Adapting ramp metering control for mixed autonomy traffic: Toward application on Minnesota freeways

Abstract: The introduction of automated vehicles (AVs) and connected automated vehicles (CAVs) is expected to significantly transform traffic dynamics. While AVs have the potential to improve traffic flow, early versions such as adaptive cruise control (ACC) vehicles may actually disrupt traffic flow. This highlights the need to develop new traffic control strategies. For example, ramp metering control strategies need to be modified and adapted to accommodate the progress of automation. Although a theoretical study has demonstrated that an extended ramp metering control algorithm is efficient in regulating traffic at different market penetration rates (MPR) of automated vehicles, it remains unclear whether the proposed control algorithm is effective for implementation.

In this study, we analyze the impact of mixed-autonomy traffic on the performance of ramp metering by simulating the control algorithm at a Minneapolis on-ramp site. Our simulation results reveal that a higher MPR of ACC vehicles can diminish traffic flow efficiency, while AVs and CAVs show potential improvements in traffic flow dynamics. For example, the travel time on the simulated highway mainline increases by 71.6\% at 100\% MPR of ACC vehicles. The travel time decreases on the mainline by 16.9\% and 32.6\% at 100\% MPR of AVs and CAVs, respectively. Furthermore, we propose an adjusted algorithm that slightly improves mainline traffic flow conditions while significantly reducing ramp waiting times across all vehicle types. On average, the wait time on the ramp decreases by 3.9\%, 4.9\%, and 17.9\% for ACCs, AVs, and CAVs, respectively. The findings of this study offer insights into adaptable traffic management strategies for mixed-autonomy traffic.