Driving style identification and its association with risky driving behaviors among truck drivers based on GPS, load condition, and in-vehicle monitoring data-Reference-Cited by-同舟云学术

Driving style identification and its association with risky driving behaviors among truck drivers based on GPS, load condition, and in-vehicle monitoring data

Published:2023-07-17 Issue: Volume: Page:1-35
ISSN:1943-9962
Container-title:Journal of Transportation Safety & Security
language:en
Short-container-title:Journal of Transportation Safety & Security

Author:

Zhang Chenxiao¹²³,Ma Yongfeng¹²³,Khattak Aemal J.⁴,Chen Shuyan¹²³,Xing Guanyang¹²³,Zhang Junjie¹²³

Affiliation:

1. School of Transportation, Southeast University, Nanjing, China

2. Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing, China

3. Jiangsu Province Collaborative Innovation Center of Modem Urban Traffic Technologies, Southeast University, Nanjing, China

4. Department of Civil Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA

Funder

National Natural Science Foundation of China

Open Project of Key Laboratory of the Ministry of Public Security for Road Traffic Safety of China

Postgraduate Research & Practice Innovation Program of Jiangsu Province

Publisher

Informa UK Limited

Subject

Safety Research,Transportation

Link

https://www.tandfonline.com/doi/pdf/10.1080/19439962.2023.2233089

Reference69 articles.

1. Driving style recognition using fuzzy logic

2. How instantaneous driving behavior contributes to crashes at intersections: Extracting useful information from connected vehicle message data

3. Safety evaluation of connected and automated vehicles in mixed traffic with conventional vehicles at intersections

4. Safety critical event prediction through unified analysis of driver and vehicle volatilities: Application of deep learning methods

5. Investigating the motivational factors influencing drivers intentions to unsafe driving behaviours: Speeding and overtaking violations

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