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.
We consider the problem of finding outliers in large multivariate databases. Outlier detection can be applied during the data cleansing process of data mining to identify problems with the data itself, and to fraud detection where groups of outliers are often of particular interest. We use replicator neural networks (RNNs) to provide a measure of the outlyingness of data records. The performance of the RNNs is assessed using a ranked score measure. The effectiveness of the RNNs for outlier detection is demonstrated on two publicly available databases.