Conflict-Free Flight Scheduling Using Strategic Demand Capacity Balancing for Urban Air Mobility Operations

TL;DR

This work tackles conflict-free, efficient scheduling for Urban Air Mobility (UAM) in congested airspace by introducing Pairwise Conflict Avoidance (PCA) based on strategic demand capacity balancing and ground delays. It derives a closed-form safe departure delay $t_d$ for pairwise conflicts and extends PCA to multi-agent scheduling via an optimization that minimizes $\sum t_i^d$ under a minimum separation constraint $|\vec{P}_{F_i}(t+t_i^d)-\vec{P}_{F_j}(t+t_j^d)|\ge h$. The approach is validated in a reduced 20$\times$20 m simulation and a Greater Atlanta metro-area case study, showing substantial reductions in total delays while maintaining safety as traffic density grows. The results indicate that higher density increases average delays and variance, but optimal sequencing yields meaningful improvements (e.g., total delay around $20.68$ minutes and average delay around $5.17$ minutes in the Atlanta case). This work provides a scalable framework and evaluation metrics for future autonomous ATM systems in UAM, enabling conflict-free operations with practical delay benefits.

Abstract

In this paper, we propose a conflict-free multi- agent flight scheduling that ensures robust separation in con- strained airspace for Urban Air Mobility (UAM) operations application. First, we introduce Pairwise Conflict Avoidance (PCA) based on delayed departures, leveraging kinematic principles to maintain safe distances. Next, we expand PCA to multi-agent scenarios, formulating an optimization approach that systematically determines departure times under increasing traffic densities. Performance metrics, such as average delay, assess the effectiveness of our solution. Through numerical simulations across diverse multi-agent environments and real- world UAM use cases, our method demonstrates a significant reduction in total delay while ensuring collision-free operations. This approach provides a scalable framework for emerging urban air mobility systems.

Figures (6)

• Figure 1: Agent A starts $t_d$ units before Agent B. At Agent B's start, A has followed the blue path. The red dashed line shows $R(t)$, which must stay above the minimum separation $h$.
• Figure 2: The two roots of Eq. \ref{['eqn:eq007']} represent two cases: a longer delay lets Agent A reach A2 (blue) before being overtaken; a shorter delay causes Agent B to catch A at A1 (red).
• Figure 3: In the general case with $N = 7$ agents, there are $N!$ possible departure orders. Managing conflict-free timings and optimizing the sequence to minimize delays are key challenges.
• Figure 4: Gray histograms show average delays for different ATDs. The red curve is the best-fit Gamma distribution with the lowest SSR.
• Figure 5: Gamma distributions show that both average delay and standard deviation increase with ATD, highlighting greater sensitivity to departure timing at higher densities.