Small Business Innovation Research/Small Business Tech Transfer
Probabilistic Trajectory Constraint Modeler
Project Description
Air traffic control research, air traffic control operations and user operations rely on simulators that predict the future time history of three-dimensional aircraft trajectories. Such predicted trajectories are fundamental inputs to a wide variety of planning, monitoring and control tasks, including airline seasonal fleet planning, pre-departure flight planning, real-time airspace and airport load forecasting, traffic sequencing and spacing, separation assurance, weather routing, runway assignment, etc. Clearly trajectory simulators are core components of many air traffic applications; it is important that they be as accurate, as efficient and as manageable as possible. We propose an innovative trajectory constraint modeling utility that leads to improvements in all three areas. Regarding trajectory simulator accuracy, there are several categories of error sources that contribute to trajectory prediction uncertainty. One key, and overlooked, source is flight plan nonconformance. Flights often fail to follow their flight plan due to various constraints that are encountered, such as altitude holding, speed control, path stretching and reroutes. It is important to model these constraints both for flight time forecasting as well as load forecasting. Such constraints are not deterministic and their variance is a major contributor to trajectory prediction error. Therefore our constraint modeler produces probabilistic constraint forecasts which, in turn, support probabilistic trajectory prediction. Advanced air traffic applications not only require trajectory predictions that (i) are as accurate as possible, but also that (ii) provide an indication of their error as well, which can vary substantially. Traditionally, predictors have produced deterministic trajectories and their uncertainty is often ignored. We also discuss in our proposal how our trajectory constraint modeler supports significant improvements in efficiency and manageability of trajectory predictors.
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Anticipated Benefits
Our 4D Probabilistic Trajectory Constraint Modeler has several valuable applications for NASA. First, it will be an important addition to existing trajectory predictors. No modifications are required as our Probabilistic Trajectory Constraint Modeler will be used to enhance the accuracy of the input flight plan data. The result will be more accurate trajectory forecasting which will improve the efficiency, benefits and acceptance of NASA air traffic control and air traffic management tools. Also, because our modeler provides the probabilistic information about the constraints, it support probabilistic trajectory prediction which will be important in the next generation decision support tools. Finally, our Probabilistic Trajectory Constraint Modeler supports extremely fast trajectory prediction. Such a trajectory predictor will enable entirely new optimization approaches for traffic planning tools.
Beyond NASA, potential applications for this work lie with operators ranging from major airlines to charters and air taxi services, the Federal Aviation Administration and international Air Navigation Service Providers (ANSPs). In fact we already have provided an initial version of an empirical trajectory predictor to an operator with great success. This product is useful for operators both for seasonal fleet planning, predeparture flight planning and real-time flight following. With more accurate flight times, operators can make more efficient use of their fleet and provide more accurate arrival times to their customers. More accurate flight times also improves the operator flight planning process. For instance, operators can compute more accurately the required fuel loading for their flights. This can reduce their takeoff weight, thus reducing their overall fuel burn and environmental impact. For their dispatch and flight following tasks, operators will have more accurate predictions of the future progress of a flight as it progresses along its route. In addition to operators, our Probabilistic Trajectory Constraint Modeler will be a valuable component for the Federal Aviation Administration and international Air Navigation Service Providers (ANSPs) for improved traffic flow planning. More »
Beyond NASA, potential applications for this work lie with operators ranging from major airlines to charters and air taxi services, the Federal Aviation Administration and international Air Navigation Service Providers (ANSPs). In fact we already have provided an initial version of an empirical trajectory predictor to an operator with great success. This product is useful for operators both for seasonal fleet planning, predeparture flight planning and real-time flight following. With more accurate flight times, operators can make more efficient use of their fleet and provide more accurate arrival times to their customers. More accurate flight times also improves the operator flight planning process. For instance, operators can compute more accurately the required fuel loading for their flights. This can reduce their takeoff weight, thus reducing their overall fuel burn and environmental impact. For their dispatch and flight following tasks, operators will have more accurate predictions of the future progress of a flight as it progresses along its route. In addition to operators, our Probabilistic Trajectory Constraint Modeler will be a valuable component for the Federal Aviation Administration and international Air Navigation Service Providers (ANSPs) for improved traffic flow planning. More »
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Primary U.S. Work Locations and Key Partners
| Organizations Performing Work | Role | Type | Location |
|---|---|---|---|
| Mosaic ATM, Inc. | Lead Organization | Industry | Leesburg, Virginia |
Ames Research Center
(ARC)
|
Supporting Organization | NASA Center | Moffett Field, California |
Primary U.S. Work Locations
-
California
-
Virginia
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