A CAV-based perimeter-free regional traffic control strategy utilizing existing parking infrastructure
Hao Liu, Vikash V. Gayah
TL;DR
This work tackles regional traffic congestion in networks with connected and autonomous vehicles by replacing traditional perimeter metering with a perimeter-free holding strategy that parks CAVs with long remaining travel distances at nearby parking when density exceeds a critical level $\rho_{cr}$. The method integrates with a base Max-Pressure signal control (Q-MP) to form Q-MPH, preserving maximum stability while using parking capacity $K^p$ to temporarily remove vehicles from the street and re-introduce them after a holding time $\tau$. Microscopic simulations show that Q-MPH improves network throughput and reduces travel times for both held and non-held vehicles, is robust to parking location patterns and capacity, and remains effective under partial CAV penetration; it also outperforms Bang-Bang and N-MP perimeter controls under various scenarios. The approach offers practical implications for leveraging existing parking resources, informing parking expansion policies and facilitating smoother transitions to higher CAV shares without prescribing a fixed region perimeter.
Abstract
This paper proposes a novel perimeter-free regional traffic management strategy for traffic networks under a connected and autonomous vehicle (CAV) environment. The proposed strategy requires CAVs, especially those with long remaining travel distances, to temporarily wait at nearby parking facilities when the network is congested. After a designated holding time, these CAVs are allowed to re-enter the network. Doing so helps reduce congestion and improve overall operational efficiency. Unlike traditional perimeter control approaches that restrict inflows to congested regions, the proposed holding strategy leverages existing parking infrastructure to temporarily hold vehicles in a way that partially avoids local queue accumulation issues. The proposed method can be easily integrated with existing signal control methods and retains the maximum stability property of the original traffic signal control methods. Simulation results show that the proposed strategy not only reduces travel time for vehicles that are not held, but can also reduce travel times for some of the held vehicles as well, which serves as another key merit of the proposed approach. Compared to the two benchmark perimeter control algorithms, the proposed strategy is more robust against demand patterns and generates stronger improvements in the operational efficiency. Importantly, since the proposed strategy requires existing parking infrastructure, its performance has been demonstrated under various configurations of parking locations and capacities. Particularly, it is demonstrated that the utilization of the parking facility consistently improves overall traffic efficiency, regardless of the facility's size. Lastly, the proposed strategy is shown to be beneficial in a partial CAV environment where only a subset of vehicles are available for holding.