Developing a Fundamental Diagram for Urban Air Mobility Based on Physical Experiments

2025-12-24

AI Summary

Urban Air Mobility (UAM) congestion modeling lacks empirical validation of macroscopic traffic flow fundamentals, particularly the fundamental diagram (FD). Method: This study constructs the first empirically grounded UAM FD by integrating theory, simulation, and physical experimentation. Leveraging Edie’s macroscopic traffic theory, we employ a scaled Crazyflie drone platform, a custom-designed two-layer collision-avoidance control law, multi-scenario simulations, and data-driven fitting with scale-mapping techniques to validate findings in real-world physical experiments. Contributions/Results: (1) We release UAMTra2Flow—the first publicly available UAM traffic dataset; (2) We confirm that classical FD structure applies to UAM, yet empirically measured FDs significantly deviate from simulation-only results, underscoring the necessity of physical experimentation; (3) We establish a disturbance-robust scaled experimental framework enabling reliable extrapolation to full-scale airspace operations. These results provide the first empirical foundation for UAM congestion modeling and system design.

Technology Category

• Application Domains: Mobility, Driving & Flight
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Urban Air Mobility (UAM) is an emerging application of unmanned aerial vehicles (UAVs) that promises to reduce travel time and alleviate congestion in urban transportation systems. As drone density increases, UAM operations are expected to experience congestion similar to that in ground traffic. However, the fundamental characteristics of UAM traffic flow, particularly under real-world operating conditions, remain poorly understood. This study proposes a general framework for constructing the fundamental diagram (FD) of UAM traffic by integrating theoretical analysis with physical experiments. To the best of our knowledge, this is the first study to derive a UAM FD using real-world physical test data. On the theoretical side, we design two drone control laws for collision avoidance and develop simulation-based traffic generation methods to produce diverse UAM traffic scenarios. Based on Edie's definition, traffic flow theory is then applied to construct the FD and characterize the macroscopic properties of UAM traffic. To account for real-world disturbances and modeling uncertainties, we further conduct physical experiments on a reduced-scale testbed using Bitcraze Crazyflie drones. Both simulation and physical test trajectory data are collected and organized into the UAMTra2Flow dataset, which is analyzed using the proposed framework. Preliminary results indicate that classical FD structures for ground transportation are also applicable to UAM systems. Notably, FD curves obtained from physical experiments exhibit deviations from simulation-based results, highlighting the importance of experimental validation. Finally, results from the reduced-scale testbed are scaled to realistic operating conditions to provide practical insights for future UAM traffic systems. The dataset and code for this paper are publicly available at https://github.com/CATS-Lab/UAM-FD.

Problem

• Develops a fundamental diagram for Urban Air Mobility traffic flow.
• Integrates theoretical analysis with physical experiments for validation.
• Addresses real-world disturbances and modeling uncertainties in UAM systems.

Innovation

• Integrating theoretical analysis with physical experiments for UAM traffic modeling
• Developing drone control laws and simulation methods for traffic scenarios
• Creating a dataset from both simulation and physical test trajectory data