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

Zhang, Yihuai; Yu, Huan · 2025 · IEEE Transactions on Intelligent Transportation Systems

DOI: 10.1109/tits.2025.3632297

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Summary

This paper addresses the challenge of mitigating congestion in mixed-autonomy traffic systems, where human-driven vehicles (HVs) and autonomous vehicles (AVs) share roadways. The rapid adoption of autonomous driving technology creates heterogeneous traffic environments with distinct spacing policies and dynamic interactions that often lead to flow instabilities. While existing control strategies like backstepping control offer stability guarantees, they require continuous updates, imposing high computational burdens and causing excessive signal changes that reduce driver comfort. To address these limitations, the authors develop an event-triggered control (ETC) framework that updates control inputs only when specific conditions are met, thereby reducing communication and actuation demands while maintaining system stability. The study models mixed-autonomy traffic using an extended Aw-Rascle-Zhang (ARZ) formulation, consisting of coupled 4×4 hyperbolic partial differential equations (PDEs). This model incorporates Area Occupancy (AO) to distinguish between HV and AV behaviors, accounting for the larger spacing typically maintained by AVs for safety. The authors analyze the system’s free-flow and congested regimes based on characteristic wave speeds, focusing specifically on the congested region where traffic information propagates upstream. The control strategy employs ramp metering as the boundary actuation mechanism. The design utilizes a backstepping method to transform the original system into a stable target system, followed by an observer-based ETC formulation to handle practical implementation under limited sensing. Rigorous Lyapunov analysis is provided to ensure exponential convergence and explicitly rule out Zeno behavior, which would cause infinitely many triggering events in finite time. 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. The results demonstrate that the ETC framework effectively stabilizes mixed traffic flows and significantly reduces the number of control updates compared to continuous backstepping controllers. Specifically, the ETC achieves near-equivalent stabilization performance with far fewer controller updates, leading to longer signal release times that reduce driver distraction. The study also finds that higher AV penetration rates result in longer release times and fewer triggering events, indicating that AVs positively impact congestion mitigation and reduce computational resource usage. The significance of this work lies in its provision of a scalable, efficient control strategy for intelligent transportation systems. By combining rigorous theoretical stability guarantees with practical applicability, the proposed ETC framework reduces the computational load on traffic management systems while improving overall traffic efficiency and safety. The findings suggest that integrating AVs into traffic networks not only enhances flow stability but also facilitates more efficient infrastructure-based control, supporting the deployment of large-scale traffic management solutions in mixed-autonomy environments.

Key finding

The proposed event-triggered control framework stabilizes mixed-autonomy traffic flows with significantly fewer controller updates than continuous backstepping controllers, while higher autonomous vehicle penetration rates further reduce triggering events and improve traffic efficiency.

Methodology

simulation_modeling

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• traffic density