Event-Triggered Regulation of Mixed-Autonomy Traffic Under Varying Traffic Conditions

Abstract: Modeling and congestion mitigation of mixed-autonomy traffic systems consisting of human-driven vehicles (HVs) and autonomous vehicles (AVs) have become increasingly critical with the rapid development of autonomous driving technology. This paper develops an event-triggered control (ETC) framework for mitigating congestion in such systems, which are modeled using an extended Aw-Rascle-Zhang (ARZ) formulation consisting of coupled 4 x 4 hyperbolic partial differential equations (PDEs). Ramp metering is employed as the boundary actuation mechanism. To reduce computational and communication burdens while avoiding excessive ramp signal changes, we design the ETC strategy based on the backstepping method, together with an observer-based ETC formulation for practical implementation under limited sensing. Rigorous Lyapunov analysis ensures exponential convergence and avoidance of Zeno behavior. Extensive simulations validate the proposed approach under diverse traffic scenarios, including varying AV penetration rates, different spacing policies, multiple demand levels, and non-recurrent congestion patterns. Results show that ETC not only stabilizes mixed traffic flows but also significantly reduces control updates, improving driver comfort, and roadway safety. Higher AV penetration rates lead to longer release time and fewer triggering events, indicating the positive impact of AVs in mitigating traffic congestion while reducing computational resource usage. Compared to continuous backstepping controllers, the proposed ETC achieves near-equivalent stabilization performance with far fewer controller updates, resulting in longer signal release time that reduces driver distraction, which demonstrates great potential for ETC applications in traffic management.