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

Yongcan Huang; Jidong J. Yang; Yongcan Huang; Jidong J. Yang

doi:10.3934/aci.2022006

Applied Computing and Intelligence

2022, Volume 2, Issue 2: 99-114. doi: 10.3934/aci.2022006

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Research article

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

Yongcan Huang ,
Jidong J. Yang ^,

Smart Mobility and Infrastructure Laboratory, College of Engineering, University of Georgia, Athens GA, USA

Received: 11 July 2022 Revised: 19 August 2022 Accepted: 12 September 2022 Published: 16 September 2022

Inspired by the recent success of deep learning in multiscale information encoding, we introduce a variational autoencoder (VAE) based semi-supervised method for detection of faulty traffic data, which is cast as a classification problem. Continuous wavelet transform (CWT) is applied to the time series of traffic volume data to obtain rich features embodied in time-frequency representation, followed by a twin of VAE models to separately encode normal data and faulty data. The resulting multiscale dual encodings are concatenated and fed to an attention-based classifier, consisting of a self-attention module and a multilayer perceptron. For comparison, the proposed architecture is evaluated against five different encoding schemes, including (1) VAE with only normal data encoding, (2) VAE with only faulty data encoding, (3) VAE with both normal and faulty data encodings, but without attention module in the classifier, (4) siamese encoding, and (5) cross-vision transformer (CViT) encoding. The first four encoding schemes adopt the same convolutional neural network (CNN) architecture while the fifth encoding scheme follows the transformer architecture of CViT. Our experiments show that the proposed architecture with the dual encoding scheme, coupled with attention module, outperforms other encoding schemes and results in classification accuracy of 96.4%, precision of 95.5%, and recall of 97.7%.
- faulty data detection,
- continuous wavelet transform,
- variational autoencoder,
- self-attention,
- semi-supervised learning
Citation: Yongcan Huang, Jidong J. Yang. Semi-supervised multiscale dual-encoding method for faulty traffic data detection[J]. Applied Computing and Intelligence, 2022, 2(2): 99-114. doi: 10.3934/aci.2022006

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Abstract

Inspired by the recent success of deep learning in multiscale information encoding, we introduce a variational autoencoder (VAE) based semi-supervised method for detection of faulty traffic data, which is cast as a classification problem. Continuous wavelet transform (CWT) is applied to the time series of traffic volume data to obtain rich features embodied in time-frequency representation, followed by a twin of VAE models to separately encode normal data and faulty data. The resulting multiscale dual encodings are concatenated and fed to an attention-based classifier, consisting of a self-attention module and a multilayer perceptron. For comparison, the proposed architecture is evaluated against five different encoding schemes, including (1) VAE with only normal data encoding, (2) VAE with only faulty data encoding, (3) VAE with both normal and faulty data encodings, but without attention module in the classifier, (4) siamese encoding, and (5) cross-vision transformer (CViT) encoding. The first four encoding schemes adopt the same convolutional neural network (CNN) architecture while the fifth encoding scheme follows the transformer architecture of CViT. Our experiments show that the proposed architecture with the dual encoding scheme, coupled with attention module, outperforms other encoding schemes and results in classification accuracy of 96.4%, precision of 95.5%, and recall of 97.7%.

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