Craig Wanke

MEASURING UNCERTAINTY IN AIRSPACE DEMAND PREDICTIONS FOR TRAFFIC FLOW MANAGEMENT APPLICATIONS

Traffic flow management (TFM) in the U.S. is the process by which the Federal Aviation Administration (FAA), with the participation of airspace users, seeks to balance the capacity of airspace and airport resources with the demand for these resources. This is a difficult process, complicated by the presence of severe weather or unusually high demand. TFM in en-route airspace is concerned with managing airspace demand, specifically the number of flights handled by air traffic control (ATC) sectors; a sector is the volume of airspace managed by an air traffic controller or controller team. Therefore, effective decision-making requires accurate sector demand predictions. While it is commonly accepted that the sector demand predictions used by current and proposed TFM decision support systems contain significant uncertainty, this uncertainty is typically not quantified or taken into account in any meaningful way. The work described here is focused on measuring the uncertainty in sector demand predictions under current operational conditions, and on applying those measurements towards improving the performance and human factors of TFM decision support systems.

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.

A Stochastic Network Model for Uncertain Spatiotemporal Weather Impact at the Strategic Time Horizon

Motivated by challenges in flow-contingency management, we introduce a stochastic network model for the spatiotemporal evolution of weather impact at a strategic time horizon. Specifically, we argue that a model that represents weather-impact propagation using local probabilistic influences can capture the rich dynamics and inherent variability in weather impact at the spatial and temporal resolution of interest. We then illustrate that such an influence model for weather impact is simple enough to permit a family of analyses that are needed for decision-support, including 1) model parameterization to meet probabilistic forecasts at time snapshots, 2) fast simulation of representative weather trajectories and impact probabilities, and 3) computation of correlations and higher-order statistics in weather impact. Also, lower-order representation of the stochastic dynamics at critical locations in the airspace is considered. Finally, a brief exploratory discussion is given on how the weather-impact model may eventually be used in tandem with network flow models to study flow contingency management. 1. Motivation and Goals As the Next Generation Air Transportation System (NextGen) comes into operation, a wide array of new decision-support tools for traffic flow management (TFM) are needed, in order to meet the performance requirements of the new system and to take advantage of its new hardware capabilities. Although decision-support for tactical TFM has been advanced significantly during the last few years, TFM design at the strategic and planning time horizons (2hrs – 1day, and days – months/years, respectively) remains challenging. A major obstacle in current TFM operations is the often overly conservative actions taken when demand exceeds capacity in either predicted or impending operations. A lack of information availability and integration, as well as grave limitations in decision support systems that assist decision makers in identifying and alleviating potential congestion in a way that minimizes the impact on the National Airspace System (NAS), are understood to be current deficits in the system. However, the details on exactly what decision support system capabilities are necessary, and the resulting products from these decision support systems, are not clearly defined. The work that we present here is motivated by this need for decision-support at the strategic time frame.

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.

A Decision Support Tool for Flow Contingency Management

This paper describes a prototype capability for Flow Contingency Management, a component of strategic Traffic Flow Management decision making in the Next Generation Air Transportation System. The Flow Contingency Management concept and associated capabilities described in this paper aim to address current shortfalls in today’s strategic planning process, namely the lack of integrated information, simulation and evaluation capabilities provided to decision makers. Specifically, the proposed prototype integrates the traffic and weather forecasts and further translates these predictions into forecasts of system impact, addressing a gap in today’s operating environment. Viewing the integrated forecast, decision makers can simulate and evaluate proposed congestion-mitigation strategies prior to implementation and quantitatively compare different options before enacting a given plan. As such, the prototype provides an integrated problem identification and quantitative what-if analysis capability for strategic traffic flow management. The paper reviews the overall concept and associated modeling framework, highlighting aspects of the model that address difficulties inherent to traffic flow management planning in the strategic timeframe. To illustrate the proposed decision making process, an example weather and traffic situation, taken from historic data, is simulated and the results highlight the envisioned operational benefits for strategic traffic flow management decision making.

Dynamic Generation of Operationally Acceptable Reroutes

*† A method is proposed for generating operationally acceptable reroutes for air traffic management when a weather event is encountered. A decision support system that dynamically creates flight specific reroutes would aid traffic managers in efficiently maneuvering flights, but only if the reroutes provided were acceptable alternatives. A s such, this research proposes a methodology that captures, through the network definition and metric evaluations, properties of reroutes that are both flexible and operationally acceptable. The methodological approach is reviewed and details are provided on the modeling formulation and solution algorithm employed. The results of the implementation of a simple example problem are investigated to identify how the different metrics and associated weighting factors defining reroute operational acceptability impact reroute quality. ITH the anticipated increase in airspace demand, managing congestion, especially during weather events, requires improved methods for assisting decision makers and increasing operational efficiency. Hazardous weather requires traffic managers to reroute flights that plan to pass through the weather, while balancing demand through sectors with reduced capacity or increased traffic volumes (resulting from other flights deviated from their original routes). Today’s methods for rerouting traffic are mostly manual: air traffic managers employ their expertise to handle a single flight, or entire flows of traffic are rerouted using National Playbook routes 1

A Framework for Flow Contingency Management

This paper presents an operational concept and corresponding framework for flow contingency management, a component of strategic traffic flow management in the Next Generation Air Transportation System. The concept and framework described in this paper aim to address the lack of information, and simulation and evaluation capabilities provided to decision makers in today’s strategic planning process. Specifically, the proposed concept explicitly models the uncertainties present at longer look-ahead times and provides quantitative analysis tools to evaluate the impact of proposed congestion-mitigation actions. This paper develops the overall concept and defines the associated modeling framework which specifies the flow of information throughout the decision making process. An example weather and traffic situation, taken from historic data, is simulated to illustrate the concept.