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A Federated Learning and Multi-Agent Reinforcement Learning Framework For Sustainable and Fair Smart Parking Systems in Urban Environments
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DOI：10.1007/s13177-026-00628-7.png)
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Abstract
En 中文
Urban mobility is increasingly challenging due to the rapid growth of the vehicles, exacerbated congestion, energy dissipation, ecological degradation and environmental concerns. The smart parking system had emerged as a promising solution for this concern; however, existing approaches find it difficult to ensure fairness in space allocation, scalability, and adaptability to the dynamic conditions of urban parking systems. The centralized learning based models had raised the challenging aspect of data privacy, communication overhead and the limited generalization process across the diversified environment. This research work addresses this challenge, by proposing the Federated Learning (FL) and Multi-Agent Reinforcement Learning (FL-MARL) method for the fair and sustainable smart parking management in the urban environment. The proposed framework leverages the federated learning to enable the decentralized training model across the various distributed parking zones, ensuring the data privacy, while the multi-agent reinforcement learning model coordinates the vehicles and parking resources for optimizing the space allocation, fairness and minimal search time. The proposed framework achieves a 96.3% prediction accuracy, a 45% reduction in CO2 emissions, a 31% improvement in parking search time, and a 28% enhancement in resource utilization compared to existing methods. This study's novelty lies in the synergistic integration of Federated Learning and Multi-Agent Reinforcement Learning to achieve privacy-preserving, fair, and energy-efficient parking management - addressing scalability, fairness, and data security challenges simultaneously.
Keywords:
Smart parking system
Federated learning
Multi-agent reinforcement learning
Data privacy
Traffic optimization
Real world datasets
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