Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/50851

TitleAnomaly detection in roads with a data mining approach
Author(s)Silva, Nuno Alberto Ribeiro
Soares, João Paulo Conceição
Shah, Vaibhav
Santos, Maribel Yasmina
Rodrigues, Helena
KeywordsRoad anomalies
Data mining
Data analytics
Data Analitics
Issue date2017
PublisherElsevier
JournalProcedia Computer Science
CitationNuno Silva, João Soares, Vaibhav Shah, Maribel Yasmina Santos, Helena Rodrigues, Anomaly Detection in Roads with a Data Mining Approach, Procedia Computer Science, Volume 121, 2017, Pages 415-422, ISSN 1877-0509
Abstract(s)Road condition has an important role in our daily live. Anomalies in road surface can cause accidents, mechanical failure, stress and discomfort in drivers and passengers. Governments spend millions each year in roads maintenance for maintaining roads in good condition. But extensive maintenance work can lead to traffic jams, causing frustration in road users. In way to avoid problems caused by road anomalies, we propose a system that can detect road anomalies using smartphone sensors. The approach is based in data-mining algorithms to mitigate the problem of hardware diversity. In this work we used scikit-learn, a python module, and Weka, as tools for data-mining. All cleaning data process was made using python language. The final results show that it is possible detect road anomalies using only a smartphone.
TypeconferencePaper
URIhttp://hdl.handle.net/1822/50851
DOI10.1016/j.procs.2017.11.056
ISSN1877-0509
Publisher versionhttps://www.sciencedirect.com/science/article/pii/S1877050917322494
Peer-Reviewedyes
AccessopenAccess
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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