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 that integrates 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. Experiments show that the full AI-integrated architecture enables agents to achieve an average commute time of 7.8--8.4 minutes, a satisfaction rate exceeding 89\%, and a reachability index over 91\%, even during peak congestion periods. Ablation studies indicate that removing intelligent modules such as RL or GNN significantly degrades performance, with commute times increasing by up to 85\% and reachability falling below 70\%. Environmental modeling demonstrates low energy consumption and minimal CO$_2$ emissions when electric modes are prioritized. These results suggest that efficient and sustainable mobility in extreme urban forms is achievable, provided adaptive AI systems, intelligent infrastructure, and real-time feedback mechanisms are implemented.

翻译：本文研究了在极端城市形态下人类移动性的可行性，此类形态以高密度垂直结构和线性城市布局为特征。为评估智能体能否在此类前所未有的拓扑结构中高效导航，我们开发了一个混合仿真框架，该框架集成了基于代理的建模、强化学习、监督学习和图神经网络。该仿真利用合成数据和高密度城市的真实轨迹，捕捉了跨多个垂直层级及不同密度场景下的多模式交通行为。实验表明，即使在高峰拥堵期，完全集成人工智能的架构也能使智能体实现平均通勤时间7.8至8.4分钟、满意度超过89%、可达性指数高于91%的表现。消融研究表明，移除强化学习或图神经网络等智能模块会显著降低性能，通勤时间最多增加85%，可达性降至70%以下。环境建模显示，当优先采用电动模式时，能耗和二氧化碳排放量均处于较低水平。这些结果表明，在极端城市形态中实现高效且可持续的移动性是可能的，前提是部署自适应人工智能系统、智能基础设施及实时反馈机制。