@article{ref1,
title="Anomalous behavior detection in trajectory data of older drivers",
journal="IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET)",
year="2023",
author="Ghoreishi, Seyedeh Gol Ara and Moshfeghi, Sonia and Jan, Muhammad Tanveer and Conniff, Joshua and Yang, KwangSoo and Jang, Jinwoo and Furht, Borko and Tappen, Ruth and Newman, David and Rosselli, Monica and Zhai, Jiannan",
volume="2023",
number="",
pages="146-151",
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.<p /> <p>Language: en</p>",
language="en",
issn="1949-4106",
doi="10.1109/HONET59747.2023.10374878",
url="http://dx.doi.org/10.1109/HONET59747.2023.10374878"
}