Road Traffic Detection with a LSTM Autoencoder using State of Polarization on Deployed Metropolitan Fiber Cable

We demonstrate the use of an unsupervised autoencoder-based Long Short Term Memory (LSTM) approach to automatically detect road traffic patterns. The model is trained on the dataset acquired from the deployed metropolitan fiber cable in the city of Turin.