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2023 | OriginalPaper | Chapter

Recognition and Comparison of Driving Styles of Heavy-Duty Truck Drivers Under Different Scenarios

Authors : Linghua Yu, Yongfeng Ma, Shuyan Chen, Hong Yao, Muxiong Zhou

Published in: Green Transportation and Low Carbon Mobility Safety

Publisher: Springer Nature Singapore

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Abstract

With the acceleration of urbanization, the demand for heavy-duty trucks has increased and transportation safety and management issues are facing large challenges. The heavy-duty truck driver’s behavior is characterized by his or her driving style and plays an important role in driving safety. Consequently, this paper proposes a novel framework to classify driving styles of heavy-duty trucks and make comparision under different scenarios. On rural road and urban road, 11 heavy-duty truck drivers were chosen to conduct experiments under no load or full load. VBOX device was applied to collect data including speed, acceleration and location information. K-means clustering was used to divide driving style into three categories including aggressive, normal and calm. The results show that load and road environment have a great influence on the driving style of heavy-duty truck drivers. It is worth noting that heavy-duty truck drivers are more aggressive with full load than no load when on urban roads. The empirical results demonstrate that the proposed method has efficiency in recognizing the driving style and reveal the variations of the driving style of heavy-duty truck drivers under different scenarios. Moreover, it is meaningful and practical to analyze the driving style in improving road construction, traffic safety and reducing energy consumption.

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Metadata
Title
Recognition and Comparison of Driving Styles of Heavy-Duty Truck Drivers Under Different Scenarios
Authors
Linghua Yu
Yongfeng Ma
Shuyan Chen
Hong Yao
Muxiong Zhou
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Green Transportation and Low Carbon Mobility Safety

Print ISBN: 978-981-19-5614-0

Electronic ISBN: 978-981-19-5615-7

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-19-5615-7_52

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