Shon Grabbe

Modeling and Optimization in Traffic Flow Management

Traffic flow management (TFM) allocates the various airport, airspace, and other resources to maintain an efficient traffic flow consistent with safety. TFM is a complex area of research involving the disciplines of operations research, guidance and control, human factors, and software engineering. Hundreds of human operators make TFM decisions that involve tens of thousands of aircraft, en route air traffic control centers, the Federal Aviation Administrations System Command Center, and many airline operation centers. This paper provides an overview of how TFM decisions are made today and challenges facing the system in the future, and reviews modeling and optimization approaches for facilitating system-wide modeling, performance assessments, and system-level optimization of the national airspace system in the presence of both en route and airport capacity constraints.

Sequential Traffic Flow Optimization with Tactical Flight Control Heuristics

‡A sequential optimization method is applied to manage air traffic flow under uncertainty in airspace capacity and demand. To support its testing, a decision support system is developed by integrating a deterministic integer programming model for assigning delays to aircraft under en route capacity constraints to reactively account for system uncertainties. To reduce computational complexity, the model assigns only departure controls, while a tactical control loop consisting of a shortest path routing algorithm and an airborne holding algorithm refines the strategic plan to keep flights from deviating into capacity constrained airspace. This integrated approach is used to conduct thirty-two, 6-hour fast-time simulation experiments to explore variations in the number and severity of departure controls, tactical reroutes, and airborne holding controls. Three feasible types of traffic flow controls emerged. The first type relied primarily on departure controls and strategic reroutes on the 300 to 400 nmi look-ahead horizon and worked best when rerouting occurred at a frequency of 10 to 15 minutes. The second type generated more tactical reroutes on the 200 ‐ 300 nmi look-ahead horizon and required little airborne holding or pre-departure control when rerouting occurred at a frequency of 5 minutes. The last type relied heavily on airborne holding controls and infrequent updates to the weather avoidance reroutes. This last type was the least desirable solution due to the impact of its airborne holding on airspace complexity and airspace users.

Initial Study of Tube Networks for Flexible Airspace Utilization

Air traffic in the United States is reaching new highs as it continues to increase at a steady pace. There is a general consensus that the National Airspace System needs to be transformed from today’s rigid airways and airspace structure to a more flexible arrangement in order to accommodate and manage this growth. It has been suggested that one way to support this divergent growth in the mix of air traffic is to divide the airspace into different categories with different levels of service and entry requirements based on policy or price; e.g., connect high-traffic regions with a network of dedicated “tubes” analogous to the interstate highway system. This paper starts with today’s air traffic as a baseline, groups airports into regions, and models a series of tubes connecting major regions. We achieve this grouping in two different ways, and present results based on the two methods. Next, we present simulation results by connecting these regions with a network of tubes. The modeling approach provides a basis for systematically studying the design and impact of dynamic airspace concepts in the National Airspace System.

Ground delay program planning under uncertainty in airport capacity

Abstract This paper presents an algorithm for assigning flight departure delays under probabilistic airport capacity. The algorithm dynamically adapts to weather forecasts by revising, if necessary, departure delays. The proposed algorithm leverages state-of-the-art optimization techniques that have appeared in recent literature. As a case study, the algorithm is applied to assigning departure delays to flights scheduled to arrive at San Francisco International Airport in the presence of uncertainty in the fog clearance time. The cumulative distribution function of fog clearance time was estimated from historical data. Using daily weather forecasts to update the probabilities of fog clearance times resulted in improvement of the algorithms performance. Experimental results also indicate that if the proposed algorithm is applied to assign ground delays to flights inbound at San Francisco International airport, overall delays could be reduced up to 25% compared to current level.

Central East Pacific Flight Scheduling

*† ‡ This paper examines the implications of strategically scheduling flights on user-preferred routes in the Central East Pacific to reduce trajectory crossing points. After first casting the flight scheduling problem in terms of a job shop scheduling problem, a 0-1 integer programming model is used to calculate the optimal departure and en route controls required to generate feasible flight schedules. To enhance the solution, a ration-by-schedule based heuristic is introduced to transform the original model into a subset of problems. By varying attributes of the machines in the job shop scheduling formation, two distinct types of feasible schedules are obtained. The first type of schedules are highly restrictive yet but free of all four-dimensional trajectory crossing points, while the second type are less restrictive but may require limited tactical flight separation maneuvers to alleviate residual crossing points. The average adjusted time savings per flight varied between 1.8 minutes and 4.6 minutes per flight when allowing for both strategic flight scheduling and user-preferred flight routing.

