Urban air mobility (UAM) has the potential to revolutionize our daily transportation, offering rapid and efficient deliveries of passengers and cargo between dedicated locations within and around the urban environment. Before the commercialization and adoption of this emerging transportation mode, however, aviation safety must be guaranteed, i.e., all the aircraft have to be safely separated by strategic and tactical deconfliction. Reinforcement learning has demonstrated effectiveness in the tactical deconfliction of en route commercial air traffic in simulation. However, its performance is found to be dependent on the traffic density. In this project, we propose a novel framework that combines demand capacity balancing (DCB) for strategic conflict management and reinforcement learning for tactical separation. By using DCB to precondition traffic to proper density levels, we show that reinforcement learning can achieve much better performance for tactical safety separation. Our results also indicate that this DCB preconditioning can allow target levels of safety to be met that are otherwise impossible. In addition, combining strategic DCB with reinforcement learning for tactical separation can meet these safety levels while achieving greater operational efficiency than alternative solutions.

As urban air mobility (UAM) promises to revolutionize urban transportation, air traffic management must evolve to accommodate increasing traffic density. Demand capacity balancing (DCB) strategically preconditions air traffic by analyzing capacity-constrained resources (CCRs) within structured airspace and determining optimal departure times to minimize conflicts and boost operational efficiency. However, uncertainties such as tactical maneuvers can disrupt pre-established plans by the DCB. To address this, we introduce a rolling-horizon DCB (RHDCB) approach, incorporating a learning-based ETA estimator to manage arrival time uncertainty. Our simulation results demonstrate that the RHDCB significantly reduces conflicts and enhances operational efficiency compared to single-round planning. Additionally, the decomposed mixed-integer linear programming (MILP) method ensures the approach meets real-time operational computational requirements.

This work outlines and applies a simulation driven methodology for safety analysis of Unmanned Traffic Management (UTM) systems that are likely to emerge in the upcoming years using cooperative strategic deconfliction as the primary mitigator of collision risk. It builds on past work that examined the implications of using volume based strategic deconfliction in UTM to mitigate collision risk between uncrewed aircraft using Monte Carlo methods in simulation. We apply this methodology to asses the sensitivities of the system in terms of safety relative to key performance characteristics of strategic deconfliction such as operational intent conformance rate and strategic deconfliction participation rate. Additionally, we examine the impact of key operational characteristics, such as operational density, on system safety. Furthermore, we simulate two different operational complexities - a low complexity operational profile of uniformly distributed out-and-back operations, and a high complexity operational profile of four different operational use cases - hub-and-spoke, long-linear-inspection, point-to-point, and out-and-back operations - distributed across the Denver metropolitan area according on features of the city and population density. The simulation results generated suggest that strategic deconfliction provides a significant safety benefit that is approximately constant with operational density, and applies under both the low complexity and high complexity operational profiles simulated. The simulated benefit is sensitive to participation rate, but not to the range of operational intent conformance rates simulated in this paper.

With the growth of Uncrewed Aircraft System (UAS) operations there is a clear need to ensure safe separation between traditional crewed aircraft and uncrewed traffic, while maximising the operational efficiency of both. Accordingly, the European Commission has specified requirements for dynamic airspace reconfiguration in its U-space regulation for facilitating UAS operations in Europe. In this paper we use Monte Carlo methods in simulation to explore the implications of using a Dynamic Airspace Reconfiguration (DAR) on UAS operations in the vicinity of an airport. Four scenarios are simulated varying the subset of operations that are modified to account for a DAR when it is announced - including new yet-to-file operations (strategic replanning), already authorized/accepted operations that have not taken off (pre-tactical replanning), and activated/airborne operations (tactical replanning). The results indicate that the number of incursions was lowest when operations were replanned across all time scales - strategically, pre-tactically and tactically, but that the cumulative delay and number of operations modified to accommodate the DAR was highest. The sensitivity of the results to the DAR notice period and DAR size were also explored, with the results suggesting that the notice period for the DAR should be maximized to reduce incursions, and to increase predictability and energy efficiency.

This paper demonstrates the use of simulation to quantify the safety and efficiency implications of airspace restrictions on Uncrewed Aircraft System (UAS) operations in U-space airspace. We simulate package delivery type operations across the city of Riga, Latvia with and without strategic deconfliction based on volumetric operational intent. Three scenarios were simulated across a range of demand levels: (1) a baseline in which no airspace restrictions are in place; (2) a scenario in which a small number of airspace restrictions are in place associated with air risk; and (3) a scenario in which a larger number of airspace restrictions are in place associated with both air risk and ground risk. The simulation results suggest that the probability of mid air collisions (MAC) increases with increasingly complex airspace restrictions, but that the effectiveness of strategic deconfliction based on volumetric intent to ensure operational safety by reducing the probability of MAC remains approximately constant. Results are also presented for average throughput and delay, which suggest that, when applying strategic deconfliction, operational restrictions limit throughput and lead to increases in delay - at all the demand levels simulated. This paper demonstrates the value of simulation to support airspace assessment, which is required by European Member States in order to meet the requirements of the European U-space regulation.

