@article{ref1,
title="Predicting driving distraction patterns in different road classes using a support vector machine",
journal="International journal for traffic and transport engineering (Belgrade)",
year="2021",
author="Ahangari, Samira and Jeihani, Mansoureh and Rahman, Md Mahmudur and Dehzangi, Abdollah",
volume="11",
number="1",
pages="e6-e6",
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.<p /> <p>Language: en</p>",
language="en",
issn="2217-5652",
doi="10.7708/ijtte.2021.11(1).06",
url="http://dx.doi.org/10.7708/ijtte.2021.11(1).06"
}