Quantifying air quality benefits resulting from few autonomous vehicles stabilizing traffic
It is anticipated that in the near future, the penetration rate of vehicles with some autonomous capabilities (e.g., adaptive cruise control, lane following, full automation, etc.) will increase on roadways. This work investigates the potential reduction of vehicular emissions caused by the whole traffic stream, when a small number of autonomous vehicles (e.g., 5% of the vehicle fleet) are designed to stabilize the traffic flow and dampen stop-and-go waves. To demonstrate this, vehicle velocity and acceleration data are collected from a series of field experiments that use a single autonomous-capable vehicle to dampen traffic waves on a circular ring road with 20–21 human-piloted vehicles. From the experimental data, vehicle emissions (hydrocarbons, carbon monoxide, carbon dioxide, and nitrogen oxides) are estimated using the Motor Vehicle Emissions Simulator (MOVES) emissions model. This work finds that vehicle emissions of the entire fleet may be reduced by between 15% (for carbon dioxide) and 73% (for nitrogen oxides) when stop-and-go waves are reduced or eliminated by the dampening action of the autonomous vehicle in the flow of human drivers. This is possible if a small fraction (∼5%) of vehicles are autonomous and designed to actively dampen traffic waves. However, these reductions in emissions apply to driving conditions under which stop-and-go waves are present. Less significant reductions in emissions may be realized from a deployment of autonomous vehicles (AVs) in a broader range of traffic conditions.
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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:
- © 2018 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Stern, Raphael E
- Chen, Yuche
- Churchill, Miles
- Wu, Fangyu
- Delle Monache, Maria Laura
- Piccoli, Benedetto
- Seibold, Banjamin
- Sprinkle, Jonathan
- Work, Daniel B
- Publication Date: 2019-2
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 351-365
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Serial:
- Transportation Research Part D: Transport and Environment
- Volume: 67
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 1361-9209
- Serial URL: http://www.sciencedirect.com/science/journal/13619209
Subject/Index Terms
- TRT Terms: Air quality; Environmental impacts; Exhaust gases; Intelligent vehicles; Traffic flow
- Identifier Terms: Motor Vehicle Emission Simulator (MOVES)
- Uncontrolled Terms: Stop and go traffic; Traffic stability
- Subject Areas: Environment; Highways; Operations and Traffic Management; Vehicles and Equipment;
Filing Info
- Accession Number: 01690478
- Record Type: Publication
- Files: TRIS
- Created Date: Dec 31 2018 3:27PM