2023/11/29 by Seyedeh Gol Ara Ghoreishi, Ghoreishi, Seyedeh Gol Ara, Sonia Moshfeghi +19
Computer Science · Medicine · Social Sciences · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis
paper · pdf · doi:10.48550/arxiv.2311.17822
openalex publication_date 2023/11/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.