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P-ADRIP : un système multi-agent auto-organisateur pour la prévision du trafic routier

Abstract : Traffic forecasting has gained more and more interests in both academic and industrial research activities. Recently, many methods for traffic forecasting based on machine learning and deep learning approaches are proposed. However, these models always encounter the unsolved questions relating to the reliability and the feasibility. Indeed, traffic forecasting is a very challenging task due to the complex spatial correlations, the high-level time dependency and the difficulty of long-term prediction. To address the mentioned challenges, we propose a novel system based on multi-agent systems approach called P-ADRIP (Prediction subsystem - Adaptive multi-agent system for DRIving behaviors Prediction) that aims to provide dynamic and real-time traffic prediction. The conducted experiments demonstrate the outstanding performance of P-ADRIP comparing to the state-ofthe-art prediction methods.
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Submitted on : Tuesday, August 30, 2022 - 2:56:56 PM
Last modification on : Friday, October 14, 2022 - 10:38:47 AM


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  • HAL Id : hal-03764716, version 1


Ha-Nhi Ngo, Elsy Kaddoum, Marie-Pierre Gleizes, Jonathan Bonnet, Goursolle Anaïs. P-ADRIP : un système multi-agent auto-organisateur pour la prévision du trafic routier. Journées Francophones sur les Systèmes Multi-Agents : SMA et Smart Cities (JFSMA 2022), Jun 2022, Saint-Etienne, France. ⟨hal-03764716⟩



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