Citation

Zhou T, Zhang J. Accid. Anal. Prev. 2019; 132: e105256.

Affiliation

Mobilities and Urban Policy Lab, Graduate School for International Development and Cooperation, Hiroshima University, Higashi Hiroshima, 739-8529, Japan. Electronic address: zjy@hiroshima-u.ac.jp.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.aap.2019.105256

PMID

31442922

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.

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.

Copyright © 2019 Elsevier Ltd. All rights reserved.

Language: en

Keywords

DBSCAN; Digital tachograph data; Driving behavior; Multilevel modeling; PCA; Truck driver