Leveraging existing high-occupancy vehicle lanes for mixed-autonomy traffic management with emerging connected automated vehicle applications

Transportation agencies have started including phased deployment of connected and automated vehicle (CAV) technologies in their transportation plans and programs. While theoretical analyses have indicated significant benefits of CAVs for improving system performance, deploying these technologies at existing or adapted highway facilities concerns more than technological issues. This study introduces the concepts and operational strategies of using managed lanes, High-Occupancy Vehicle (HOV) lanes in particular, for mixed-autonomy traffic management. The study enhances existing and develops new algorithms for three selected freeway CAV applications as a CAV technology integration, including speed harmonization, cooperative adaptive cruise control (CACC), and cooperative merge. Instead of evaluation on a synthetic segment, this study reports a large-scale simulation-based real-world case study to investigate CAV early deployment opportunities on Interstate 66 outside the Beltway. The simulation results show that, for all scenarios, individual and bundled CAV applications can significantly improve traffic performance in terms of delay and throughput. CACC platooning is the most effective individual strategy to improve traffic performance. The bundled CAV applications can benefit the system, even with low CAV market penetration, and fully handle more than 130 percent of the existing demand with high CAV market penetration rates. Additionally, left-side dedicated ramps and shared managed lane operational strategies are also beneficial, even during early deployment stages.

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    • This material is published by permission of the Next Mobility Lab, operated by the University of Cincinnati, and produced for the US Department of Transportation under Contract No. DTFH6116D00030. The US Government retains for itself, and others acting on its behalf, a paid-up, non-exclusive, and irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. . Abstract reprinted with permission of Taylor & Francis.
  • Authors:
    • Guo, Yi
    • Ma, Jiaqi
  • Publication Date: 2020-1

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  • English

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  • Accession Number: 01762035
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jan 8 2021 9:30AM