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Evaluation and collaborative monitoring of streets and roads conditions by means of smartphone sensors

Grant number: 16/07767-3
Support Opportunities:Research Grants - Innovative Research in Small Business - PIPE
Duration: March 01, 2017 - November 30, 2017
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computer Systems
Principal Investigator:Vinícius Mourão Alves de Souza
Grantee:Vinícius Mourão Alves de Souza
Host Company:Onion Tecnologia Ltda. - ME
City: São Carlos
Pesquisadores principais:
Rafael Alves de Souza ; Rafael Geraldeli Rossi
Associated researchers: Aline Colares Do Vale ; André Gustavo Maletzke ; Diego Furtado Silva
Associated scholarship(s):17/04652-3 - Evaluation and collaborative monitoring of streets and roads conditions by means of smartphone sensors, BP.PIPE


In Brazil and abroad, the road system has a key role in the transport of passengers and cargo. In the cargo transportation sector, the road system is responsible for 61% of national movements and in relation to passenger transport, it dominates with 95% of share. Although fundamental to the society and economic growth, it is observed that the infrastructure of the most of the streets and roads in Brazil is inadequate, and the quality of the pavement it's a major problem. The low quality of the pavement increases the time of travels, raises the costs of vehicles maintenance, brings more risk to users, raises pollutant emissions and have a direct impact on the price composition of goods. For better planning of maintenance and appropriate interventions on the roads, it is essential the use of assessment tools that allow constant monitoring of their conditions. In order to reduce the manual effort or the use of expensive equipment such as inertial profilometer, this research project proposes to develop a mobile application that makes use of smartphones sensors, such as accelerometers and GPS, in conjunction with machine learning algorithms and signal analysis for the evaluation and monitoring of the conditions of streets and roads. Thus, it is possible that different users contribute to the constant, automatic and ubiquitous monitoring given the use of navigation applications on smartphones fixed in their vehicles while driving. The product to be developed in this project has significant advantages over the current tools. The current tools have a high cost and the dependence of an expert, they also do not allow a comprehensive evaluation in a broad area and the constant monitoring of an environment with frequent changes. With the information of this application, road transport companies or conventional users can plan routes that consider the quality of the roads/streets, aiming to reduce the travel time, fuel saving and increased security. In addition to the mobile application, we also want to develop a system responsible for generating reports about the quality of roads and streets over time. Such reports can be strategic and assist in the decision-making process of companies and public agencies. For example, municipalities and companies responsible for the maintenance of roads can plan interventions more adequately and estimate the costs of these interventions. Thus, it is expected that the product to be developed in this project generates social and economic impacts on different segments of society. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
SOUZA, VINICIUS M. A.. Asphalt pavement classification using smartphone accelerometer and Complexity Invariant Distance. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, v. 74, p. 198-211, . (16/07767-3)
SOUZA, VINICIUS M. A.; CHERMAN, EVERTON A.; ROSSI, RAFAEL G.; SOUZA, RAFAEL A.; ADAMS, N; TUCKER, A; WESTON, D. Towards Automatic Evaluation of Asphalt Irregularity Using Smartphone's Sensors. ADVANCES IN INTELLIGENT DATA ANALYSIS XVI, IDA 2017, v. 10584, p. 12-pg., . (16/07767-3)
SOUZA, VINICIUS M. A.; GIUSTI, RAFAEL; BATISTA, ANTONIO J. L.. Asfault: A low-cost system to evaluate pavement conditions in real-time using smartphones and machine learning. PERVASIVE AND MOBILE COMPUTING, v. 51, p. 121-137, . (16/07767-3)

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