NaviGNN: Multi-Agent Reinforcement Learning and Graph Neural Network for Sustainable Mobility in Futuristic Smart Cities

ArXi:2507.15143v3 Announce Type: replace This paper investigates the feasibility of human mobility in extreme urban morphologies characterized by high-density vertical structures and linear city layouts. To assess whether agents can navigate efficiently within such unprecedented topologies, we develop a hybrid simulation framework integrating agent-based modeling, reinforcement learning (RL), supervised learning, and graph neural networks (GNNs