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

TL;DR

This paper presents a semi-supervised deep learning approach using variational autoencoders and multiscale dual encoding with attention mechanisms to accurately detect faulty traffic data from time series, outperforming other encoding schemes.

Contribution

It introduces a novel multiscale dual-encoding VAE-based method with attention for fault detection in traffic data, enhancing classification accuracy over existing schemes.

Findings

Achieved 96.4% classification accuracy.

Outperformed other encoding schemes in precision and recall.

Demonstrated effectiveness of dual encoding with attention in traffic fault detection.

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 adopted 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%.