughness Sensing: The SmartRoadSense Case

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