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In this paper we propose a model for accurate traffic prediction under both normal and abnormal conditions. The model is based on the identification of the traffic patterns shown under both normal and abnormal conditions using the density-based clustering algorithm DBSCAN, and the use of different prediction models for each separate cluster that represents a traffic pattern. The k- Nearest Neighbor...
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