  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.
