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

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). The simulation captures multi-modal transportation behaviors across multiple vertical levels and varying density scenarios, using both synthetic data and real-world traces from high-density cities. Experimental results show that the fully integrated AI architecture enables agents to achieve an average commute time of 7.8-8.4 minutes, a satisfaction rate exceeding 89%, and a reachability index above 91%, even during peak congestion periods. Ablation studies indicate that removing intelligent modules such as RL or GNNs significantly degrades performance, with commute times increasing by up to 85% and reachability dropping below 70%. Baseline comparisons against Dijkstra, A*, DQN, and standard GCN further confirm the superiority of the proposed model across all mobility and sustainability metrics. Environmental modeling demonstrates low energy consumption and minimal CO2 emissions when electric transportation modes are prioritized. These findings suggest that efficient and sustainable mobility in extreme urban environments is achievable, provided that adaptive AI systems, intelligent infrastructure, and real-time feedback mechanisms are effectively implemented.