Real-Time Intelligent Landing-Management Under Urban Unpredictable Operations

The integration of electric Vertical Takeoff and Landing (eVTOL) vehicles into urban transportation presents challenges in scalability, real-time adaptability, and operational efficiency. This study introduces an Agent-Based Modeling (ABM) framework for traffic management, dynamically assigning eVTOLs to landing pads based on real-time data to enhance efficiency in congested urban environments. A comparative analysis is conducted across various scheduling and sequencing approaches, including Mixed-Integer Linear Programming (MILP), Time-Advance (TA), heuristic methods, receding horizon scheduling, reinforcement learning (RL)-based frameworks, and decentralized agent-based strategies. While MILP and TA offer structured scheduling, they struggle with scalability. Heuristic and receding horizon methods improve adaptability but require frequent recomputation, and RL-based approaches show promise but demand extensive training. Current Decentralized models support distributed decision-making but face efficiency constraints at scale. The proposed ABM framework effectively manages 200 eVTOLs with near-linear computational scaling, facilitating real-time negotiations and reducing computational bottlenecks seen in centralized models. Simulation results indicate improved assignment efficiency, landing pad utilization, and reduced negotiation times under high-density conditions. As UAM systems expand, ABM may contribute to operational resilience. Future work will explore integrating environmental factors to further enhance robustness.