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.

翻译：城市空中交通（UAM）作为无人机（UAV）的新兴应用领域，有望缩短出行时间并缓解城市交通系统的拥堵问题。随着无人机密度的增加，UAM运行预计将出现类似地面交通的拥堵现象。然而，人们对UAM交通流的基本特性，尤其是在实际运行条件下的特性，仍知之甚少。本研究提出了一个通过理论分析与物理实验相结合来构建UAM交通基本图（FD）的通用框架。据我们所知，这是首个利用真实物理测试数据推导UAM FD的研究。在理论方面，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产生多样化的UAM交通场景。基于Edie的定义，应用交通流理论构建FD并表征UAM交通的宏观特性。为了考虑实际干扰和建模不确定性，我们进一步使用Bitcraze Crazyflie无人机在缩比测试平台上进行了物理实验。收集了仿真和物理测试的轨迹数据，并将其整理为UAMTra2Flow数据集，使用所提出的框架进行分析。初步结果表明，适用于地面交通的经典FD结构同样适用于UAM系统。值得注意的是，从物理实验获得的FD曲线与基于仿真的结果存在偏差，这凸显了实验验证的重要性。最后，将缩比测试平台的结果按比例推演至实际运行条件，为未来UAM交通系统提供了实用见解。本文的数据集和代码已在https://github.com/CATS-Lab/UAM-FD公开。