Air Traffic Management Initiatives Research Articles

Recommending Strategic Air Traffic Management Initiatives in Convective Weather

The presence of uncertainty in weather forecasts poses significant challenges for air traffic managers. These challenges can have major repercussions on stakeholders in terms of their impact on the delay within the system. In this paper, we discuss an approach for recommending traffic management initiative (TMI) parameters during uncertain weather conditions. We propose four methods for TMI selection. The first two favor random exploration of TMI decisions. An epsilon-greedy approach and a softmax algorithm are also evaluated against the two random exploration approaches. A parallel fast-time simulation framework is presented for evaluating the proposed methods over a range of weather forecast scenarios. A set of regional TMIs is applied and tested against a set of case days in which the airspace capacity in the Northeast United States was compromised by convective weather. Both the softmax and epsilon-greedy approaches demonstrate strong performance relative to the other methods. The results suggest that the approach could potentially aid air traffic stakeholders in understanding how to best deal with weather forecast uncertainty.

Unsupervised prototype reduction for data exploration and an application to air traffic management initiatives

We discuss a new approach to unsupervised learning and data exploration that involves summarizing a large data set using a small set of “representative” elements. These representatives may be presented to a user in order to provide intuition regarding the distribution of observations. Alternatively, these representatives can be used as cases for more detailed analysis. We call the problem of selecting the representatives the unsupervised prototype reduction problem. We discuss the KC-UPR method for this problem and compare it to other existing methods that may be applied to this problem. We propose a new type of distance measure that allows for more interpretable presentation of results from the KC-UPR method. We demonstrate how solutions from the unsupervised prototype reduction problem may be used to provide decision support for the planning of air traffic management initiatives, and we produce computational results that compare the effectiveness of several methods in this application. We also provide an example of how the KC-UPR method can be used for data exploration, using data from air traffic management initiatives at Newark Liberty International Airport.

Unsupervised Prototype Reduction for Data Exploration and An Application to Air Traffic Management Initiatives

We discuss a new approach to unsupervised learning and data exploration that involves summarizing a large data set using a small set of ``representative'' elements. These representatives may be presented to a user in order to provide intuition regarding the distribution of observations. Alternatively, these representatives can be used as cases for more detailed analysis. We call the problem of selecting the representatives the Unsupervised Prototype Reduction problem. We discuss the KC-UPR method for this problem and compare it to other existing methods that may be applied to this problem. We propose a new type of distance measure that allows for more interpretable presentation of results from the KC-UPR method. We demonstrate how solutions from the Unsupervised Prototype Reduction problem may be used to provide decision support for the planning of air traffic management initiatives, and we produce computational results that compare the effectiveness of several methods in this application. We also provide an example of how the KC-UPR method can be used for data exploration, using data from air traffic management initiatives at Newark Liberty International Airport.

Robust Aircraft Routing

We propose a robust optimization approach to minimize total propagated delay in the aircraft routing problem. Instead of minimizing the expected total propagated delay by assuming that flight leg delays follow specific probability distributions, our model minimizes the maximal possible total propagated delay when flight leg delays lie in a prespecified uncertainty set. We develop exact and tractable solution approaches for our robust model. The major contribution of our model is that it allows us to explicitly model and handle correlation in flight leg delays (e.g., because of weather or various air traffic management initiatives) that existing approaches cannot efficiently incorporate. Using both historical delay data and simulated data, we evaluate the performance of our model and benchmark against the state-of-the-research stochastic approach. In most of the cases, we observe that our model outperforms the existing approach in lowering the mean, reducing volatility, and mitigating extreme values of total propagated delay. In the cases where a deficit in one of the three criteria exists, gains in the other two criteria usually offset this disadvantage. These results suggest that robust optimization approaches can provide promising results for the aircraft routing problem. The online appendix is available at https://doi.org/10.1287/trsc.2015.0657 .

Airline-Driven Performance-Based Air Traffic Management: Game Theoretic Models and Multicriteria Evaluation

Defining air traffic management as the tools, procedures, and systems employed to ensure safe and efficient operation of air transportation systems, an important objective of future air traffic management systems is to support airline business objectives, subject to ensuring safety and security. Under the current model for designing air traffic management initiatives, the central authority overseeing and regulating air traffic management in a region makes trade-offs between specified performance criteria. The research presented in this paper aims instead to allow the airline community to set performance goals and thus make trade-offs between different performance criteria directly, before specific air traffic management strategies are determined. We propose several approaches for collecting inputs from airlines in a systematic way and for combining these airline inputs into implementable air traffic management initiatives. These include variants of averaging, voting, and ranking mechanisms. We also propose multiple criteria for evaluating the effectiveness of each approach, including Pareto optimality, airline profitability, system optimality, equity, and truthfulness of airline inputs. We apply a game-theoretic approach to examine the potential for strategic (gaming) behavior by airlines. We offer a broad evaluation of each approach, first by providing some theoretical insights, and then by simulating each of the approaches for a generic system using Monte Carlo methods, sampling values for input parameters from a wide range. We also provide an indication of how the approaches might perform in a real system by simulating ground delay programs at two airports in the New York City area. We first apply a simplified model that simulates the process of selecting only planned end times of a ground delay program, using Monte Carlo methods. Next, we apply a more detailed model that simulates the process of selecting planned end times and reduced airport arrival rates. Finally, we characterize the effectiveness of each of the considered approaches on the proposed criteria and identify the most desirable approaches. We conclude that voting schemes, which score highly on all criteria (including airline profitability, system optimality, and equity), represent the most promising approaches (among those considered) to elicit airline preferences, thereby allowing the central authority to design air traffic management initiatives that optimize system performance while respecting the objectives of airlines.

Robust Aircraft Routing

We propose a robust optimization approach to minimize total propagated delay in the aircraft routing problem, a setting first developed by Lan et al. (2006) and then extended by Dunbar et al. (2012, 2014). Instead of minimizing the expected total propagated delay by assuming that flight leg delays follow specific probability distributions, our model minimizes the maximal possible total propagated delay when flight leg delays lie in a pre-specified uncertainty set. We develop exact and tractable solution approaches for our robust model. The major contribution of our model is that it allows us to explicitly model and handle correlation in flight leg delays (e.g., due to weather or various air traffic management initiatives) that existing approaches cannot efficiently incorporate. Using both historical delay data and simulated data, we evaluate the performance of our model and benchmark against the state-of-the-research stochastic approach (Dunbar et al. 2014). In most of the cases, we observe that our model outperforms the existing approach in lowering the mean, reducing volatility, and mitigating extreme values of total propagated delay. In the cases where a deficit in one of the three criteria exists, gains in the other two criteria usually offset this disadvantage. These results suggest that robust optimization approaches can provide promising results for the aircraft routing problem.

Agent-Based Modeling and Simulation of Collaborative Air Traffic Flow Management using Brahms

The air traffic demand on the US national airspace frequently exceeds its available capacity. In current operations, the Air Traffic Service Provider designs and implements air traffic management initiatives with minimal interaction with the airlines. NASA and its partners have developed a new collaborative air traffic flow management concept of operations that involves the users of the airspace to a greater degree. In this paper, we describe an agent-based simulation of the new concept of operations and our planned experimentation to determine if the new concept of operations will lead to better utilization of the national airspace.