RioBusData: Outlier Detection in Bus Routes of Rio de Janeiro
This work addresses the issue of monitoring and understanding irregular bus routes for transportation authorities and users in Rio de Janeiro, representing an incremental application of existing methods to new data.
The paper tackles the problem of identifying outlier bus trajectories in Rio de Janeiro's public transportation system using real-time GPS data, resulting in a tool called RioBusData that automatically detects these outliers with a Convolutional Neural Network and provides visualizations for user exploration.
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.