Detection of Frequent Anomalies in Urban Road Transport

• Ana Beatriz Cruz Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ)
• João Ferreira Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ)
• Diego Carvalho Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ)
• Eduardo Mendes Fundação Getúlio Vargas (FGV)
• Esther Pacitti Inria / University of Montpellier
• Rafaelli Coutinho Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ)
• Fabio Porto National Laboratory for Scientific Computing (LNCC)
• Eduardo Ogasawara Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ)

DOI: https://doi.org/10.5753/sbbd.2018.22242

Abstract

The growth of urban population and, consequently, the number of vehicles causes the increase of traffic jams and emission of polluting gases. In this context, we observe the intensification of papers that aim to identify bottlenecks and their causes. These papers propose methodologies that use trajectory data model and aim to explain systemic behaviors. This article proposes the identification and classification of anomalies in the urban road transport system from space-time aggregations to permanent objects. The methodology consists of pre-processing of data, identification of anomalies, identification, and classification of frequent patterns. Through it, we can identify the systemic and specific behaviors on the urban transit of Rio de Janeiro.

Keywords: Anomaly detection, road transport, spatiotemporal aggregations, anomaly identification and classification

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