The air transportation system in the United States is one of the most complex systems in the world. Projections of increasing air traffic demand in conjunction with limited capacity, that is volatile and affected by exogenous random events, represent a major problem in aviation system management.
From a management perspective, it is essential to make efficient use of the available resources and to create mechanisms that will help alleviate the problems of the imbalance between demand and capacity.
Air traffic delays are always present and the more air traffic increases the more the delays will increase with very unwanted economic impacts. It is of great interest to study them further in order to be able to more effectively mitigate them.
A first step would be to try to predict them under various circumstances. A second step would be to develop various mechanisms that will help in reducing delays in different settings. The scope of this dissertation is to look closer at a threefold approach to the problem of congestion in aviation.
The first effort is the prediction of delays and the development of a model that will make these predictions under a wide variety of distributional assumptions. The work presented here is specifically on a continuum approximation using diffusion methods that enables efficient solutions under a wide variety of distributional assumptions.
The second part of the work effort presents the design of a parsimonious language of exchange, with accompanying allocation mechanisms that allow carriers and the FAA to work together quickly, in a Collaborative Decision Making environment, to allocate scarce capacity resources and mitigate delays.
Finally, because airlines proactively use longer scheduled block times to deal with unexpected delays, the third portion of this dissertation presents the assessment of the monetary benefits due to improvements in predictability as manifested through carriers’ scheduled block times.
Air traffic system undergoes a continuous transformation by shifting to smarter, satellite-based and more advanced technologies (FAA, 2013b). An important feature of the Next Generation Air Transportation System (NextGen) is the use of four-dimensional trajectories (4D Trajectories). Aircraft position will be known not only in space but also in time. This will increase the precision of the operations and lead to the reduction of the required spacing between the aircraft.
Currently the system is stochastic and with 4D Trajectories in place will move to a more deterministic system. The queuing models that are more suitable to predict delays in the current state are stochastic in nature and based on solutions of differential equations. Delays in the future, when everything will be more predictable, will be best modeled with deterministic models.
INCORPORATING AIRLINES’ PREFERENCES IN RESOURCE ALLOCATION MECHANISMS DURING IRREGULAR OPERATIONS
As mentioned in the first section of this dissertation a couple of new and promising Traffic Management Initiatives are about to start being implemented; i.e. the Collaborative Trajectories Options Program (CTOP) and the Collaborative Airspace Constraint Resolution (CACR). When these systems are fully implemented, there will be a capability at the FAA to allow carrier preferences to affect the allocation of constrained airspace resources. However it is not clear whether (FAA 2011a) carriers will be able to generate full and robust sets of trajectory alternatives on the fly, with associated costs, in response to suddenly changing capacity conditions, and (Ball and Lulli 2004) that such information, even if it could be generated, could be exploited in a systematic optimization of resource allocation.
This is the motivation for the work presented in this research. A mechanism is proposed by which simpler, yet still useful, information could be submitted by carriers, and an algorithm is demonstrated that directly employs this information to influence the capacity allocation process. Also extensions are proposed for both the way that airlines can submit their preferences and the resource allocation algorithm. Finally the long run effect of using the proposed allocation mechanism with this preference structure is tested. A version of this work has also been published, in this case in Vlachou and Lovell (2013).
IMPACT OF IMPROVED PREDICTABILITY
As mentioned in the beginning of this dissertation, airlines will benefit from increased flight predictability. Airlines tend to add extra time to their scheduled block times in order to absorb delays and maintain their schedules intact as much as possible. It is a way to deal with unexpected delays that occur frequently and cause many problems to the airlines due to missed connections, crews being overtime, unhappy passengers etc. The anticipated mechanisms by which benefits could be realized as a result of improvements in strategic flight predictability can be articulated as follows:
- A reduction in the variability of actual flight times should lead to a reduction in scheduled block times and fuel buffers.
- The reduction in scheduled block times should lead to shorter actual block times.
- The reduction in fuel buffer will lead to a reduction in contingency fuel loaded, which will also lead to a reduction in actual fuel usage.
- With improvements in scheduled and actual block times, carriers could hypothetically achieve the same levels of scheduled operations with fewer aircraft and less total crew duty time.
While the number and duration of operations is not expected to change under this hypothesis, the fuel burned on every segment of each itinerary would be reduced.
The air transportation system in the United States is one of the most complex systems in the world. Projections of increasing air traffic demand in conjunction with limited capacity, that is volatile and affected by exogenous random events, represent a major problem in aviation system management. Air traffic delays are always present and the more air traffic increases the more the delays will increase with serious economic impacts. The scope of this dissertation was to look closer at a threefold approach to the problem of congestion in aviation.
The first part of this thesis was related to the prediction of delays and the development of a model that will make these predictions under a wide variety of distributional assumptions. In this work the mathematical construction of a continuum approximation to a queuing system was presented, that might represent a single congested resource in the National Airspace System, such as an airport, a runway, or some enroute resource. This was the first time ever to consider diffusion approximation in the aviation setting. While the model formulation was based on past work done in other areas like biology, the numeric solution scheme – Finite Element Method (FEM) – was part of this work.
A discrete approximation to the queue length density function was constructed by using triangular basis functions, instead of Gauss – Legendre quadrature, that have known integrals and can be easily solved. The Monte Carlo simulation was set up to serve as the ground truth to compare with the results from the diffusion approximation. It was achieved the replication of the known steady-state results from that small set of queuing systems for which equilibrium results are known in closed form.
Source: University of Maryland
Author: Vlachou | Kleoniki