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Prédiction de l'état du trafic routier basée sur les motifs et les chaînes de Markov

Feda Almuhisen 1 Nicolas Durand 1 Leonardo Brenner 2 Quafafou Mohamed 1
1 DANA - Data Mining at scale
LIS - Laboratoire d'Informatique et Systèmes
2 MOFED - Modèles et Formalismes à Evénements Discrets
LIS - Laboratoire d'Informatique et Systèmes
Abstract : This paper proposes a new method for predicting traffic state within short time windows. This method takes advantage from space-partitioning, pattern extraction and Markov modelling. From trajectories, frequent regions are extracted where vehicles repeatedly pass through by using the frequent closed patterns and the traffic state is detected based on the evolution of these patterns over time. The next state of traffic for the frequent regions is then predicted based on the Markov models. Experiments on real-world data show that the proposed method is more accurate than a baseline method.
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Submitted on : Wednesday, December 12, 2018 - 2:27:52 PM
Last modification on : Tuesday, April 2, 2019 - 9:43:19 AM
Long-term archiving on: : Wednesday, March 13, 2019 - 2:11:02 PM


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  • HAL Id : hal-01858562, version 1



Feda Almuhisen, Nicolas Durand, Leonardo Brenner, Quafafou Mohamed. Prédiction de l'état du trafic routier basée sur les motifs et les chaînes de Markov. 25èmes Rencontres de la Société Francophone de Classification (SFC 2018), Sep 2018, Paris, France. ⟨hal-01858562⟩



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