Dynamic Capacity Balancing in Urban Airspace: Comparing Historical and Real-time Aggregate Flow Data

As urban ground transportation congestion increases, there is growing interest in urban air transportation, such as delivery drones and air taxis. However, managing air traffic in densely populated urban areas poses significant challenges, which require effective flight planning, separation management, and airspace design. This paper investigates dynamic capacity balancing methods to manage air traffic in constrained urban airspace, where drones must fly above the existing road network. Specifically, it compares the effectiveness of labelling high-complexity zones using historical data versus real-time aggregate flow data. The results indicate that while both approaches reduce airspace intrusions and improve safety, the best approach depends on traffic demand levels. At lower demand levels, using historical data yields better safety outcomes, whereas using real-time data is more effective at higher demand levels due to its flexibility. At their best, both methods increase the travel distance by less than 6% while reducing airspace intrusions by 30% compared to a case without dynamic capacity balancing.