Lixia Song

Predicting Sector Capacity for TFM Decision Support

En route sector congestion exists when the air traffic demand on an en route sector exceeds the sector capacity. A metric of sector capacity is needed that (1) is a good approximation of the amount of traffic that can be effectively handled in the sector, (2) can be predicted at look-ahead times of 30 minutes to several hours, and (3) can include the impact of convective weather on available capacity. This paper presents a novel approach to predicting sector capacity for airspace congestion management. First, a set of primary flow patterns for each sector of interest is identified through cluster analysis. Second, the sector capacity for each pattern is established based on observed system performance transition behavior. Finally, future sector capacity for a given prediction look-ahead time can be predicted through pattern recognition. Quantifying sector capacity as a function of traffic flow pattern also provides a basis for capturing weather impact on sector capacity.

MODELING TRAFFIC PREDICTION UNCERTAINTY FOR TRAFFIC MANAGEMENT DECISION SUPPORT

TFM personnel are known as Traffic Management Coordinators (TMCs) or Traffic Management Specialists (TMSs), depending on the facility in which they work. The general term for these personnel is traffic managers. One of their primary responsibilities is to ensure that traffic at national airspace system (NAS) resources (e.g., airspace sectors, airports) does not exceed levels that can be safely managed by controllers. Traffic managers also endeavor to ensure fair and equitable treatment for all NAS users, i.e., operators of commercial, general aviation, military, and other aircraft. Air Traffic Flow Management (TFM) is the process of balancing demand for airspace and airport resources with the capacity of those resources, in order to achieve both safe and efficient traffic throughput. Demand is typically estimated by predicting flight trajectories, and comparing the predictions to capacity metrics for airports and airspace. The effectiveness of TFM decision-making depends on the accuracy of these predictions. This effectiveness can be improved not only by improving prediction accuracy, but by quantifying the uncertainty in those predictions. When the uncertainty is known, decision analysis and risk management techniques can be applied to improve decision-making performance. To support this goal, a novel method has been developed for measuring and simulating uncertainty in traffic demand predictions. This method employs empirical observations of traffic characteristics to develop statistical models of the error distributions in demand predictions, which in turn can be used for Monte-Carlo simulation of specific traffic scenarios. Preliminary statistical results are presented here, as well as a discussion of simulation applications for both analysis and real-time decision-support tasks.

Predicting Sector Capacity under Severe Weather Impact for Traffic Flow Management

Sector capacity is dependent on the complexity of the traffic flows within the sector, as well as the presence or absence of hazardous weather. It is well-accepted that the sector capacity must be reduced when an area of severe weather overlaps part of the sector. However, no accepted algorithms for calculating the capacity under severe weather impact have been developed. This paper presents an approach to predict sector capacity under severe weather impact for airspace congestion management. The nominal sector capacity is predicted as a function of traffic flow patterns to capture the traffic complexity. Under severe weather impact, pilot deviation behavior models developed by Lincoln Laboratory are applied to identify the weather avoidance altitude field in the sector. Graph theory is applied to decide the available ratio of flow capacity. Given the predicted traffic flow pattern and the available capacity ratio of each flow, the nominal sector capacity is reduced based on the combination of the flows in the predicted traffic flow pattern. Results are presented for an example sector with example weather impact.

Methodologies of Estimating the Impact of Severe Weather on Airspace Capacity

Accurate predictions of reductions in airspace (sector) capacity due to weather are needed to make effective Traffic Flow Management (TFM) decisions. Sector capacity is dependent on the complexity of the traffic flows within the sector, as well as the presence or absence of hazardous weather. It is well-accepted that the sector capacity must be reduced when an area of severe weather overlaps part of the sector. However, no accepted algorithms for calculating the capacity under severe weather impact have been developed. This paper discusses the three proposed methodologies, which translate 2D sector weather coverage, 3D weather avoidance altitude field coverage, and flow-based available sector capacity ratio to sector capacity reduction.

