Learning Roadway Surface Disruption Patterns Using the Bag of Words Representation

González, Luis; Moreno, Ricardo; Escalante, Hugo Jair; Martínez, Fernando; Carlos, Manuel Ricardo

doi:10.1109/tits.2017.2662483

Cited by 45 publications

(21 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The estimate of the roughness index (IRI) requires a fixed accelerometer in the car cabin, calibration to take into account the tires and the suspension system of the car, and skilled labor. Accelerometer sensors and Global Positioning System (GPS) have been widely employed for detecting surface conditions [10,11,[13][14][15][16][17]. In common, these proposals attempt to detect single anomalies such as potholes, bumps, or other road surface anomalies.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Our previous works on road artefact detection with commonly used devices such as smartphones, as well as works from several researchers [4], from different countries [5] in the last ten years [6], concentrated on detection of single road artefacts during a one-time drive over the road fragment. Different proposals have been introduced to detect potholes based on the accelerometers mounted in cars, as well as using smartphones of the road users, including: basic thresholding [7], neural networks of different kinds and machine learning [8], Bag of Words [9], as well as digital fingerprinting [10].…”

Section: Assessment Of the Road Quality Using Accelerometersmentioning

confidence: 99%

Real-Time Road Quality Assessment Using Smartphones and Cloud Lambda Architecture

Badurowicz

Cieplak

2019

MATEC Web Conf.

View full text Add to dashboard Cite

In this paper, the authors are proposing a computer system built in the cloud-computing fashion for the collection of data from smartphones to achieve a crowdsensing-based quality assessment of roads, as well as detection, identification, and assessment of road artefacts (potholes, speed bumps, etc). The proposed information processing methods were based on Lambda architecture and the integration of different types of crowdsourced data to finally calculate the singular value of the human-readable road quality. Finally, the prototype implementation of the system for gathering, processing, integration, and processing the road quality data is also presented.

show abstract

“…The research work (Gonzalez, Moreno, Escalante, Martinez, & Carlos, 2017) propose an algorithm for automatically detecting the following events: bumpers, asphalt stops, metal stops, irregular road and regular road. Their algorithm uses the BoW technique (Bag of Words) for extracting short segments of the signal coming from the accelerometer.…”

Section: Related Workmentioning

confidence: 99%

Generic Module for Collecting Data in Smart Cities

Martínez

Ramírez

Estrada³

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:The Future Internet brings new technologies to the common life of people, such as Internet of Things, Cloud Computing or Big Data. All this technologies have change the way people communicate and also the way the devices interact with the context, giving rise to new paradigms, as the case of smart cities. Currently, the mobile devices represent one of main sources of information for new applications that take into account the user context, such as apps for mobility, health, of security. Several platforms have been proposed that consider the development of Future Internet applications, however, no generic modules can be found that implement the collection of context data from smartphones. In this research work we present a generic module to collect data from different sensors of the mobile devices and also to send, in a standard manner, this data to the Open FIWARE Cloud to be stored or analyzed by software tools. The proposed module enables the human-as-a-sensor approach for FIWARE Platform.

show abstract

Learning Roadway Surface Disruption Patterns Using the Bag of Words Representation

Cited by 45 publications

References 16 publications

A participatory sensing framework to classify road surface quality

A participatory sensing framework to classify road surface quality

Real-Time Road Quality Assessment Using Smartphones and Cloud Lambda Architecture

Generic Module for Collecting Data in Smart Cities

Contact Info

Product

Resources

About