Disaggregation Method for an Aggregate Traffic Flow Management Model

A linear time-varying aggregate traffic flowmodel can be used to develop traffic flowmanagement strategies using optimization algorithms. However, there are few methods available in the literature to translate these aggregate solutions into practical control actions involving individual aircraft. In this paper, a computationally efficient disaggregation algorithm is proposed by employing a series of linear program and mixed integer linear program methods, which converts an aggregate (flow-based) solution to a flight-specific control action. Numerical results generated by the optimization method and the disaggregation algorithm are presented and illustrated by applying them to generate traffic flow management schedules for a typical day in the U.S. National Airspace System.

Performance evaluation of airborne separation assurance for free flight

Airborne separation assurance is a key requirement for free flight operations. This paper investigates the feasibility of airborne separation assurance for free flight by evaluating the performance of Conflict Detection and Resolution (CD&R) schemes in a simulated air traffic environment. Two qualitatively different CD&R methods were evaluated in an air traffic simulation environment provided by the Future ATM Concepts Evaluation Tool (FACET). The evaluation was based on a horizontal-plane free flight traffic scenario constructed with initial conditions from actual air traffic data; nearly a thousand aircraft were modeled in this 6-hour scenario. The results of the performance evaluation indicate that airborne separation assurance performed quite well in the free flight evaluations: (1) All of the conflicts were resolved; (2) The impact on flight efficiency, as measured by path-length and flight-time changes, was quite small; and, (3) The impact on system stability, as measured by additional aircraft experiencing conflicts due to the “domino effect,” was modest.

Optimizing Aircraft Trajectories with Multiple Cruise Altitudes in the Presence of Winds

This study develops a trajectory-optimization algorithm for approximately minimizing aircraft travel time and fuel burn by combining a method for computing minimum-time routes in winds on multiple horizontal planes and an aircraft fuel burn model for generating fuel-optimal vertical profiles. It is applied to assess the potential benefits of flying user-preferred routes for commercial cargo flights operating between Anchorage, Alaska and major airports in Asia and the contiguous United States. Flying wind-optimal trajectories with a fuel-optimal vertical profile reduces average fuel burn of international flights cruising at a single altitude by 1–3%. The potential fuel savings of performing en route step climbs are not significant for many shorter domestic cargo flights that have only one step climb. Wind-optimal trajectories reduce fuel burn and travel time relative to the flight-plan route by up to 3% for the domestic cargo flights. However, for transoceanic traffic, the fuel burn savings could be as mu...

Traffic Flow Management Using Aggregate Flow Models and the Development of Disaggregation Methods

A linear time-varying aggregate traffic flow model can be used t o develop Traffic Flow Management strategies based on optimization algorithms. However, there are no methods available in the literature to translate these aggregate solutions into actions involving individual aircraft. This paper describes and implements a computationally efficient disaggregation algorithm, which converts an aggregate (flow-based) solution to a flight-specific control action. Numerical results generated by the optimization method and the disaggregation algorithm are presented and illustrated by applying them to generate TFM schedules for a typical day in the U.S. National Airspace System. The results show that the disaggregation algorithm generates control actions for individual flights while keeping the air traffic behavior very close to the optimal solution.

Design and Evaluation of a Dynamic Programming Flight Routing Algorithm Using the Convective Weather Avoidance Model

The optimization of tra! c flows in congested airspace with varying convective weather is a challenging problem. One approach is to generate shortest routes between origins and destinations while meeting airspace capacity constraint in the presence of uncertainties, such as weather and airspace demand. This study focuses on development of an optimal flight path search algorithm that optimizes national airspace system throughput and e! ciency in the presence of uncertainties. The algorithm is based on dynamic programming and utilizes the predicted probability that an aircraft will deviate around convective weather. It is shown that the running time of the algorithm increases linearly with the total number of links between all stages. The optimal routes minimize a combination of fuel cost and expected cost of route deviation due to convective weather. They are considered as alternatives to the set of coded departure routes which are predefined by FAA to reroute pre-departure flights around weather or air tra! c constraints. A formula, which calculates predicted probability of deviation from a given flight path, is also derived. The predicted probability of deviation is calculated for all path candidates. Routes with the best probability are selected as optimal. The predicted probability of deviation serves as a computable measure of reliability in pre-departure rerouting. The algorithm can also be extended to automatically adjust its design parameters to satisfy the desired level of reliability.