Flow-aware platoon formation of Connected Automated Vehicles in a mixed traffic with human-driven vehicles

Connected Automated Vehicles (CAVs) bring promise of increasing traffic capacity and energy efficiency by forming platoons with short headways on the road. However at low CAV penetration, the capacity gain will be small because the CAVs that randomly enter the road will be sparsely distributed, diminishing the probability of forming long platoons. Many researchers propose to solve this issue by platoon organization strategies, where the CAVs search for other CAVs on the road and change lanes if necessary to form longer platoons. However, the current literature does not analyze a potential risk of platoon organization in disrupting the flow and reducing the capacity by inducing more lane changes. In this research, the authors use driving model of Cooperative Adaptive Cruise Control (CACC) vehicles and human-driven vehicles that are validated with field experiments and find that platoon organization can indeed drop the capacity with more lane changes. But when the traffic demand is well below capacity, platoon organization forms longer CAV platoons without reducing the flow. Based on this finding, the authors develop the Flow-Aware platoon organization strategy, where the CAVs perform platoon organization conditionally on the local traffic state, i.e., a low flow and a high speed. The authors simulate the Flow-Aware platoon organization on a realistic freeway network and show that the CAVs successfully form longer platoons, while ensuring a maximal traffic flow.

• Record URL:
  https://doi.org/10.1016/j.trc.2021.103442
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X21004307
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2021 Soomin Woo andAlexander Skabardonis . Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Woo, Soomin
  • 0000-0001-8326-3145
  • Skabardonis, Alexander
• Publication Date: 2021-12

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 133
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Autonomous intelligent cruise control; Autonomous vehicles; Connected vehicles; Highway capacity; Human beings; Microsimulation; Motor vehicles; Traffic platooning; Vehicle mix
• Subject Areas: Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01789931
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 30 2021 4:26PM