Deep learning approaches for electrical vehicular mobility management - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

Deep learning approaches for electrical vehicular mobility management

(1, 2, 3, 4) , (5) , (6) , (1, 7, 4) , (1, 2, 3, 4) , (8)
1
2
3
4
5
6
7
8

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.
Not file

Dates and versions

hal-02460694 , version 1 (30-01-2020)

Identifiers

Cite

Aicha Dridi, Chérifa Boucetta, Abubakar Yau Alhassan, Hassine Moungla, Hossam Afifi, et al.. Deep learning approaches for electrical vehicular mobility management: invited paper. International Conference on Wireless Networks and Mobile Communications (WINCOM), 2019, Fez, Morocco. pp.1-6, ⟨10.1109/WINCOM47513.2019.8942569⟩. ⟨hal-02460694⟩
66 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More