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Pattern Mining of Older Drivers' Driving Behavior Through Telematics-data-driven Unsupervised Learning.

Sonia Moshfeghi¹, Jinwoo Jang²

¹Ph.D. Candidate, Department of Civil, Environmental, and Geoamatics, College of Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431 USA.

IEEE Sensors Journal

|November 5, 2025

Summary

This summary is machine-generated.

Older drivers aged 65+ exhibit predominantly conservative driving patterns, according to a study using in-vehicle sensors. This research analyzed driving behaviors to identify distinct styles for improved traffic safety insights.

Keywords:

Driving behavior deep embedded clustering older drivers self-organizing maps sensor data

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Area of Science:

Gerontology
Transportation Safety
Data Science

Background:

Aging populations present unique challenges in road safety.
Older drivers (65+) face increased crash risks and injury severity.
Understanding age-specific driving behaviors is crucial for safety interventions.

Purpose of the Study:

To develop a framework for analyzing and clustering older driver behaviors using in-vehicle sensor data.
To identify distinct driving styles and patterns within the 65+ demographic.
To leverage advanced machine learning for complex driving data interpretation.

Main Methods:

Utilized in-vehicle sensor data, including speed, acceleration, braking, RPM, throttle, fuel, engine, and ambient temperature.
Applied Self-Organizing Maps (SOMs) for data visualization and dimensionality reduction.
Employed Deep Embedded Clustering (DEC) combined with K-means and agglomerative methods for pattern identification.

Main Results:

5x5 grid SOMs effectively visualized multiple driving features simultaneously.
DEC + K-means and DEC + agglomerative clustering proved effective for determining optimal cluster numbers.
Clustering analysis revealed two distinct clusters, with the predominant driving style being conservative.

Conclusions:

The study successfully identified conservative driving patterns as dominant among older drivers (65+).
The proposed framework and methodologies are applicable to diverse driving features and demographics.
Findings support broader applications in traffic analysis, driver behavior modeling, and safety research.