TY  - JOUR
PY  - 2023//
TI  - Abnormal driving detection using GPS data
JO  - IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET)
A1  - Boateng, Charles
A1  - Yang, Kwangsoo
A1  - Ghoreishi, Seyedeh Gol Ara
A1  - Jang, Jinwoo
A1  - Jan, Muhammad Tanveer
A1  - Conniff, Joshua
A1  - Furht, Borko
A1  - Moshfeghi, Sonia
A1  - Newman, David
A1  - Tappen, Ruth
A1  - Zhai, Jinnan
A1  - Rosseli, Monica
SP  - 210
EP  - 215
VL  - 2023
IS  - 
N2  - Given a GPS dataset comprising driving records captured at one-second intervals, this research addresses the challenge of Abnormal Driving Detection (ADD). The study introduces an integrated approach that leverages data preprocessing, dimensionality reduction, and clustering techniques. Speed Over Ground (SOG), Course Over Ground (COG), longitude (lon), and latitude (lat) data are aggregated into minute-level segments. We use Singular Value Decomposition (SVD) to reduce dimensionality, enabling K-means clustering to identify distinctive driving patterns. <br><br>RESULTS showcase the methodology's effectiveness in distinguishing normal from abnormal driving behaviors, offering promising insights for driver safety, insurance risk assessment, and personalized interventions.<p />  <p>Language: en</p>
LA  - en
SN  - 1949-4106
UR  - http://dx.doi.org/10.1109/honet59747.2023.10374718
ID  - ref1
ER  -