A participatory sensing framework to classify road surface quality

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.

[1]  Gurdit Singh,et al.  Smart patrolling: An efficient road surface monitoring using smartphone sensors and crowdsourcing , 2017, Pervasive Mob. Comput..

[2]  Igor Muzetti Pereira,et al.  Using Crowdsourcing Techniques and Mobile Devices for Asphaltic Pavement Quality Recognition , 2016, 2016 VI Brazilian Symposium on Computing Systems Engineering (SBESC).

[3]  James V. Krogmeier,et al.  A Recursive Multiscale Correlation-Averaging Algorithm for an Automated Distributed Road-Condition-Monitoring System , 2011, IEEE Transactions on Intelligent Transportation Systems.

[4]  Jie Wu,et al.  CRSM: Crowdsourcing Based Road Surface Monitoring , 2013, 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing.

[5]  Chih-Wei Yi,et al.  Toward Crowdsourcing-Based Road Pavement Monitoring by Mobile Sensing Technologies , 2015, IEEE Transactions on Intelligent Transportation Systems.

[6]  Hugo Jair Escalante,et al.  Learning Roadway Surface Disruption Patterns Using the Bag of Words Representation , 2017, IEEE Transactions on Intelligent Transportation Systems.

[7]  Vinicius F. S. Mota,et al.  Users in the urban sensing process , 2016 .

[8]  Huan Liu,et al.  Feature Selection and Classification - A Probabilistic Wrapper Approach , 1996, IEA/AIE.

[9]  Hu Ng,et al.  Identification of Road Surface Conditions using IoT Sensors and Machine Learning , 2019 .

[10]  Marco Conti,et al.  The structure of online social networks mirrors those in the offline world , 2015, Soc. Networks.

[11]  Thiago H. Silva,et al.  Towards scalable mobile crowdsensing through device-to-device communication , 2018, J. Netw. Comput. Appl..

[12]  Hiroyuki Oneyama,et al.  A study on the use of smartphones under realistic settings to estimate road roughness condition , 2014, EURASIP J. Wirel. Commun. Netw..

[13]  Aboul Ella Hassanien,et al.  RoadMonitor: An Intelligent Road Surface Condition Monitoring System , 2014, IEEE Conf. on Intelligent Systems.

[14]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[15]  P. Rousseeuw Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[16]  Abhijit Mukherjee,et al.  Community Sensor Network for Monitoring Road Roughness Using Smartphones , 2017, J. Comput. Civ. Eng..

[17]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[18]  Ethem Alpaydin Introduction to machine learning, 2rd ed , 2014 .

[19]  Eugene J. O'Brien,et al.  The use of vehicle acceleration measurements to estimate road roughness , 2008 .

[20]  Ryan Newton,et al.  The pothole patrol: using a mobile sensor network for road surface monitoring , 2008, MobiSys '08.

[21]  Alberto Carini,et al.  A Study on the Influence of Speed on Road Roughness Sensing: The SmartRoadSense Case † , 2017, Sensors.

[22]  Andrew McCallum,et al.  Efficient clustering of high-dimensional data sets with application to reference matching , 2000, KDD '00.

[23]  Larry A. Rendell,et al.  The Feature Selection Problem: Traditional Methods and a New Algorithm , 1992, AAAI.

[24]  Juan Torres Arjona,et al.  A simplified computer vision system for road surface inspection and maintenance , 2016 .

[25]  Atsuhiro Takasu,et al.  Estimating Road Surface Condition Using Crowdsourcing , 2016, ISIP.

[26]  Marco Antonio Farah Caldas,et al.  A Model for the Evaluation of Brazilian Road Transport: A Sustainable Perspective , 2018 .

[27]  Girts Strazdins,et al.  Real time pothole detection using Android smartphones with accelerometers , 2011, 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS).

[28]  Hiroshi Motoda,et al.  Computational Methods of Feature Selection , 2022 .

[29]  Vittorio Astarita,et al.  A Mobile Application for Road Surface Quality Control: UNIquALroad , 2012 .

[30]  Raouf Boutaba,et al.  A comprehensive survey on machine learning for networking: evolution, applications and research opportunities , 2018, Journal of Internet Services and Applications.