Traffic Anomaly Detection in Intelligent Transport Applications with Time Series Data using Informer
Xinggan Peng, Yuxuan Lin, Qi Cao, Yigang Cen, Huiping Zhuang, Zhiping Lin
Abstract
Multivariate time series traffic dataset is usually large with multiple feature dimensions for long time duration under certain time intervals or sampling rates. In applications such as intelligent transportation systems, some machine learning methods being applied to traffic anomaly detections are computed under certain assumptions and require further improvements. Transport traffic time series data may also suffer from unbalanced number of training data where large amount of labelled training data available for a few popular classes, but with very small amount of labelled data for corner cases. In this paper, based on the recent long sequences prediction method Informer, an anomaly detection algorithm with an anomaly score generator is proposed that does not require any assumptions of data. The encoder-decoder architecture is adopted in the anomaly score generator. The encoder consists of three stacking ProbSparse self-attention mechanisms that significantly reduce computing complexity. The decoder incorporates two multi-head attention layers and a fully connected layer to obtain an output of anomaly scores. Then a One-Class Support Vector Machines (OCSVM) is applied to be the anomaly classifier. The proposed algorithm is capable of detecting anomalies for both vehicle traffic flows and pedestrian flows. It has been verified by applying to a real-world dataset consisting of traffic flows recorded in 2021, as well as to a public anomaly detection dataset.