Competitive Learning for Unsupervised Anomaly Detection in Intelligent Transportation Systems

Intelligent Transportation Systems (ITSs) are expected to have a profound impact on the quality of experience in future smart cities. Anomaly detection is an imperative for urban ITS applications to alleviate vulnerabilities that may cause accidents and fatal causalities. Previously proposed anomaly detection methods mostly require prior knowledge and domain specific training and/or optimization procedures. Therefore, in this work, we propose Competitive Learning based Anomaly Detection (CLAD) as a generic end-to-end approach for unsupervised anomaly detection using Auto Regressive Integrated Moving Average (ARIMA) forecasting model, data imaging and Centroid Neural Networks (CentNNs). Utilizing multi-dimensional time-series data obtained from diverse sensory measurements in the DIGINET-PS smart city infrastructure of TU Berlin, we compare performance of CLAD with unsupervised competitive learning as well as deep learning based anomaly detection techniques. Experimental results show that proposed approach results in higher detection accuracy and precision compared to other methods when multiple degrees of anomalies are considered.