Multi-lane-merging strategy for connected automated vehicles on freeway ramps

Xiaoling Luo, Xiaofeng Li, Mohammad Razaur Rahman Shaon, Yongxiang Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

Most studies assume the ramp to be a single lane regardless of the possibility of multiple lanes on a freeway ramp. This assumption limits the application of previous ramp metering strategies in the real world. Therefore, this paper proposes a strategy for a centralized controller to efficiently merge connected automated vehicles from a multiple-lane ramp. The proposed strategy aims to allow vehicles from different lanes to pass through the conflict point with the objective of minimum delay and fuel consumption. Numerical experiments are carried out to compare the proposed strategy with first-in-first-out (FIFO) and Vissim built-in strategies. Simulation results indicate that the proposed strategy can reduce more delay than FIFO and Vissim-based strategies. Furthermore, the proposed strategy is also found to be the most reliable in various scenarios with different traffic demand splits, safe headways, and numbers of lanes.

Original languageEnglish (US)
JournalTransportmetrica B
DOIs
StateAccepted/In press - 2022
Externally publishedYes

Keywords

  • connected automated vehicles
  • Freeway ramp
  • sequence optimization
  • trajectory optimization
  • vehicle merging

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Transportation

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