View Video Presentation: https://doi.org/10.2514/6.2022-3317.vid Demand capacity balancing (DCB) has been proposed as a strategic mechanism to balance efficiency and predictability for Urban Air Mobility (UAM) operations when operational uncertainties are high. In this paper, we seek to determine how DCB can be implemented to ensure safe and efficient UAM operations in coordination with a tactical deconfliction system. We use simulations to explore the safety of representative UAM operations applying tactical deconfliction methods, and estimate airspace resource capacities that could be applied, using DCB, to ensure safe and efficient UAM operations. We apply this approach to determine airspace capacity in two baseline route structures - simulating merging flows and crossing flows respectively - which are then applied to a hypothetical UAM network in New York City. The benefits of DCB to support tactical deconfliction for safety assurance across a range of demand values is demonstrated and compared to safety metrics applying tactical deconfliction only, and to a baseline without DCB or tactical deconfliction. In addition, we also show the trade-off between ground delay and safety metrics while implementing various levels of DCB. The results suggest that DCB may be a feasible mechanism to ensure safe and efficient UAM operations. The results also reveal some early insights into interactions between the strategic DCB and the tactical deconfliction.

This paper provides an initial analysis of the ability of volume based deconfliction to mitigate air risk between cooperative unmanned operations in an Unmanned Traffic Management (UTM) setting. Namely, we use high-fidelity simulation in combination with a collection of UTM services to evaluate the functional and performance requirements for strategic deconfliction that are emerging from the standards work in UTM. Our objective is to assess how well the requirements developed by standards groups can support end-to-end safety. We consider two key aspects of strategic deconfliction within our evaluation: how operational volumes are constructed and how well unmanned vehicles are able to conform to their planned operational volumes in the presence of system error. To that end, we outline an end-to-end simulation framework that can be used to evaluate system level implications of UTM requirements. We apply the framework to (1) provide quantitative guidance for the risk reduction associated with strategic deconfliction in UTM, and to (2) provide operational recommendations that would enable operators to meet safety targets prescribed by conformance rate and strategic deconfliction requirements in the UTM ecosystem. Keywords—UAS; UTM; safety; separation; deconfliction

The increased use of drones and air-taxis is expected to make airspace resources more congested, necessitating the use of unmanned aircraft systems traffic management (UTM) initiatives to ensure safe and efficient operations. Typically, strategic UTM involves solving an optimization problem that ensures that proposed flight schedules do not exceed airspace and vertiport capacities. However, the dynamic nature and low lead-time of applications such as on-demand delivery and urban air mobility traffic may reduce the efficiency and fairness of strategic UTM. We first discuss the adaptation of three fairness metrics into a traffic flow management problem (TFMP). Then, with computational simulations of a drone package delivery scenario in Toulouse, we evaluate trade-offs in the TFMP between efficiency and fairness, as well as between different fairness metrics. We show that system fairness can be improved with little loss in efficiency. We also consider two approaches to the integrated scheduling of both high lead-time flights (i.e., flights with a schedule known in advance) and low lead-time flights in a rolling horizon optimization framework. We compare the performance of both approaches for different horizon lengths and under varying proportions of high and low lead-time flights.

Strategic deconfliction is a key mechanism for achieving separation between Urban Air Mobility (UAM) operations. However, operational uncertainties may degrade its effectiveness. In this paper, we quantify the effectiveness of strategic deconfliction in mitigating scheduled and unscheduled flight delays under operational uncertainty in the form of normally distributed departure and airborne errors. A range of demand levels representing early-stage UAM operations were simulated across a conceptual network of 3 vertiports in the San Francisco Bay Area. Three approaches to strategic deconfliction were simulated which varied the requirement for rescheduling into the existing schedule when an operation incurred departure or airborne error – a tight conformance requirement tied to the minimum spacing requirement; a relaxed conformance requirement comparable to that used for internal departure scheduling in Time-Based Flow Management; and no conformance requirement in which operations were never rescheduled into the existing scheduled – only replanned tactically. Results from these simulations were compared to a baseline that simulated tactical deconfliction without strategic deconfliction. Results suggest that departure and airborne delays under strategic deconfliction are highly sensitive to how much rescheduling is required into the existing schedule. Results applying strategic deconfliction with no conformance requirement, with departure and airborne error being accommodated tactically, showed significantly improved performance – even at relatively high demand and error variability. Future work should explore the safety and gaming implications of strategic deconfliction with such relaxed conformance requirements and compare its performance to using demand capacity balancing instead of strategic deconfliction.

