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Communication Dans Un Congrès Année : 2021

Sequential Patterns for Spatio-Temporal Traffic Prediction

Feda Almuhisen
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  • PersonId : 1022028
Nicolas Durand
Mohamed Quafafou
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  • PersonId : 1056801

Résumé

This paper proposes a method for predicting the traffic status of a city within time windows. The method takes advantage of spacepartitioning, closed sequential pattern extraction, emerging pattern detection, and Markov chain modeling. From trajectories, we identify active regions in which moving objects mostly visit. The traffic status of each region is detected based on continuous tracking of closed sequential patterns evolution over time. Based on the proposed Markov model, the near-future status of traffic is then predicted. The traffic status is reported on maps and can be used to enhance future city transportation. The experiments on real-world data sets show that the proposed method provides promising results.
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Dates et versions

hal-03480960 , version 1 (09-06-2022)

Identifiants

Citer

Feda Almuhisen, Nicolas Durand, Leonardo Brenner, Mohamed Quafafou. Sequential Patterns for Spatio-Temporal Traffic Prediction. The 20th IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2021), Dec 2021, Melbourne, Australia. pp.595-602, ⟨10.1145/3486622.3493977⟩. ⟨hal-03480960⟩
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