Capacity of a freeway lane with platoons of autonomous vehicles mixed with regular traffic

In the near future, autonomous vehicles (AVs) will travel sharing the current freeways with human driven vehicles. The efficiency of this mixed traffic scenario will depend on the ability of AVs to behave cooperatively. Otherwise, the introduction of uncoordinated AVs might lead to capacity reductions. Connected AVs (CAVs) traveling in platoons represents a promising management strategy to get the most from the AVs’ technological revolution. Most of previous research has used traffic microsimulation tools to assess platooning and other CAVs’ cooperative driving strategies, achieving good results. However, the robust macroscopic modeling alternative, which typically yields the necessary insights and fundamental knowledge to set the foundations for the development of management and control strategies, remains almost unexplored. This paper contributes to fill this research gap by providing a generalized macroscopic model to estimate the average CAVs platoon length for a given traffic demand and penetration rate of CAVs. Two different platooning schemes are compared (i.e. cooperative and opportunistic) representing the best and worst case scenarios. The estimation of CAVs platoon length is of much importance as it is the main factor driving capacity improvements on freeways, which under the appropriate conditions could exceed 10.000 vehicles per hour and lane.

• Record URL:
  https://doi.org/10.1016/j.trb.2021.03.010
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0191261521000540
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01912615
• Supplemental Notes:
  • © 2021 Marcel Sala and Francesc Soriguera. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Sala, Marcel
  • Soriguera, Francesc
• Publication Date: 2021-5

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 116-131
• Serial:
  • Transportation Research Part B: Methodological
  • Volume: 147
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0191-2615
  • Serial URL: http://www.sciencedirect.com/science/journal/01912615

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Freeways; Highway capacity; Macroscopic traffic flow; Traffic lanes; Traffic platooning; Vehicle mix
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01774363
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 16 2021 9:26AM