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Article

PREDICTING DRIVING DISTRACTION PATTERNS IN DIFFERENT ROAD CLASSES USING A SUPPORT VECTOR MACHINE

DOI: 10.7708/ijtte.2021.11(1).06


11 / 1 / 102-114 Pages

Author(s)

Samira Ahangari - Department of Transportation and Infrastructure Studies, Morgan State University, Baltimore, MD 21251, USA -

Mansoureh Jeihani - Department of Transportation and Infrastructure Studies, Morgan State University, Baltimore, MD 21251, USA -

Md Mahmudur Rahman - Department of Computer Science, Morgan State University, Baltimore, MD 21251, USA -

Abdollah Dehzangi - Department of Computer Science, Morgan State University, Baltimore, MD 21251, USA -


Abstract

This study investigates driving behavior under distraction on four different road classes – freeway, urban arterial, rural, and local road in a school zone – using a high-fidelity driving simulator. Some 92 younger participants from a reasonably diverse sociodemographic background drove a realistic midsize network in the Baltimore metropolitan area and were exposed to different distractions. A total of 1,952 simulation runs were conducted. An ANOVA and Tukey Post Hoc analysis showed that distracted driving behavior demonstrates different patterns on various roads. This research developed a support vector machine model that achieved distraction prediction ability among different routes with an accuracy of 94.24%, which to the best of our knowledge, is the best for such a task. The results indicate that driver distraction prediction models probably would be more accurate if developed separately for each road class.


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Acknowledgements:

The authors would like to thank the Maryland Department of Transportation-Motor Vehicle Administration-Maryland Highway Safety Office (GN-Morgan State-2019-291) and the Urban Mobility & Equity Center at Morgan State University for their funding support.


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