Anomalous Behavior Detection in Trajectory Data of Older Drivers
This work addresses the detection of mild cognitive impairment and safety issues for older drivers, representing an incremental improvement in domain-specific applications.
The paper tackled the problem of detecting anomalous driving behaviors in older drivers' trajectory data, proposing an Edge-Attributed Matrix method that successfully identified abnormal behaviors in real-world datasets.
Given a road network and a set of trajectory data, the anomalous behavior detection (ABD) problem is to identify drivers that show significant directional deviations, hardbrakings, and accelerations in their trips. The ABD problem is important in many societal applications, including Mild Cognitive Impairment (MCI) detection and safe route recommendations for older drivers. The ABD problem is computationally challenging due to the large size of temporally-detailed trajectories dataset. In this paper, we propose an Edge-Attributed Matrix that can represent the key properties of temporally-detailed trajectory datasets and identify abnormal driving behaviors. Experiments using real-world datasets demonstrated that our approach identifies abnormal driving behaviors.