Ramp metering is a widely deployed traffic management strategy for improving freeway efficiency, yet conventional approaches often lead to highly uneven delay distributions across on-ramps, undermining user acceptance and long-term sustainability. While existing fairness-aware ramp metering methods can mitigate such disparities, they typically rely on centralized optimization, detailed traffic models, or data-intensive learning frameworks, limiting their real-world applicability, particularly in networks operating legacy ALINEA-based systems. This paper proposes C-EQ-ALINEA, a decentralized, coordinated, and equity-aware extension of the classical ALINEA feedback controller. The approach introduces lightweight information exchange among neighbouring ramps, enabling local coordination that balances congestion impacts without centralized control, additional infrastructure, or complex optimization. C-EQ-ALINEA preserves the simplicity and robustness of ALINEA while explicitly addressing multiple notions of fairness, including Harsanyian, Egalitarian, Rawlsian, and Aristotelian perspectives. The method is evaluated in a calibrated 24-hour microsimulation of Amsterdam's A10 ring road using SUMO. Results demonstrate that C-EQ-ALINEA substantially improves the equity of delay distributions across ramps and users, while maintaining (in several configurations surpassing) the efficiency of established coordinated strategies such as METALINE. These findings indicate that meaningful fairness gains can be achieved through minimal algorithmic extensions to widely deployed controllers, offering a practical and scalable pathway toward sustainable and socially acceptable freeway operations. Open source implementation available on GitHub.

翻译：匝道调节是一种广泛应用的交通管理策略，旨在提升高速公路效率，然而传统方法往往导致各入口匝道间的延误分布极不均衡，损害了用户接受度与长期可持续性。尽管现有的公平感知匝道调节方法能够缓解此类差异，但它们通常依赖于集中式优化、详细的交通模型或数据密集型学习框架，这限制了其在实际场景中的应用，尤其是在运行传统ALINEA系统的网络中。本文提出C-EQ-ALINEA，一种对经典ALINEA反馈控制器的去中心化、协调且关注公平性的扩展方法。该方法引入了相邻匝道间的轻量级信息交换，实现了无需集中控制、额外基础设施或复杂优化的局部协调，从而平衡拥堵影响。C-EQ-ALINEA保持了ALINEA的简洁性与鲁棒性，同时明确处理了多种公平性概念，包括哈桑尼、平等主义、罗尔斯和亚里士多德视角。该方法在基于SUMO对阿姆斯特丹A10环路的24小时标定微观仿真中进行了评估。结果表明，C-EQ-ALINEA显著改善了匝道与用户间延误分布的公平性，同时保持（在多种配置下甚至超越了）如METALINE等现有协调策略的效率。这些发现表明，通过对广泛部署的控制器进行最小化的算法扩展，即可实现显著的公平性提升，为可持续且社会可接受的高速公路运营提供了一条实用且可扩展的路径。开源实现可在GitHub上获取。