As the demand for Unmanned Aircraft Systems (UAS) operations increases, UAS Traffic Flow Management (UTFM) initiatives are needed to mitigate congestion, and to ensure safety and efficiency. Congestion mitigation can be achieved by assigning airborne delays (through speed changes or path stretches) or ground delays (holds relative to the desired takeoff times) to aircraft. While the assignment of such delays may increase system efficiency, individual aircraft operators may be unfairly impacted. Dynamic traffic demand, variability in aircraft operator preferences, and differences in the market share of operators complicate the issue of fairness in UTFM. Our work considers the fairness of delay assignment in the context of UTFM. To this end, we formulate the UTFM problem with fairness and show through computational experiments that significant improvements in fairness can be attained at little cost to system efficiency. We demonstrate that when operators are not aligned in how they perceive or value fairness, there is a decrease in the overall fairness of the solution. We find that fairness decreases as the air-ground delay cost ratio increases and that it improves when the operator with dominant market share has a weak preference for the fairness of its allocated delays. Finally, we implemented UTFM in a rolling-horizon setting with dynamic traffic demand, and find that efficiency is adversely impacted. However, the impact on fairness is varied and depends on the metric used.

Current air traffic management processes lack integration among elements of the airspace system such as airport and airspace resources, often due to disconnected and inconsistent use of information, models, and metrics. This integration is hindered further by the separation between the traffic management problem, which has focused on a gate-to-gate flight model, and the management of connecting vehicles, crews, and passengers between flights. To help induce such integration, an accrued delay metric is used to continuously measure the delay that a flight has accumulated during the flight and inherited from previous flights through the turnaround at airports. The authors presented previously an application of this metric to mitigate the delay propagation between management initiatives during a flight. In this paper, the authors extend this metric to mitigate delay propagation through vehicle turnaround. It is first shown through a historical data analysis that this delay propagation is a significant source of delay for airline operators and that schedule padding hides some of it. Then, accrued delay along a vehicle trip is shown to mitigate some of this propagation across the network. Through an abstract, closed, three-node network example, using accrued delay is shown to help implicitly coordinate the distributed scheduling among the nodes. These nodes may represent a set of airports or a set of vertiports in an urban air mobility environment. It is shown that prioritizing flights that have accrued high delay at previous nodes significantly reduces the variance of flight delay.

There is an increasing interest in applyingmethods based onMachine Learning Techniques (MLT) to problems in Air Traffic Management(ATM). The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large databases. This paper reviews the current-state-of-the art in applying MLT to aviation operations, its promises and challenges. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The promises and challenges in applying MLT to ATM is traced through three examples based on the authors’ experience, each separated by a decade, to show the influence of data and feature selection in the successful application of MLT to ATM. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

The emergence of new operations, such as package delivery and air taxis, that could co-exist in the current airspace has been a topic of great interest in recent years. A big challenge associated with the introduction of these new operations is ensuring that the complex system-of-systems responsible for these operations known as Unmanned Traffic Management (UTM) meet the high safety standards of aviation. In this work, we present a general purpose framework for validating decision making systems, protocols, and algorithms that may exist in the UTM ecosystem. We propose a novel, simulation driven approach for validation in UTM that can automatically discover the failure modes of a decision making system, and optimize the parameters that configure the system to improve its performance. We apply this approach to Urban Air Mobility (UAM) and package delivery use cases, and examine in detail the failure modes and the potential improvements of two UTM services critical for strategic deconfliction: trajectory planning and optimal scheduling. Using simulation, we demonstrate that our algorithm is able to discover failure modes in the system that would be challenging for humans to find and fix, and we show how the algorithm can learn from these failure modes to improve the performance of the UTM services in question. We also demonstrate the significant sample efficiency improvements that our algorithm has over naive stress testing approaches that rely on uniform sampling.

Effective Remote Identification of unmanned aircraft is critical to their integration into civil airspaces. This paper assesses the ability of proposed unlicensed technologies (Bluetooth and WiFi) to support Remote Identification, and also creates a framework for modeling communication performance for unmanned aircraft, both at scale. Through simulation, we show that most currently commercially available Bluetooth and WiFi implementations would require significant ground antenna support in order to be able to avoid saturation situations at even low demand rates. We also show the flexibility of the simulation framework to study regional coverage and the effect of tuning different parameters on performance.

