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Deep learning approaches for electrical vehicular mobility management: invited paper

Abstract : Electrical vehicular (EV) energy management is a promising trend. Forecasting vehicular trajectories and delay is crucial for EV energy management. The presented work is devoted to the study and the application of deep learning techniques on specific road trajectories. First, exhaustive deep learning algorithms are considered. Second, road traces are converted to time series. Then, delays and road trajectories are analyzed. In fact, we consider two Recurrent Neural Networks (RNN): LSTM (Long Short Term Memory) and GRU (Gated Recurrent Units). Neural Networks are adapted and trained on 60 days of real urban traffic of Rome in Italy. We calculate the Loss function for both machine learning techniques which is defined by mean square error (MSE) and Root mean square error (RMSE). Experimental results demonstrate that both LSTM and GRU are adequate for the context of EV in terms of route trajectory and delay prediction.
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https://hal.archives-ouvertes.fr/hal-02460694
Contributor : Hossam Afifi <>
Submitted on : Thursday, January 30, 2020 - 11:34:40 AM
Last modification on : Friday, March 27, 2020 - 2:21:18 AM

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Aicha Dridi, Chérifa Boucetta, Abubakar Yau Alhassan, Hassine Moungla, Hossam Afifi, et al.. Deep learning approaches for electrical vehicular mobility management: invited paper. WINCOM 2019: 7th international conference on Wireless Networks and Mobile Communications, Oct 2019, Fez, Morocco. pp.1-6, ⟨10.1109/WINCOM47513.2019.8942569⟩. ⟨hal-02460694⟩

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