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Buses are the primary means of public transportation in the city of Rio deJaneiro, carrying around 100 million passengers every month. Recently,real-time GPS coordinates of all operating public buses has been made publiclyavailable - roughly 1 million GPS entries each captured each day. In an initialstudy, we observed that a substantial number of buses follow trajectories thatdo not follow the expected behavior. In this paper, we present RioBusData, atool that helps users identify and explore, through different visualizations,the behavior of outlier trajectories. We describe how the system automaticallydetects these outliers using a Convolutional Neural Network (CNN) and we alsodiscuss a series of case studies which show how RioBusData helps users betterunderstand not only the flow and service of outlier buses but also the bussystem as a whole.

RioBusData: Outlier Detection in Bus Routes of Rio de Janeiro