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Abstract

Disclosed are systems and methods for traffic pattern prediction under abnormal behavior through collaborative knowledge transferring from node to node. In one example, a system includes a processor and a memory having instructions that cause the processor to determine vehicle traffic flows at each of a plurality of nodes using a general model that utilizes hyperparameters that derive relationships between and within each node of the plurality of nodes and observed data from sensors monitoring the plurality of nodes. The observed data includes real-world traffic data affected by hidden parameters. Using an understandable algorithm, the general model derives correlations between the hidden parameters from the observed data at multiple levels.

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