周界管控（Perimeter control）是一种有效的城市交通管理策略，通过聚合网络动力学调节进入拥堵城区的车流。现有方法主要优化系统级效率指标（如总行程时间或路网吞吐量），却常忽视公平性考量，导致各入口处的延误分布不均。本研究将公平性目标显式融入周界管控设计，提出基于队列均衡的机制。本文以旧金山城市路网中的金融区为对象，开展大规模微观仿真实证研究，评估其性能表现与实施挑战。结果表明，传统周界管控不仅可降低总延误与内部延误，亦能改善多种公平性度量指标（Harsanyian、Rawlsian、Utilitarian、Egalitarian）。在此基础上，队列均衡策略在保持传统性能水平的同时，显著提升公平性，尤其在需求异质性场景（即各入口拥堵程度分布不均）下效果更为突出。所提出的框架有助于推动新兴智能交通系统（ITS）的公平化管控设计，并提升用户接受度。

Perimeter control is an effective urban traffic management strategy that regulates inflow to congested urban regions using aggregate network dynamics. While existing approaches primarily optimize system-level efficiency, such as total travel time or network throughput, they often overlook equity considerations, leading to uneven delay distributions across entry points. This work integrates fairness objectives into perimeter control design through explicit queue balancing mechanisms.A large-scale, microscopic case study of the Financial District in the San Francisco urban network is used to evaluate both performance and implementation challenges. The results demonstrate conventional perimeter control not only reduces total and internal delays but can also improve fairness metrics (Harsanyian, Rawlsian, Utilitarian, Egalitarian). Building on this observation, queue balancing strategies match conventional performance while yielding measurable fairness improvements, especially in heterogeneous demand scenarios, where congestion is unevenly distributed across entry points. The proposed framework contributes toward equitable control design for emerging intelligent transportation systems and higher user acceptance for those.