An empirical study on fuel consumption of commercial automated vehicles
Increasing commercial vehicles are equipped with automated driving features. Adaptive cruise control, a critical longitudinal control system of commercial automated vehicles (AVs), may have significant impacts on fuel consumption. To investigate the impacts, this paper collected high-resolution trajectory data of commercial AVs with different operating scenarios, speed ranges, and headway settings on the highway system. The AVs’ fuel consumption was calculated by several state-of-the-art or classical vehicle fuel consumption models. From empirical analyses, the authors found that as the AV headway setting increases, the corresponding fuel consumption decreases. Also, the authors found that as the speed of AV traffic increases, the impacts of AV headway settings on fuel consumption decrease. Moreover, the authors compared the fuel consumption of AVs and human-driven vehicles (HVs). The authors found that for the same settings, the AVs always require less fuel consumption than the HVs. Following these findings, a set of managerial insights were provided into relevant stakeholders for future AV traffic.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/13619209
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Supplemental Notes:
- © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Shi, Xiaowei
- Yao, Handong
- Liang, Zhaohui
- Li, Xiaopeng
- Publication Date: 2022-5
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: 103253
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Serial:
- Transportation Research Part D: Transport and Environment
- Volume: 106
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 1361-9209
- Serial URL: http://www.sciencedirect.com/science/journal/13619209
Subject/Index Terms
- TRT Terms: Commercial vehicles; Empirical methods; Fuel consumption; Intelligent vehicles
- Subject Areas: Energy; Geotechnology; Vehicles and Equipment;
Filing Info
- Accession Number: 01843310
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 25 2022 10:06AM