The deployment of Autonomous Vehicles (AVs) poses considerable challenges and unique opportunities for the design and management of future urban road infrastructure. In light of this disruptive transformation, the Right-Of-Way (ROW) composition of road space has the potential to be renewed. Design approaches and intelligent control models have been proposed to address this problem, but we lack an operational framework that can dynamically generate ROW plans for AVs and pedestrians in response to real-time demand. Based on microscopic traffic simulation, this study explores Reinforcement Learning (RL) methods for evolving ROW compositions. We implement a centralised paradigm and a distributive learning paradigm to separately perform the dynamic control on several road network configurations. Experimental results indicate that the algorithms have the potential to improve traffic flow efficiency and allocate more space for pedestrians. Furthermore, the distributive learning algorithm outperforms its centralised counterpart regarding computational cost (49.55\%), benchmark rewards (25.35\%), best cumulative rewards (24.58\%), optimal actions (13.49\%) and rate of convergence. This novel road management technique could potentially contribute to the flow-adaptive and active mobility-friendly streets in the AVs era.

翻译：自动驾驶车辆（AVs）的部署为未来城市道路基础设施的设计与管理带来了重大挑战与独特机遇。在这一颠覆性变革背景下，道路空间的通行权（ROW）构成有望得到重构。尽管已有多种设计方法与智能控制模型被提出以应对该问题，但尚缺乏能够根据实时需求动态生成面向AVs与行人的ROW方案的操作性框架。本研究基于微观交通仿真，探索利用强化学习（RL）方法演化ROW构成。我们分别采用集中式学习范式与分布式学习范式，对多种道路网络配置实施动态控制。实验结果表明，该算法具有提升交通流效率、为行人分配更多空间的能力。此外，分布式学习算法在计算成本（49.55%）、基准奖励（25.35%）、最佳累积奖励（24.58%）、最优动作（13.49%）及收敛速度方面均优于集中式算法。这种新型道路管理技术有望为AVs时代下流量自适应与主动出行友好型街道的构建做出贡献。