Archivio Istituzionale della RicercaWe 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.
Road Traffic Detection with a LSTM Autoencoder using State of Polarization on Deployed Metropolitan Fiber Cable / Usmani, Fehmida; D'Amico, Andrea; Bratovich, Rudi; Fransisco, ; Martinez, R.; Straullu, Stefano; Virgillito, Emanuele; Aquilino, Francesco; Pastorelli, Rosanna; Curri, Vittorio. - ELETTRONICO. - (2023), pp. 1-3. (Intervento presentato al convegno International Conference on Optical Network Design and Modeling tenutosi a Coimbra, Portugal nel 08-11 May 2023).
Road Traffic Detection with a LSTM Autoencoder using State of Polarization on Deployed Metropolitan Fiber Cable
D’Amico Andrea;Virgillito Emanuele;Curri Vittorio
2023
Abstract
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.
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https://hdl.handle.net/11583/2986368