Current Volume 9
Abstract
This paper proposes a Unified Multi-Agent Framework (UMAF) for real-time public transport dispatching in smart cities. The framework integrates eight critical technical domains: spatial flux modeling, 5G V2X communication, battery State-of-Health (SoH) monitoring, human-centric automation, federated edge privacy, adversarial AI defense, behavioral economics, and multi-agent reinforcement learning. Current dispatching systems suffer from fragmentation — they optimize for traffic flow while ignoring hardware constraints or cybersecurity threats. By utilizing a Multi-Agent Reinforcement Learning (MARL) engine cross-verified against chemistry-aware battery models and lightweight adversarial filters, the proposed system demonstrates a 15–18% reduction in deadheading and a 20% extension in electric vehicle (EV) fleet longevity. Furthermore, the implementation of a federated edge architecture ensures 100% privacy compliance with minimal computational latency, providing a secure and sustainable foundation for next-generation smart city mobility. This research uniquely bridges the gap between academic AI theory and real-world operational constraints by proposing a framework that is simultaneously technically rigorous, privacy-preserving, and economically viable. The results indicate that UMAF outperforms existing state-of-the-art dispatch systems across all evaluated performance metrics.
Citations
IRE Journals:
Hemanth Kumar H "UMAF: Unified Multi-Agent Framework for Real-Time Public Transport Dispatching" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2941-2951 https://doi.org/10.64388/IREV9I11-1718018
IEEE:
Hemanth Kumar H
"UMAF: Unified Multi-Agent Framework for Real-Time Public Transport Dispatching" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1718018
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