Unsupervised LSTMs-based Learning for Anomaly Detection in Highway Traffic Data

Since road traffic is nowadays predominant, improving its safety, security and comfortability may have a significant positive impact on people’s lives. This objective requires suitable studies of traffic behavior, to help stakeholders in obtaining non-trivial information, understanding the traffic models and plan suitable actions. While, on one hand, the pervasiveness of georeferencing and mobile technologies allows us to know the position of relevant objects and track their routes, on the other hand the huge amounts of data to be handled, and the intrinsic complexity of road traffic, make this study quite difficult. Deep Neural Networks (NNs) are powerful models that have achieved excellent performance on many tasks. In this paper we propose a sequence-to-sequence (Seq2Seq) autoencoder able to detect anomalous routes and consisting of an encoder Long Short Term Memory (LSTM) mapping the input route to a vector of a fixed length representation, and then a decoder LSTM to decode back the input route. It was applied to the TRAP2017 dataset freely available from the Italian National Police.