The air traffic flow management problem with enroute capacities

Includes bibliographical references (p. 41-42).Research supported by grants from Draper Laboratory and the FAA. Research supported by a Presidential Young Investigator Award. DDM-9158118Dimitris Bertsimas, Sarah Stock

Control and optimization algorithms for air transportation systems

Modern air transportation systems are complex cyber-physical networks that are critical to global travel and commerce. As the demand for air transport has grown, so have congestion, flight delays, and the resultant environmental impacts. With further growth in demand expected, we need new control techniques, and perhaps even redesign of some parts of the system, in order to prevent cascading delays and excessive pollution. In this survey, we consider examples of how we can develop control and optimization algorithms for air transportation systems that are grounded in real-world data, implement them, and test them in both simulations and in field trials. These algorithms help us address several challenges, including resource allocation with multiple stakeholders, robustness in the presence of operational uncertainties, and developing decision-support tools that account for human operators and their behavior. Keywords: Air transportation; Congestion control; Large-scale optimization; Data-driven modeling; Human decision processe

Identification of high-level functional/system requirements for future civil transports

In order to accommodate the rapid growth in commercial aviation throughout the remainder of this century, the Federal Aviation Administration (FAA) is faced with a formidable challenge to upgrade and/or modernize the National Airspace System (NAS) without compromising safety or efficiency. A recurring theme in both the Aviation System Capital Investment Plan (CIP), which has replaced the NAS Plan, and the new FAA Plan for Research, Engineering, and Development (RE&D) rely on the application of new technologies and a greater use of automation. Identifying the high-level functional and system impacts of such modernization efforts on future civil transport operational requirements, particularly in terms of cockpit functionality and information transfer, was the primary objective of this project. The FAA planning documents for the NAS of the 2005 era and beyond were surveyed; major aircraft functional capabilities and system components required for such an operating environment were identified. A hierarchical structured analysis of the information processing and flows emanating from such functional/system components were conducted and the results documented in graphical form depicting the relationships between functions and systems

The stochastic air traffic flow management rerouting problem

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (leaves 42-43).We formulate a model for planning the rerouting of aircraft to alleviate en-route congestion, with system capacity being modeled stochastically. To overcome problems with tractability, we apply a Dantzig-Wolfe decomposition and present an efficient method for solving it. The decomposed formulation is shown to be tractable for real-world problem, and it generates up to a ten percent reduction in cost when compared to an otherwise equivalent deterministic model. We show that even when the decomposed formulation fails to terminate within a reasonable time, a near-optimal solution can still be generated.by Joshua B. Marron.M.Eng

Computational Methods for Probabilistic Inference of Sector Congestion in Air Traffic Management

This article addresses the issue of computing the expected cost functions from a probabilistic model of the air traffic flow and capacity management. The Clenshaw-Curtis quadrature is compared to Monte-Carlo algorithms defined specifically for this problem. By tailoring the algorithms to this model, we reduce the computational burden in order to simulate real instances. The study shows that the Monte-Carlo algorithm is more sensible to the amount of uncertainty in the system, but has the advantage to return a result with the associated accuracy on demand. The performances for both approaches are comparable for the computation of the expected cost of delay and the expected cost of congestion. Finally, this study shows some evidences that the simulation of the proposed probabilistic model is tractable for realistic instances.Comment: Interdisciplinary Science for Innovative Air Traffic Management (2013

Alternative Trajectory Options for Delay Reduction in Demand and Capacity Balancing

Aiming to a more collaborative demand and capacity balancing (DCB), in the scope of trajectory based operations, this paper presents an approach that takes alternative trajectories into a DCB optimization algorithm. These alternative trajectories are generated by the airspace users for those flights traversing hotspots (i.e. sectors with demand above capacity), which are predicted by the Network Manager. The trajectories consider lateral re-routings and/or vertical avoidance of all detected hotspots, which, along with different types of delay measures (including linear holding and in-flight delay recovery), are then integrated as a whole into a centralized optimization model to manage the traffic flow under a set of static scheme of airspace capacities. The combination of trajectory options and distribution of delays are hence optimized with the objective of minimizing the total deviation with regard to airspace users’ preferences (taking into account the fuel consumption, route charge and the cost of delay). Results suggest that delays can be remarkably reduced once alternative trajectory options are included in the DCB algorithm. Nevertheless, this delay reduction is obtained by diverting a large number of flights, yielding to an interesting trade-off between environmental impact and cost-efficiency for the airspace users.Peer ReviewedPostprint (published version

Applications of stochastic modeling in air traffic management:Methods, challenges and opportunities for solving air traffic problems under uncertainty

In this paper we provide a wide-ranging review of the literature on stochastic modeling applications within aviation, with a particular focus on problems involving demand and capacity management and the mitigation of air traffic congestion. From an operations research perspective, the main techniques of interest include analytical queueing theory, stochastic optimal control, robust optimization and stochastic integer programming. Applications of these techniques include the prediction of operational delays at airports, pre-tactical control of aircraft departure times, dynamic control and allocation of scarce airport resources and various others. We provide a critical review of recent developments in the literature and identify promising research opportunities for stochastic modelers within air traffic management

Enhanced Demand and Capacity Balancing based on Alternative Trajectory Options and Traffic Volume Hotspot Detection

Nowadays, regulations in Europe are applied at traffic volume (TV) level consisting in a reference location, i.e. a sector or an airport, and in some traffic flows, which act as directional traffic filters. This paper presents an enhanced demand and capacity balance (EDCB) formulation based on constrained capacities at traffic volume level. In addition, this approach considers alternative trajectories in order to capture the user driven preferences under the trajectory based operations scope. In fact, these alternative trajectories are assumed to be generated by the airspace users for those flights that cross regulated traffic volumes, where the demand is above the capacity. For every regulated trajectory the network manager requests two additional alternative trajectories to the airspace users, one for avoiding the regulated traffic volumes laterally and another for avoiding it vertically. This paper considers that the network manager allows more flexibility for the new alternative trajectories by removing restrictions in the Route Availability Document (RAD). All the regulated trajectories (and their alternatives) are considered together by the EDCB model in order to perform a centralised optimisation minimising the the cost deviation with respect to the initial traffic situation, considering fuel consumption, route charges and cost of delay. The EDCB model, based on Mixed-Integer Linear Programming (MILP), manages to balance the network applying ground delay, using alternative trajectories or both. A full day scenario over the ECAC area is simulated. The regulated traffic volumes are identified using historical data (based on 28th July of 2016) and the results show that the EDCB could reduce the minutes of delay by 70%. The cost of the regulations is reduced by 11.7%, due to the reduction of the delay, but also because of the savings in terms of fuel and route charges derived from alternative trajectories