Tudor Masek

Defining Multi-Resolution Networks for Flow Contingency Management

This paper describes a network modeling approach developed to support Flow Contingency Management, a component of the strategic traffic flow management system in the Next Generation Air Transportation System. The overall concept and associated modeling framework described in this paper provide a set of requirements for defining the network structure. Specifically, the network must be designed to allow a queuing model to propagate stochastic flows and analyze the impact of flow constraints as well as demandshaping controls. In addition, the network topology must result in a computationallytractable framework to support strategic timeframe decision making. To address these needs, a network model that uses multiple levels of resolution to represent various National Airspace System resources is proposed. Specifically, it is proposed that a boundary forming an area(s) of interest be defined, within which resources are represented at a greater level of detail than resources outside the area(s). Finally, an example problem, based on historic traffic and weather, is used to validate the effectiveness of using multiple levels of resolution within the network model and analyze the benefits and costs associated with proposing various boundaries on the area of interest.

Modeling Air Traffic Demand for a Real-Time Queuing Network Model of the National Airspace System

A predictive model for departure traffic demand and its route distribution at look-ahead times of 2-15 hours is proposed, for use in a queuing-network-based tool for strategic Traffic Flow Management (TFM). The proposed model uses a combination of operational data (filed flight plans, schedules), historical statistics of demand, and time- of-operation-specific factors to generate statistical predictions of traffic demand for particular routes between pairs of airports or airport clusters. Specifically, a two-stage predictor for demand is proposed. First, traffic demand for an origin-destination (O-D) pair is modeled as the summation of a known demand which captures filed and scheduled traffic, and an unknown demand which is modeled as non-homogeneous Poisson process. Second, the fraction of this O-D traffic demand on each route is modeled using a linear regression, with the historical route fractions, known (filed) route fractions, and wind-adjusted transit times for the routes serving as regressors. Historical data on demands and actual traffic volumes are used to evaluate aspects of the model, including the Poisson-process assumption and the regression model for route distributions.

Reinventing high density area departure traffic management

As a complex dynamic system, todays National Airspace System (NAS) can be very sensitive to disruptive events. High density area departure management is particularly sensitive to such disruptions. This paper builds upon previous research that proposes an operational concept to ensure safe, efficient, and stable departure traffic management in the Next Generation Air Transportation System. This research first proposes the proper roles and responsibilities of Traffic Management Coordinators (TMCs) in different facilities and then defines the functions/capabilities needed to support the roles and responsibilities identified. This paper models, simulates, and compares the performance of the proposed operation with todays operation. The sensitivity of the proposed operation to events like over-head flow constraints is also examined and compared with todays operation. The results reveal that the proposed concept provides performance enhancements and system stability in response to disruption.

A Decision Support Tool for High Density Area Departure and Arrival Traffic Management

An operational concept is proposed to improve high-density area departure and arrival traffic management that specifically accounts for complications arising from multiple airports located in close proximity. In the proposed concept, the roles and responsibilities are redistributed among the Traffic Management Coordinators in different facilities, which include the Air Route Traffic Control Center, Terminal Radar Approach Control, and the Airport Traffic Control Tower. The redistribution of roles and responsibilities facilitates improved decision making capabilities thereby increasing safe, efficient, and stable operation of departure and arrival traffic in the Next Generation Air Transportation System. This paper proposes a set of functions and capabilities needed to support the roles and responsibilities defined in the proposed concept. The decision support system framework defines three levels of decision making and incorporates an optimization methodology to assist decision makers at the different phases of the decision process. A detailed description of the decomposition and corresponding decision support system structure are presented and a description of the optimization models is provided. An analysis is performed on a realistic traffic example to demonstrate the optimization model and illustrate the concept.

Designing Traffic Flow Management Strategies Using Multiobjective Genetic Algorithms

This paper develops an approach for automation-assisted design of traffic flow management strategies using multiobjective genetic algorithms. In situations where the imbalance of demand and capacity cannot be managed with tactical measures, traffic management initiatives are often imposed; however, the definition of these initiatives, including a number of parameters required for implementation, are determined based on experience. For automation to assist in this process, a method is needed to identify a small number of solutions that embody the best strategies across a variety of metrics. This paper proposes an approach that uses multiobjective genetic algorithms to readily identify the Pareto set of solutions and further cluster these solutions based on the structure of the strategies as well as their performance. Results verify the performance, accuracy, and scalability of the multiobjective genetic algorithm for identifying the Pareto set of solutions by evaluating only a minute fraction of the design...

