An intelligent agent-based simulation of human mobility in extreme urban morphologies

Abstract

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.