CAV-Based Active Congestion Resolving for Improving Mainline Traffic Flow Efficiency of A Freeway On-Ramp Merging Section

We propose a simple and novel method to improve freeway mainline traffic flow efficiency by actively resolving the congestion caused by a mainline vehicle's facilitating maneuver for creating a gap for on-ramp merging vehicles, in an environment of all connected automated vehicles (CAV s). First, we present an analytical finding derived from the kinematic wave theory with a triangular fundamental diagram, which is consistent with Aimsun simulation results. That is, under an uncongested mainline condition and given the gap size to be created, mainline traffic flow efficiency cannot be improved by manipulating the speed at which the facilitating vehicle creates the gap. This is due to the recovery wave speed being constantly equal to the congestion wave speed. To circumvent this difficulty, we resort to leveraging the congestion resolving phase and propose creating a recovery wave of a speed higher than the normal congestion wave speed. Characterizing CAVs' car-following behaviors by Newell's simplified car-following theory, we analytically show that this can be accomplished by properly modifying the affected mainline CAV s' adopted car-following characteristic constants during congestion resolving. The effectiveness of the proposed method in improving traffic flow efficiency is validated by simulations.