Prototype augmentation-based spatiotemporal anomaly detection in smart mobility systems

•A prototype augmentation method utilizes resampling techniques to enhance the diversity of anomaly patterns is presented.•A group of spatiotemporal unsupervised models is constructed to generate low-level anomalies patterns and meta-features.•A hybrid unsupervised-supervised stacking ensemble framework is proposed to detect spatiotemporal anomalies.•Materialization of this framework and evaluation of its efficacy in detecting anomalous line-pressing incidents are validated. In complex mobility systems, the widespread presence of spatiotemporal anomaly patterns poses substantial challenges to effective governance and decision-making. A notable example of this challenge is evident in traffic anomalous incidents detection, where the combination of low accuracy in anomaly detection and poor scenario generalization performance significantly impacts the overall performance of anomaly detection. This paper introduces a prototype augmentation-based framework tailored for spatiotemporal anomaly detection in the context of smart mobility system. This framework utilizes prototype augmentation technique to enhance the diversity of anomaly patterns, ensuring that the integrity of the original anomaly information is preserved. Essentially, the prototype augmentation-based anomaly detector employed in this framework is a hybrid unsupervised-supervised stacking ensemble. It leverages the strengths of unsupervised component learners to generate pseudo dimensions while integrating a supervised meta-detector for evaluating the component learners’ performance across diverse environmental contexts. Additionally, we materialize this framework and assess its performance in detecting anomalous line-pressing incidents. Empirical results demonstrate our framework’s superior accuracy and transferability in detecting anomalous traffic incidents compared to alternative methods using a real-world dataset.