@article{ref1,
title="Analysis of commercial truck drivers' potentially dangerous driving behaviors based on 11-month digital tachograph data and multilevel modeling approach",
journal="Accident analysis and prevention",
year="2019",
author="Zhou, Tuqiang and Zhang, Junyi",
volume="132",
number="",
pages="e105256-e105256",
abstract="This study analyzed the potentially dangerous driving behaviors of commercial truck drivers from both macro and micro perspectives. The analysis was based on digital tachograph data collected over an 11-month period and comprising 4373 trips made by 70 truck drivers. First, different types of truck drivers were identified using principal component analysis (PCA) and a density-based spatial clustering of applications with noise (DBSCAN) at the macro level. Then, a multilevel model was built to extract the variation properties of speeding behavior at the micro level. <br><br>RESULTS showed that 40% of the truck drivers tended to drive in a substantially dangerous way and the explained variance proportion of potentially extremely dangerous truck drivers (79.76%) was distinctly higher than that of other types of truck drivers (14.70%˜34.17%). This paper presents a systematic approach to extracting and examining information from a big data source of digital tachograph data. The derived findings make valuable contributions to the development of safety education programs, regulations, and proactive road safety countermeasures and management.<br><br>Copyright © 2019 Elsevier Ltd. All rights reserved.<p /> <p>Language: en</p>",
language="en",
issn="0001-4575",
doi="10.1016/j.aap.2019.105256",
url="http://dx.doi.org/10.1016/j.aap.2019.105256"
}