# RioBusData: Outlier Detection in Bus Routes of Rio de Janeiro

arXiv: https://arxiv.org/abs/1601.06128 · HTML: https://mlsanity.com/paper/1601.06128/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2016.

## Built on by (most cited first, 2 of 2)

- [NUMOSIM: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks](https://mlsanity.com/paper/2409.03024/) (2024)
- [Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework](https://mlsanity.com/paper/2410.01011/) (2024)

## Abstract (arXiv)

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.