Quantifying the impacts of dynamic control in connected and automated vehicles on greenhouse gas emissions and urban NO₂ concentrations

Communication between vehicles and road infrastructure can enable more efficient use of the road network and hence reduce congestion in urban areas. This improvement can be enhanced by distributed control due to its lighter computational load and higher reliability. Despite favourable impacts on traffic, little is known about the effects of such systems on near-road air quality. In this study, an End-To-End (E2E) dynamic distributed routing algorithm in Connected and Automated Vehicles (CAVs) was applied in downtown Toronto, to identify whether benefits to network throughput were associated with lower near-road NO₂ concentrations. The authors observe significant reductions in the emissions of Greenhouse Gases (GHGs) with increased penetration of CAVs. Nonetheless, at times, the emissions of nitrogen oxides (NOₓ) increased with higher CAVs. Besides, a higher frequency and severity of NO₂ hot-spots were observed under a 100% CAV scenario. Impacts of the proposed system on electric energy consumption in a full electric vehicle network were also investigated, indicating that the addition of CAVs that are electric did not contribute to high energy savings. The authors propose that such new transformative technologies in transportation should be designed with air pollution and public health goals.

• Record URL:
  • https://doi.org/10.1016/j.trd.2019.06.008
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1361920919303402
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13619209
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Tu, Ran
  • Alfaseeh, Lama
  • Djavadian, Shadi
  • Farooq, Bilal
  • Hatzopoulou, Marianne
• Publication Date: 2019-8

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 142-151
• Serial:
  • Transportation Research Part D: Transport and Environment
  • Volume: 73
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1361-9209
  • Serial URL: http://www.sciencedirect.com/science/journal/13619209

Subject/Index Terms

• TRT Terms: Air quality; Algorithms; Autonomous vehicles; Concentration (Chemistry); Connected vehicles; Electric vehicles; Energy consumption; Environmental impacts; Greenhouse gases; Nitrogen dioxide; Nitrogen oxides; Routing; Traffic congestion; Urban areas
• Uncontrolled Terms: Dynamic routing
• Geographic Terms: Toronto (Canada)
• Subject Areas: Energy; Environment; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01711730
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 22 2019 8:00AM