Abstract
The work in progress on a LSTM sequence to sequence variational autoencoder generative model is presented. The architecture is trained on a floating car data dataset in order to grasp the statistical features of the traffic demand in the city of Rome. An analysis of parameters influence is furnished. The generated trajectories are briefly compared with the ones in the dataset. Further work direction is provided.
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Acknowledgements
This work has been partly carried out in the framework of the Triennial Plan 2019–2021 of the National Research on the Electrical System (Piano Triennale 2019–2021 della Ricerca di sistema elettrico nazionale), funded by the Italian Ministry of Ecologic Transition.
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Chiesa, S., Taraglio, S. (2021). Traffic Modelling Through a LSTM Variational Auto Encoder Approach: Preliminary Results. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12950. Springer, Cham. https://doi.org/10.1007/978-3-030-86960-1_43
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