Multivariate deep learning approach for electric vehicle speed forecasting

TitreMultivariate deep learning approach for electric vehicle speed forecasting
Publication TypeJournal Article
Year of Publication2021
AuthorsMalek, YN, Najib, M, Bakhouya, M, Essaaidi, M
JournalBig Data Mining and Analytics
Mots-clésDeep learning, Design and control, Dynamic parameters, Electric vehicles, energy efficiency, Forecasting, Forecasting methods, Intelligent systems, Intelligent transport systems, Learning approach, Long short-term memory, Long-term forecasting, Multivariate modeling, Roads and streets, Speed, Traffic control, Traffic simulators, Vehicle actuated signals

Speed forecasting has numerous applications in intelligent transport systems' design and control, especially for safety and road efficiency applications. In the field of electromobility, it represents the most dynamic parameter for efficient online in-vehicle energy management. However, vehicles' speed forecasting is a challenging task, because its estimation is closely related to various features, which can be classified into two categories, endogenous and exogenous features. Endogenous features represent electric vehicles' characteristics, whereas exogenous ones represent its surrounding context, such as traffic, weather, and road conditions. In this paper, a speed forecasting method based on the Long Short-Term Memory (LSTM) is introduced. The LSTM model training is performed upon a dataset collected from a traffic simulator based on real-world data representing urban itineraries. The proposed models are generated for univariate and multivariate scenarios and are assessed in terms of accuracy for speed forecasting. Simulation results show that the multivariate model outperforms the univariate model for short- and long-term forecasting. © 2018 Tsinghua University Press.




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