Citation

Boateng C, Yang K, Ghoreishi SGA, Jang J, Jan MT, Conniff J, Furht B, Moshfeghi S, Newman D, Tappen R, Zhai J, Rosseli M. IEEE Int. Conf. Smart Communities Improv. Qual. Life Using AI Robot IoT HONET 2023; 2023: 210-215.

Copyright

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

DOI

10.1109/honet59747.2023.10374718

PMID

38560052

PMCID

PMC10979306

Abstract

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.

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.

Language: en

Keywords

GPS; Singular Value Decomposition