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Inspired by the recent success of deep learning in multiscale informationencoding, we introduce a variational autoencoder (VAE) based semi-supervisedmethod for detection of faulty traffic data, which is cast as a classificationproblem. Continuous wavelet transform (CWT) is applied to the time series oftraffic volume data to obtain rich features embodied in time-frequencyrepresentation, followed by a twin of VAE models to separately encode normaldata and faulty data. The resulting multiscale dual encodings are concatenatedand fed to an attention-based classifier, consisting of a self-attention moduleand a multilayer perceptron. For comparison, the proposed architecture isevaluated against five different encoding schemes, including (1) VAE with onlynormal data encoding, (2) VAE with only faulty data encoding, (3) VAE with bothnormal and faulty data encodings, but without attention module in theclassifier, (4) siamese encoding, and (5) cross-vision transformer (CViT)encoding. The first four encoding schemes adopted the same convolutional neuralnetwork (CNN) architecture while the fifth encoding scheme follows thetransformer architecture of CViT. Our experiments show that the proposedarchitecture with the dual encoding scheme, coupled with attention module,outperforms other encoding schemes and results in classification accuracy of96.4%, precision of 95.5%, and recall of 97.7%.

Semi-supervised multiscale dual-encoding method for faulty traffic data detection