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 -
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.
Number of downloads: 470
Keywords:
distracted driving;
driving simulator;
ANOVA;
post hoc tukey;
machine learning;
support vector machine;
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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