Citation

Ghoreishi SGA, Moshfeghi S, Jan MT, Conniff J, Yang KS, Jang J, Furht B, Tappen R, Newman D, Rosselli M, Zhai J. IEEE Int. Conf. Smart Communities Improv. Qual. Life Using AI Robot IoT HONET 2023; 2023: 146-151.

Copyright

(Copyright © 2023, IEEE Computer Society Conference Publishing Services)

DOI

10.1109/HONET59747.2023.10374878

PMID

38567025

PMCID

PMC10985541

Abstract

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, hard-brakings, 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.

Language: en

Keywords

abnormal detection and spatio-temporal network database; Trajectory data mining