Lane Management with Variable Lane Width and Model Calibration for Connected Automated Vehicles

Connected autonomous vehicles (CAVs) may be able to operate with less longitudinal and lateral spacing than traditional human-driven vehicles (HVs) due to the fast and precise control technologies and cooperative maneuvers. With this appealing feature, it is possible to allocate specific narrower highway lanes to CAVs to increase traffic throughput. This paper proposes an analytical lane management framework that determines the optimal number of CAV lanes needed for a highway segment to maximize its throughput considering the narrowed width of CAV lanes. The proposed optimization model takes into account varying mixed traffic demand levels, CAV market penetration rates, platooning intensities, and CAV technology scenarios. The results from the numerical experiments reveal that the proposed lane management framework with the narrowed CAV lane width increases highway throughput for various parameter settings in different CAV technology scenarios. In order to bring the developed lane management model to the implementation stage, this paper proposes an analytical methodology on how to estimate model parameters (e.g., CAV market penetration rate, platooning intensity, and average headway) with mixed traffic trajectories when they become available in the near future. The authors illustrate that the application of the developed calibration method with synthetic mixed traffic data adapted from the NGSIM dataset.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AHB30 Standing Committee on Vehicle-Highway Automation.
  • Corporate Authors:

    Transportation Research Board

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  • Authors:
  • Conference:
  • Date: 2019

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References;
  • Pagination: 7p

Subject/Index Terms

Filing Info

  • Accession Number: 01697431
  • Record Type: Publication
  • Report/Paper Numbers: 19-02407
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:27AM