Urban Air Mobility (UAM) is an emerging application of unmanned aerial vehicles that promises to reduce travel time and alleviate congestion in urban transportation systems. As drone density increases, UAM traffic is expected to experience congestion similar to that in ground traffic. However, the fundamental characteristics of UAM traffic, particularly under real-world operating conditions, remain largely unexplored. 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 UAM FDs using real-world physical experiment 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 with a near-stationary traffic condition filtering method to construct the FD. 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 experiment 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, especially the Underwood model, are 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.

翻译：城市空中交通（UAM）作为无人驾驶航空器的新兴应用，有望缩短出行时间并缓解城市交通系统的拥堵问题。随着无人机密度增加，UAM交通预计将出现类似地面交通的拥堵现象。然而，UAM交通的基本特性，尤其是在实际运行条件下的表现，目前仍未得到充分探索。本研究提出一个通用框架，通过理论分析与物理实验相结合的方式构建UAM交通基本图。据我们所知，这是首个利用真实物理实验数据推导UAM基本图的研究。在理论层面，我们设计了两种用于防撞的无人机控制律，并开发了基于仿真的交通生成方法以构建多样化的UAM交通场景。基于Edie的定义，我们应用交通流理论并结合近稳态交通条件过滤方法来构建基本图。为考虑实际干扰和建模不确定性，我们进一步使用Bitcraze Crazyflie无人机在缩比实验平台上开展物理实验。仿真与物理实验的轨迹数据被收集整理为UAMTra2Flow数据集，并采用所提框架进行分析。初步结果表明，地面交通的经典基本图结构（特别是Underwood模型）适用于UAM系统。值得注意的是，物理实验获得的基本图曲线与仿真结果存在偏差，这凸显了实验验证的重要性。最后，我们将缩比实验平台的结果扩展至实际运行条件，为未来UAM交通系统提供实用参考。本文数据集与代码已公开于https://github.com/CATS-Lab/UAM-FD。