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.
The deep models are compared to a classical Dynamic Time Warping (DTW) approach and the results indicated that the deep model is not only more efficient (especially for large data sets) than the state of the art, but also yielded better accuracy for two standard benchmarks. More closely related to our approach is the work by @cite_6 . They used Replicator Neural Networks (RNNs) to reconstruct the input sample and, once the model was trained, samples that have a high reconstruction error were marked as outliers. Our approach to outlier detection is similar to this work in the sense that both models learn only frequent samples, while uncommon samples yield higher errors. On the other hand, RioBusData does not use a sample reconstruction approach: instead of applying RNNs over the data, it uses CNNs. To the best of our knowledge, RioBusData is the first application of CNNs for outlier detection, and, as outlined in , the results are promising.
@cite_6
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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.