Merging and Diverging Impact on Mixed Traffic of Regular and Autonomous Vehicles

In the context of Connected and Autonomous Vehicles (CAVs), this paper aims to examine the impacts of CAVs on mixed regular-automated traffic flow with the increase of the market penetration rate, in consideration of on-ramp merging and off-ramp diverging of vehicles. Lane changes are a major part of lateral motions, affecting surrounding vehicles locally and traffic flow collectively. On the basis of reinforcement learning technique, a cooperative lane-changing strategy was first developed to enable farsighted lane-changing behavior by CAVs in favor of traffic efficiency. The 3-lane highway stretch with one on-ramp and one off-ramp was applied in this study. With extensive simulations, the results suggest that the inclusion of CAVs considerably improves traffic flow, mean speed, and traffic capacity. Meanwhile, the existence of on/off-ramps has substantial impacts on the lane-changing processes. This work can shed some light on an aspect of the mixed traffic network dynamics for future mobility.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2020.2974291
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2021, IEEE.
• Authors:
  • Guo, Jingqiu
  • Cheng, Senlin
  • Liu, Yangzexi
• Publication Date: 2021-3

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 1639-1649
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 22
  • Issue Number: 3
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Behavior; Connected vehicles; Driving; Interchange ramps; Lane changing; Traffic flow; Traffic models; Vehicle mix
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01770499
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Apr 26 2021 3:14PM