Emerging Urban Air Mobility (UAM) operations are expected to introduce novel air traffic networks in metropolitan areas in order to provide on-demand air transportation services and alleviate ground congestion. Yet, metropolitan regions are typically characterized by complex and dense terminal airspace structure that accommodates arrival and departure traffic from large metroplex airports. Therefore, UAM operations are expected to be initially integrated into urban airspace without interfering with conventional operations and compromising current safety and efficiency levels. This paper presents a data-driven approach to identify and predict available urban airspace that is procedurally separated from conventional air traffic towards supporting UAM integration. We use historical aircraft tracking and meteorological data to learn the spatial distribution of air traffic in the terminal airspace and create a probabilistic traffic model to predict active traffic patterns and their spatial confidence regions given current operational conditions. We demonstrate the approach for the city of Sao Paulo and its closest commercial airport, Congonhas (CGH), in Brazil. The results show that leveraging the traffic flow dynamics to allocate the urban airspace dynamically is beneficial to increase UAM accessibility by more than 5% from 3000 ft. Moreover, airspace availability is found to be highly sensitive to the applied separation requirements, emphasizing the importance of leveraging advanced technologies to progressively make such requirements less stringent.

新兴的城市空中交通 (UAM) 运营预计将在大都市区引入新型空中交通网络，以提供按需空中运输服务并缓解地面拥堵。然而，大都市地区的典型特征是复杂而密集的终端空域结构，可容纳来自大型都市机场的到达和离开交通。因此，预计 UAM 运行最初将被整合到城市空域中，而不会干扰常规运行并影响当前的安全和效率水平。本文提出了一种数据驱动的方法来识别和预测在程序上与传统空中交通分离的可用城市空域，以支持 UAM 集成。我们使用历史飞机跟踪和气象数据来了解终端空域中空中交通的空间分布，并创建一个概率交通模型来预测当前运营条件下的活跃交通模式及其空间置信度区域。我们展示了巴西圣保罗市及其最近的商业机场孔戈尼亚斯 (CGH) 的方法。结果表明，利用交通流动态动态分配城市空域有利于将 UAM 可达性从 3000 英尺提高 5% 以上。此外，发现空域可用性对应用的间隔要求高度敏感，强调了利用先进技术逐步降低此类要求。