@article{ref1,
title="Study on driving style clustering based on K-means and Gaussian mixture model",
journal="China safety science journal (CSSJ)",
year="2019",
author="Liu, T. and Fu, R. and Zhang, M. and Tian, S.",
volume="29",
number="12",
pages="40-45",
abstract="In order to study drivers' car-following characteristics and explore an effective method to classify driving styles, 50 participants were recruited to carry out a real road driving test. A GMM with results of K-means clustering was established based on two-dimensional variables: average car-following time gap and average braking time gap. And then results of different types of drivers were analyzed. The research shows that clustering result is better with three categories (aggressive drivers, steady drivers, and conservative drivers) with an average contour value of 0. 45. It is found that aggressive drivers tend to choose a smaller car-following time gap or braking time gap while conservative drivers usually take a larger value, and a much softer clustering result with a high separability between samples would be achieved. © 2019 China Safety Science Journal. All rights reserved.<p /><p>Language: zh</p>",
language="zh",
issn="1003-3033",
doi="10.16265/j.cnki.issn1003-3033.2019.12.007",
url="http://dx.doi.org/10.16265/j.cnki.issn1003-3033.2019.12.007"
}