Probabilistic Airspace Congestion Management

*† ‡ This paper presents a novel approach to airspace congestion management, in which prediction uncertainties are measured and explicitly applied to improve decision-making. The planned automation system would continuously monitor airspace conditions, and identify areas of potential congestion. Airspace users are alerted if their flights are planned through congested areas, can take preventive measures, or provide alternate route options. If congestion probabilities continue to rise, the system suggests minimal corrective actions, such as flight-specific ground delays or reroutes, to maintain an acceptable level of congestion risk. In this way, both safe traffic levels and high throughput can be maintained.

Integrated Collaborative Departure Traffic Management

In today’s departure traffic management operation, Traffic Management Coordinators (TMCs) face numerous challenges allocating the “right route to the right flight at the right time”, especially when thunderstorms are present. The lack of integrated traffic, weather, and airspace resource information leads to inefficient, reactive operations, and the lack of common understanding of the situation creates mistrust among decision makers. The point of action (the air traffic control tower) is too far removed from the point of decision making (the air traffic control center), producing departure queues where the wrong flight is in the wrong place at the wrong time. This paper discusses these challenges in today’s departure traffic management operation, how these challenges can be mitigated with the help of the right decision support tool(s), and how to truly realize the goal of having the right route for the right flight at the right time.

Integrated Departure Route Planning

This paper describes research on the integrated departure route planning (IDRP) operational concept and supporting functions that will assist traffic flow management (TFM) personnel conducting departure management. IDRP will provide automated support and information to help decision makers evaluate and implement different solutions, taking into account all significant data such as filed flight plans and acceptable alternatives, surface departure queues, predicted convective weather and traffic congestion impacts to routes in the terminal area and nearby en route airspace, and forecast uncertainty. By bringing all of these factors into a single integrated environment, IDRP will reduce the time needed to make departure management decisions and coordinate their implementation, increase the effectiveness of the decisions made, and support efficient revision of departure management plans as weather and traffic situations change. IDRPpsilas background and operational concept are described, followed by an overview of a prototype used to demonstrate and evaluate the concept. Results are presented from a series of interviews with experts having TFM experience; these sessions helped to enhance the operational concept and refine the information requirements for the prototype. Next steps are to implement the IDRP prototype in operational centers for the purpose of obtaining further data about the usage and benefits of the capabilities, and to conduct additional laboratory research exploring more highly automated IDRP functionality and addressing integration issues.

Using Probabilistic Demand Predictions for Traffic Flow Management Decision Support

This paper presents candidate information requirements and visualization concepts for explicit representation of uncertainty in decision support for air traffic flow management (TFM). Existing decision support systems for TFM provide a limited representation of uncertainty in predictions about future demand for national airspace system resources. These limitations often result in overly conservative decision-making that restricts traffic flows unnecessarily. It is believed that a better understanding of the sources and magnitude of uncertainty will assist TFM decision-makers in making timely decisions about which decisions to make and which to defer until a better understanding of a situation is developed. The research presented here is a component of a broad effort that seeks to develop risk management tools for TFM. The information presentation concepts described were developed through a set of interviews with operational experts in the TFM domain. The findings provide initial design guidance for the development of human/machine interface concepts for traffic flow management under uncertainty, and help to identify requirements for quantitative modeling of TFM uncertainty.

Solving Probabilistic Airspace Congestion: Preliminary Benefits Analysis

In the U.S. National Airspace System (NAS) a function called traffic flow management (TFM) seeks a balance between resource capacities and the demands placed upon them by air traffic. In general, capacity cannot be manipulated, and it is necessary for demand to be altered to meet a reduced capacity. Typically, demand can be altered in time (via delay, i.e., slowing flights so that the number per unit time is reduced) or space (via rerouting, when specific airspace sector capacity is reduced, e.g., during severe en route weather). This paper discusses the use of probability modeling for assessing airspace capacity, and discusses comparison of three techniques for generating solutions to the problem of demand allocation during reduced airspace capacity caused by severe en route weather.

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.