Abstract
This paper presents a novel deep learning architecture for identifying outliers in the context of intelligent transportation systems. The use of a convolutional neural network with an efficient decomposition strategy is explored to find the anomalous behavior of urban traffic flow data. The urban traffic flow data set is decomposed into similar clusters, each containing homogeneous data. The convolutional neural network is used for each data cluster. In this way, different models are trained, each learned from highly correlated data. A merging strategy is finally used to fuse the results of the obtained models. To validate the performance of the proposed framework, intensive experiments were conducted on urban traffic flow data. The results show that our system outperforms the competition on several accuracy criteria.