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Benefits of the Integrated Demand Management (IDM) concept were assessed utilizing a newly developed automated simulation capability called ‘Traffic Management Initiative Automated Simulation (TMIAutoSim).’ The IDM concept focuses on improving traffic flow management (TFM) by coordinating the FAA’s strategic Traffic Flow Management System (TFMS) with its more tactical Time-Based Flow Management (TBFM) system. The IDM concept leverages a new TFMS capability called Collaborative Trajectory Options Program (CTOP) to strategically pre-condition traffic demand flowing into a TBFM-managed arrival environment, where TBFM is responsible for tactically managing traffic by generating precise arrival schedules. The IDM concept was developed over a multi-year effort, focusing on solving New York metroplex airport arrival problems. TMIAutoSim closely mimics NASA’s high-fidelity simulation capabilities while enabling more data to be collected at higher speed. Using this new capability, the IDM concept was evaluated using realistic traffic across various weather scenarios. Six representative weather days were selected after clustering three-months of historical data. For those selected six days, Newark Liberty International Airport (EWR) and LaGuardia Airport (LGA) arrival traffic scenarios were developed. For each selected day, the historical data were analyzed to accurately simulate actual operations and the weather impact of the day. The current day operations and the IDM concept operations were simulated for the same weather scenarios and the results were compared. The selected six days were categorized into two groups: ‘clear weather’ for days without Ground Delay Programs (GDP) and ‘convective weather’ for days with GDP and significant weather around New York metroplex airports. For the clear weather scenarios, IDM operations reduced last-minute, unanticipated departure delays for short-haul flights within TBFM control boundaries with minimal to no impact on throughput and total delay. For the convective weather scenarios, IDM significantly reduced delays and increased throughput to the destination airports.

The air traffic management system lacks integration among its elements often due to using inconsistent information, models, and metrics about the traffic. Transitioning to trajectory-based operations, whereby flights are managed by full trajectories in space and time, will enable more integration, with the help of increased automation. Building on trajectory-based operations, an metric is proposed, which continuously measures the amount of delay that a flight has accumulated up to the current time, including delays incurred during the current flight and inherited from previous flights through the turnaround process. Through a time-based metering and scheduling example, we show how using accrued delay as a metric can help integrate the decision-making across multiple decision horizons, leading to more efficient and balanced access to airspace services. We show that when prioritizing flights that have already accrued high delay because of a constrained runway resource, significant gains are achieved in terms of reducing total delay and its variance. We studied the sensitivity of these gains to numerous factors, such as time-based versus distance-based horizons, horizon size, and errors in conformance to scheduled times.

Flight delays occur when demand for capacity-constrained airspace or airports exceeds predicted capacity. Demand for capacity-constrained airspace or airports can be controlled by a series of Traffic Management Initiatives (TMIs), which use departure and airborne delays, as well as pre-departure and airborne reroutes, to manage access to the constrained resources. Two systems exist in current and planned future operations to address imbalances between demand and capacity. The Collaborative Trajectory Options Program (CTOP) reduces demand to constrained resources by assigning strategic departure delay and pre-departure reroutes. Reroutes are selected from Trajectory Options Sets (TOSs) submitted by airlines. As flights approach the constrained resource, the Time-Based Flow Management System (TBFM) is used to assign tactical delay to satisfy constraints. This paper describes experiments performed to study the impact of varying levels of airline participation in CTOP via submission of TOSs on ground delay and flight time, and the impact of departure uncertainty on TBFM delays. Results suggest that as CTOP participation increases, average ground delays decrease for all airlines, but to the greatest extent for airlines participating in CTOP. A threshold in CTOP participation, which varies with the constraint capacity, is identified beyond which there is relatively little further reduction in average ground delays. Similarly, given the likely level of CTOP participation, the capacity reduction for which CTOP would be an appropriate TMI is also identified. Results also suggest that high average departure errors and high variability in departure error can make the prioritization of TBFM internal departures in TBFM metering and scheduling infeasible. Departure errors at current levels are, however, acceptable.

For tools that generate more efficient flight routes or reroute advisories, it is important to ensure compatibility of automation and autonomy decisions with human objectives so as to ensure acceptability by the human operators. In this paper, the authors developed a proof of concept predictor of operational acceptability for route changes during a flight. Such a capability could have applications in automation tools that identify more efficient routes around airspace impacted by weather or congestion and that better meet airline preferences. The predictor is based on applying data mining techniques, including logistic regression, a decision tree, a support vector machine, a random forest and Adaptive Boost, to historical flight plan amendment data reported during operations and field experiments. Cross validation was used for model development, while nested cross validation was used to validate the models. The model found to have the best performance in predicting air traffic controller acceptance or rejection of a route change, using the available data from Fort Worth Air Traffic Control Center and its adjacent Centers, was the random forest, with an F-score of 0.77. This result indicates that the operational acceptance of reroute requests does indeed have some level of predictability, and that, with suitable data, models can be trained to predict the operational acceptability of reroute requests. Such models may ultimately be used to inform route selection by decision support tools, contributing to the development of increasingly autonomous systems that are capable of routing aircraft with less human input than is currently the case.