Zhao Xiaohang, Yan Yangzhi
School of Civil Engineering, Dalian University of Technology, Dalian 116024, China.
Department of Architecture and Civil Engineering, School of Engineering, City University of Hong Kong, Hong Kong 999077, China.
Sensors (Basel). 2025 Apr 9;25(8):2389. doi: 10.3390/s25082389.
Efficient parking distribution is crucial for urban traffic management; nevertheless, variable demand and spatial disparities raise considerable obstacles. Current research emphasizes local optimization but neglects the fundamental challenges of real-time parking allocation, resulting in inefficiencies within intricate metropolitan settings. This research delineates two key issues: (1) A dynamic imbalance between supply and demand, characterized by considerable fluctuations in parking demand over time and across different locations, rendering static allocation solutions inefficient; (2) spatial resource optimization, aimed at maximizing the efficiency of limited parking spots to improve overall system performance and user satisfaction. We present a Multi-Agent Reinforcement Learning (MARL) framework that incorporates adaptive optimization and intelligent collaboration for dynamic parking allocation to tackle these difficulties. A reinforcement learning-driven temporal decision mechanism modifies parking assignments according to real-time data, whilst a Graph Neural Network (GNN)-based spatial model elucidates inter-parking relationships to enhance allocation efficiency. Experiments utilizing actual parking data from Melbourne illustrate that Multi-Agent Reinforcement Learning (MARL) substantially surpasses conventional methods (FIFO, SIRO) in managing demand variability and optimizing resource distribution. A thorough quantitative investigation confirms the strength and flexibility of the suggested method in various urban contexts.
高效的停车分布对于城市交通管理至关重要；然而，需求的变化和空间差异带来了相当大的障碍。当前的研究强调局部优化，但忽视了实时停车分配的根本挑战，导致在复杂的大都市环境中效率低下。本研究阐述了两个关键问题：（1）供需之间的动态不平衡，其特征是停车需求随时间和不同地点存在相当大的波动，使得静态分配解决方案效率低下；（2）空间资源优化，旨在最大化有限停车位的效率，以提高整体系统性能和用户满意度。我们提出了一个多智能体强化学习（MARL）框架，该框架结合了自适应优化和智能协作来进行动态停车分配，以解决这些难题。强化学习驱动的时间决策机制根据实时数据修改停车分配，而基于图神经网络（GNN）的空间模型阐明停车之间的关系以提高分配效率。利用来自墨尔本的实际停车数据进行的实验表明，多智能体强化学习（MARL）在管理需求变化和优化资源分配方面大大超过了传统方法（先进先出、随机选择）。全面的定量研究证实了所提出方法在各种城市环境中的优势和灵活性。
