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This paper proposes a novel anomaly classification algorithm that can be deployed in a distributed manner and utilizes microscopic traffic variables shared by neighboring vehicles to detect and classify traffic anomalies under different traffic conditions. The algorithm, which incorporates multiresolution concepts, is based on the likelihood estimation of a neural network output and a bisection-based decision threshold. We show that, when applied to real-world traffic scenarios, the proposed algorithm can detect all the traffic anomalies of the reference test data set; this result represents a significant improvement over our previously proposed algorithm. We also show that the proposed algorithm can effectively detect and classify traffic anomalies even when the following two cases occur: 1) the microscopic traffic variables are available from only a fraction of the vehicle population, and 2) some microscopic traffic variables are lost due to degradation in vehicle-to-vehicle (V2V) or vehicle-to-infrastructure communications (V2I).