Adan E. Vela

A mixed integer program for flight-level assignment and speed control for conflict resolution

We consider the air traffic conflict resolution problem and develop an optimization model for generating speed trajectories that minimize the fuel expended to avoid conflicts. The problem is formulated by metering aircraft at potential conflict points. The developed model is a mixed integer linear program that can be solved in near real-time for large number of aircraft.

Near Real-Time Fuel-Optimal En Route Conflict Resolution

In this paper, we consider the air-traffic conflict-resolution problem and develop an optimization model to identify the required heading and speed changes of aircraft to avoid conflict such that fuel costs are minimized. Nonconvex fuel functions in the optimization problem are modeled through tight linear approximations, which enable the formulation of the problem as a mixed-integer linear program. The significance of the developed model is that fuel-optimal conflict-resolution maneuvers can be identified in near real time, even for conflicts involving a large number of aircraft. Computational tests based on realistic air-traffic scenarios demonstrate that conflicts involving up to 15 aircraft can be solved in less than 10 s with an optimality gap of around 0.02%.

Determining Stochastic Airspace Capacity for Air Traffic Flow Management

Deterministic air traffic flow management (TFM) decisions---the state of the art in terms of implementation---often result in unused airspace capacity. This is because the inherent uncertainties in weather predictions make it difficult to determine the number of aircraft that can be safely accommodated in a region of airspace during a given period. On the other hand, stochastic TFM algorithms are not amenable to implementation in practice due to the lack of valid stochastic mappings between weather forecasts and airspace capacity to serve as inputs to these algorithms. To fill this gap, we develop a fast simulation-based methodology to determine the stochastic capacity of a region of airspace using integrated weather-traffic models. The developed methodology consists of combining ensemble weather forecast information with an air traffic control algorithm to generate capacity maps over time. We demonstrate the overall methodology through a novel conflict resolution procedure and a simple weather scenario generation tool, and also discuss the potential use of ensemble weather forecasts. An operational study based on comparisons of the generated capacity distributions with observed impacts of weather events on air traffic is also presented.

Data-Based Modeling and Optimization of En Route Traffic

Air traffic management aims at ensuring safe and efficient movement of aircraft in the airspace. With the predicted growth of air transportation, providing traffic flow managers with the tools to support decision-making is essential. These tools should aid in accommodating the air traffic throughput increase while limiting controller workload and ensuring high safety levels. The objective of this paper is to present a methodology to model and simulate traffic in a given portion of the airspace from data under nominal and perturbed conditions. A new framework for en route traffic flow management and airspace health monitoring is developed. It is based on a data-driven approach for air traffic flow modeling using historical data. This large-scale three-dimensional flow network provides valuable insight on airspace complexity. A linear programming formulation for optimizing en route air traffic is proposed. It takes into account a controller task load model based on flow geometry, in order to estimate airspa...

Determining bounds on controller workload rates at an intersection

This paper considers the problem of determining the maximum controller workload associated with two intersecting flows of aircraft according to aircraft arrival rates. Based on the structure of the aircraft arrivals, an optimal control strategy is presented for minimizing the maximum rate of resolutions required to deconflict traffic at the intersection. Bounds on the rate of resolution take into account magnitude constraints in the conflict resolution commands issued to aircraft. The goal of this research is to establish worst-case measures of workload such that air traffic flow managers can route traffic along network structures without exceeding human-controller performance capabilities.

Airspace Statistical Proximity Maps Based on Data-Driven Flow Modeling

This paper presents a new methodology that aims to rapidly generate 3-D sector proximity maps, which indicate the probability of presence of at least one or two aircraft at any given point in a considered sector. The maps are generated using an aircraft flow model driven from historical data. Time-varying flow characteristics such as routes, speed, probability density function of the inter-arrival time between two consecutive aircraft, were determined using ETMS data. The maps are intended to be a predictive tool for traffic flow management in order to anticipate for a given time period how different flows may interact together and to predict which “critical” regions may be subject to possible conflict between aircraft.

Formulation of Reduced-Taskload Optimization Models for Conflict Resolution

This paper explores methods to include aspects of controller taskload into conflict-resolution programs through a parametric approach. We are motivated by the desire to create conflict-resolution decision-support tools that operate within a human-in-the-loop control architecture by actively accounting for, and moderating, controller taskload. Specifically, we introduce two conflict-resolution programs with the objective of managing controller conflict-resolution taskload, i.e., the number of maneuvers used to separate air traffic. Managing conflict-resolution taskload is accomplished by penalizing aircraft maneuvers through their L1 norm in the cost function or constraining the number of maneuvers directly. Analysis of the programs reveals that both approaches are successful at managing controller conflict-resolution taskload and minimizing fuel burn. Directly constraining conflict-resolution taskload is more successful at minimizing the variation in the number of aircraft maneuvers issued and returning the aircraft to their desired exit point. Penalizing maneuvers through L1 norm costs is more successful at reducing controller conflict-resolution taskload at lower traffic volumes. Ultimately, results demonstrate that the inclusion of such parametric models can successfully regulate controller conflict-resolution taskload.

Air traffic optimization on data-driven network flow model

This paper presents a new framework for Traffic Flow Management and Airspace Health Monitoring based on data-driven approach for air traffic flows modeling using historical data. The large-scale 3-dimensional flow network of the Cleveland center airspace provides valuable insight on airspace complexity. A linear formulation of the Traffic Flow Management Problem is proposed, taking into account estimations of controller workload based on flow geometry. Preliminary results for the problem are discussed, pointing out clues for further research.

Topologically based decision support tools for aircraft routing

This paper considers the problem of rapidly generating aircraft trajectories and providing visual tools for analyzing airspace in the presence of convective weather. When strong convective weather elements are present within an airspace, nominal operations are disrupted. Often, such disruptions result in the closure of arrival and departure routes, or major jetways. Partial recovery from disturbances can take the form of ground delay programs and diversions, re-routing of flights to adjacent airspaces, or utilization of other standard arrival and departure fixes. The work presented here proposes two decision support tools to aid air traffic controllers and managers in re-routing traffic. Starting from weather information, partitions of the airspace are constructed whose boundaries and junctions form a Voronoi graph. The graph is used for path planning and provides information on optimal routing policies. Furthermore, the partitions are also used to identify the set of reachable space by airplanes. A key feature of the tool is that a set of optimal solutions and/or informational content is provided, as opposed to simply providing a single optimal trajectory solution or output with limited informative content. The sets of optimal solutions are obtained by finding homotopy classes for flight routing, leading to greater flexibility in decision making. The central thesis of this paper is that graph-based and continuous trajectory-based path planning algorithms can provide the necessary information for strategic and tactical path planning through weather, and are thus well suited as decision support tools for enhancing air traffic management.

A simplified approach to determine airspace complexity maps under automated conflict resolution

This paper presents a new methodology for rapidly generating complexity maps for various configurations taking into account the influence of some conflict avoidance algorithm at a pair-wise intersection level. The complexity maps are based on analytical expressions, validated through simulations, of the probability of conflict and the spatial distribution of aircraft. This ¿closed-loop¿ analysis explicitly considers the role of the conflict resolution algorithm, here the offset method. It gives therefore a more realistic image of the current and future health of the considered airspace as a function of the encounter and aircraft flows characteristics. Some results of the usual ¿open-loop¿ approach are also validated, while highlighting their limitations.