Cooperative decision-making for mixed traffic: A ramp merging example

The rapid conceptual development and commercialization of connected automated vehicle (CAV) has led to the problem of mixed traffic, i.e., traffic mixed with CAVs and conventional human-operated vehicles (HVs). The paper studies cooperative decision-making for mixed traffic (CDMMT). Using discrete optimization, a CDMMT mechanism is developed to facilitate ramp merging, and to properly capture the cooperative and non-cooperative behaviors in mixed traffic. The CDMMT mechanism can be described as a bi-level optimization program in which state-constrained optimal control-based trajectory design problems are imbedded in a sequencing problem. A bi-level dynamic programming-based solution approach is developed to efficiently solve the problem. The proposed modeling mechanism and solution approach are generic to deterministic decisions and can guarantee system-efficient solutions. A micro-simulation environment is built for model validation and analysis of mixed traffic. The results show that compared to the scenario with 100% HVs, ramp-merging can be smoother in mixed traffic environment. At high CAV penetration, the section throughput increases about 18%. With the proposed CDMMT mechanism, traffic throughput can be further increased by 10–15%. The proposed methods form the basis of traffic analysis and cooperative control at ramp-merging sections under mixed traffic environment.

• Record URL:
  https://doi.org/10.1016/j.trc.2020.102764
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X20306768
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2020 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Sun, Zhanbo
  • Huang, Tianyu
  • Zhang, Peitong
• Publication Date: 2020-11

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 120
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Decision making; Dynamic programming; Interchange ramps; Merging traffic; Microsimulation; Optimization; Vehicle mix
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01754554
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 13 2020 9:17AM