Distributive Perimetral Queue Balancing Mechanisms: Towards Equitable Urban Traffic Gating and Fair Perimeter Control

Sun, 10 Jan 2027 00:00:00 +0000

106th Annual Meeting of the Transportation Research Board (TRB 2027), Washington, DC, USA, January 10-14, 2027.

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.

TRB conference page · Source code on GitHub

Multi-Agent Reinforcement Learning of Karma Bidding Strategies

Sat, 11 Jul 2026 00:00:00 +0000

43rd International Conference on Machine Learning (ICML 2026), New Frontiers in Game-Theoretic Learning Workshop, Seoul, South Korea, July 11, 2026.

Capacity-constrained shared infrastructure systems require demand management mechanisms that balance efficiency and fairness. Karma mechanisms address this challenge using an artificial, non-tradable currency that enables decentralized allocation through repeated bidding, but computing equilibrium strategies in such settings is difficult due to unknown population dynamics, stochastic demand, and computation time in practice. Here, we study suitable regimes to prove convergence towards stochastic Nash equilibria when following a multi-agent reinforcement learning approach. Computational case studies demonstrate empirically that learned policies closely approximate equilibrium behavior, and further assess impact of learning algorithm and policy initialization on convergence speed. The work highlights the practical potential of the approach for real-world decision support in repeated access-allocation settings.

ICML conference page · Paper on OpenReview · Source code on GitHub

Anastasia Psarou | Kevin Riehl

Distributive Perimetral Queue Balancing Mechanisms: Towards Equitable Urban Traffic Gating and Fair Perimeter Control

Multi-Agent Reinforcement Learning of Karma Bidding Strategies