TY  - JOUR
PY  - 2023//
TI  - Anomalous behavior detection in trajectory data of older drivers
JO  - IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET)
A1  - Ghoreishi, Seyedeh Gol Ara
A1  - Moshfeghi, Sonia
A1  - Jan, Muhammad Tanveer
A1  - Conniff, Joshua
A1  - Yang, KwangSoo
A1  - Jang, Jinwoo
A1  - Furht, Borko
A1  - Tappen, Ruth
A1  - Newman, David
A1  - Rosselli, Monica
A1  - Zhai, Jiannan
SP  - 146
EP  - 151
VL  - 2023
IS  - 
N2  - 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>
LA  - en
SN  - 1949-4106
UR  - http://dx.doi.org/10.1109/HONET59747.2023.10374878
ID  - ref1
ER  -