Air Route Clustering for a Queuing Network Model of the National Airspace System

A network queuing model of the National Airspace System has been developed to support research into a strategic air traffic flow management capability. One of the challenges in the execution of the model is the size of the network – the computing resources required when modeling the entire United States are immense. As a way to reduce the network size, we investigate route clustering, i.e., grouping similar routes to reduce the number of paths between two airports. Clustering routes comes at a cost: as the number of clusters falls, the with-in cluster variability rises, and the solution quality is diminished. A trade-off curve for solution quality vs. cluster variability is developed for a sample problem involving seven major airports. I. Introduction/Background A prototype capability for strategic air traffic flow management is undergoing research and development. The capability, called Flow Contingency Management (FCM), will supply automated decision support for what currently is a mostly manual process. 1 It is recognized that strategic decisions made with a 2- to 24-hour time horizon will likely improve air traffic flows in the National Airspace System (NAS) by averting large-scale traffic congestion due to weather. The Next-Generation Air Transportation System (NextGen) mid-term concept reflects the need for this type of capability. Basic functionality has been developed for the prototype, including the representation of weather and traffic forecasts, and the integration of the two forecasts for predictions of significant impact. At the operative look-ahead times, there is significant uncertainty in the forecasts of both weather and traffic and, therefore, it is not appropriate to represent traffic at the level of individual flights. Rather, an aggregate model has been developed whereby traffic is represented as flows (an undifferentiated count of flights progressing in quarter-hour steps) in a queuing network. An initial formulation of such a model uses historical aircraft routings, one-day-prior filed flight routes, and “dayof” filed and predicted counts as input to a regression model to create the demand on a network of routes between airports. In the network, routes are represented by sequences of airspace sectors, 5 demand is expressed as the fraction per route of total flow between airports, and airports are represented as source and sink nodes. The queuing network model operates by associating air traffic demand with a sequence of sectors, and advancing time in quarterhour increments. Sectors have a finite capacity, and flights may queue before transiting a sector, if demand would exceed capacity. In prior work, it was found that clustering airports reduced the network size and complexity, as well as the model’s run-time. 2 In this paper, we explore another means of reducing network size: route clustering, i.e., grouping of similar routes between airports. Assessing similarity of routes requires a similarity/difference measure and we propose the use of a specialized algorithm called “edit distance,” appropriate for lexical string representation, i.e., the sequence of sectors in a route. The paper is organized as follows. The next section describes the clustering algorithm: edit distance, similarity/difference assessment, and selection of a clustering method. Subsequent sections examine initial results, selection of a similarity threshold, and trading-off regression model error and resultant network size. A final section summarizes findings and suggests a next step in the analysis.

Framework for High-Density-Area Departure and Arrival Traffic Management

An operational concept is proposed to improve high-density-area departure and arrival traffic management that specifically accounts for complications arising in metroplex operations where multiple airports are located in close proximity. In the proposed concept, the roles and responsibilities are redistributed among the traffic management coordinators in different facilities, which include the air route traffic control center, terminal radar approach control, and the airport traffic control tower. The redistribution of roles and responsibilities facilitates improved decision-making capabilities, thereby increasing safe, efficient, and stable operation of departure and arrival traffic in the Next Generation Air Transportation System. This paper proposes a set of functions and capabilities needed to support the roles and responsibilities defined in the proposed concept. The decision support system framework defines three levels of decision making and incorporates an optimization methodology to assist decisi...

Measuring Flight Efficiency in the National Airspace System

Measuring and monitoring resources in the National Airspace System (NAS) is a key activity of Federal Aviation Administration (FAA) operational managers. Airports and associated terminal areas are complex systems and require a number of metrics to properly characterize performance. In this document, 13 performance measures are described, in three different categories: (1) flow efficiency, for both taxiing and airborne flights, (2) runway utilization, for both arrivals and departures, and (3) rate of flights not cancelled or diverted, for both arrivals and departures. These 13 metrics capture performance in the different stages of flight from push-back to gate arrival. Sample calculations and use cases are presented, using Chicago O’Hare International Airport (ORD) as a subject airport. The multiple measures may be combined for an omnibus metric, for quick assessment of performance at that airport, in a next-day review context.

A Monte Carlo simulation tool for evaluating space launch and re-entry operations

The Federal Aviation Administration (FAA) and the MITRE Corporation (MITRE), among other entities, are working together to integrate space launch and re-entry operations into the National Airspace System (NAS). As part of the effort, MITRE is building a flexible, fast-time Monte Carlo modeling and simulation capability that offers operational measures of safety. Once validated, the FAA can use it to assess different separation concepts and associated standards, and support the FAAs Safety Management System process. The developed capability will help the FAA determine what separation concepts and associated standards ensure safety for each space launch and re-entry operation and will provide insight into the required surveillance performance, air traffic control response times, and traffic limits to enable new separation concepts. MITRE built the capability using our models for aircraft flight trajectories, aircraft-to-aircraft conflict prediction and detection, and surveillance performance. To tailor these models for use in examining space launch and re-entry operations, MITRE also developed a space vehicle trajectory model and debris model (based on Stanford Universitys work-Range Safety Assessment Tool) for use in the simulation of the scenarios. This paper focuses on describing the modeling capabilities, algorithms, and some preliminary simulation results.