  Buses are the primary means of public transportation in the city of Rio de
Janeiro, carrying around 100 million passengers every month. Recently,
real-time GPS coordinates of all operating public buses has been made publicly
available - roughly 1 million GPS entries each captured each day. In an initial
study, we observed that a substantial number of buses follow trajectories that
do not follow the expected behavior. In this paper, we present RioBusData, a
tool that helps users identify and explore, through different visualizations,
the behavior of outlier trajectories. We describe how the system automatically
detects these outliers using a Convolutional Neural Network (CNN) and we also
discuss a series of case studies which show how RioBusData helps users better
understand not only the flow and service of outlier buses but also the bus
system as a whole.
