For a more complete understanding of the present invention and further advantages thereof, reference is now made to the following Detailed Description, taken in conjunction with the drawings, in which:
The air traffic demand prediction system 10 includes a schedule retrieval component 12, an expanded route prediction component 14, a trajectory modeling component 16, a sector crossing component 18, an enroute traffic retrieval component 20, a departure time prediction component 22, a response filter component 24, and a graph generation component 26. Such components 12-26 may also be referred to herein as the schedule retriever 12, the expanded route predictor, the trajectory modeler, the sector crossing predictor 18, the enroute traffic retriever 20, the departure time predictor 22, the response filter 24, and the graph generator 26. In the present embodiment, the various components 12-26 of the air traffic demand prediction system 10 are implemented in software instructions executable by one or more processors. In other embodiments, one or more of the components 12-26 of the air traffic demand prediction system may be implemented in hardware or in programmable logic (e.g., in a field programmable gate array) instead of software.
Using various inputs, the components 12-26 of the air traffic demand prediction system 10 generate a demand model 28. The demand model 28 is provided to a demand model interface 30 for presentation to and utilization by a user of the air traffic demand system 10. In this regard, the demand model interface 30 may be a graphical user interface (GUI) displayable on a display device such as, for example, a computer monitor. In this regard, the demand model interface 30 may be implemented in software instructions executable by one or more processors. In other embodiments, the demand model interface 30 may be a non-graphical interface and it may be implemented in hardware or in programmable logic (e.g., in a field programmable gate array) instead of software.
The schedule retrieval component 12 operates to retrieve a flight schedule 32. The schedule retrieval component 12 may retrieve the flight schedule 32 by combining various sources of information including published flight schedules (e.g., the official airline guide (OAG)) available from various airlines and air charter services. The flight schedule 32 includes flight information relating to one or more flights scheduled to depart during a time period of interest. In this regard, the flight information in the flight schedule 32 may include, for example, airline, aircraft type, scheduled departure time, departure airport and destination airport for each flight in the schedule 32. The time period of interest may, in general, be a block of time of any desired length starting at any time in the future. However, in one embodiment, the time period of interest is a one-hour period commencing fifteen hours in the future. The duration of the time period of interest and/or when such time period commences may be fixed in the air traffic management system 10 or variable based on, for example, user selected preferences during start-up of the air traffic demand prediction system 10 and/or user input during operation of the system 10.
Once the flight schedule 32 is created for a time period of interest, a flight request 34 may be selected from the flight schedule 32 for subsequent processing by the air traffic demand prediction system 10. The flight request 34 may also be referred to herein as the requested flight 34. The flight information from the flight schedule 32 for the requested flight 34 is input to the expanded route prediction component 14. Additionally, further information 58 relating to the requested flight 32 may be input to the trajectory modeling component 16. Of particular significance to the trajectory modeling component 16 is the cruise speed and cruise altitude of the flight request 32. Such additional information (e.g., cruise speed, cruise altitude) 58 may be associated with flights included in the schedule 32 by the schedule retrieval component 12 from historical data and/or predictive algorithms.
The expanded route prediction component 14 receives as inputs the flight information for the flight request 34 and also geometric cluster data 36 relating to air traffic routes and historical data 38 relating to air traffic routes. The historical data 38 includes information describing individual flight paths taken by completed flights from departure airports to destination airports. Such information may comprise geographic position fixes specified by, for example, latitude and longitude (lat/long points) associated with the various segments of an individual flight path. The geometric cluster data 36 includes averages or other combinations of the information describing similar individual flight paths taken by completed flights from departure airports to destination airports. In this regard, the geometric cluster data 36 may be obtained from the historical data 38 as described in connection with
All of the historical data 38 and the geometric cluster data 36 available may not necessarily be relevant to the particular flight request 34 being processed since the historical data 38 and the geometric cluster data 36 available may relate to completed flights between different departure and/or destination airports than those in the flight information associated with the flight request 34. In this regard, only historical data 38 and geometric cluster data 36 associated with flights between the same departure and destination airports as in the flight information associated with the flight request 34 being processed may be selected from the historical data 38 and the geometric cluster data 36 for input to the expanded route prediction component 14. For example, if the requested flight 34 originates in San Francisco and is destined for Chicago O'Hare, then historical data 38 and geometric cluster data 36 relating to completed flights from San Francisco to Chicago O'Hare may be selected as the relevant data for input to the expanded route prediction component 14.
Using flight information for the flight request 34, relevant cluster data 36 and relevant historical data 38 as inputs, the expanded route prediction component 14 operates to generate predicted two-dimensional expanded route information (predicted ER2d) 40 associated with the flight request 34. In this regard, the predicted ER2d 40 associated with the flight request 34 includes predicted geographic position fixes (e.g., lat/long points) that define a route expected to be flown by the requested flight 34 from its departure airport to its destination airport. Such predicted route will involve one or more, and often many, air traffic control sectors within the airspace from the departure airport to the destination airport.
The enroute traffic retrieval component 20 operates to generate a set of zero or more enroute flights associated with the flight request 34 for input to the trajectory modeling component 16. An enroute flight consists of two-dimensional expanded route information along with cruise speed and cruise altitude (collectively enroute information 42). In this regard, the enroute information 42 may be obtained from a database of enroute data 44. The enroute data 44 may, for example, include information from a flight plan filed for the requested flight 34 prior to departure and/or actual information transmitted from the flight and/or obtained by systems monitoring the airspace traversed by the flight.
The trajectory modeling component 16 receives the predicted ER2d 40 from the expanded route prediction component 14 along with the additional flight information 58 (e.g., predicted cruise speed and cruise altitude) associated with the requested flight 34. Using these inputs, the trajectory modeling component 16 operates to generate predicted four-dimensional expanded route information (predicted ER4d) 46. In this regard, the predicted ER4d 46 includes geographic position fixes (e.g., latitude/longitude points) that define a route expected to be flown by the requested flight 34 from its departure airport to its destination airport along with altitude and times associated with such geographic position fixes. Also, when available, the enroute information 42 from the enroute traffic retrieval component 20 is input to the trajectory modeling component 16 to provide an enhanced picture of airspace demand in addition to the airspace demands imposed by the requested flight 34 being processed.
The sector crossing component 18 receives the predicted ER4d 46 from the trajectory modeling component 16. Using the predicted ER4d 46 as an input, the sector crossing component 18 outputs predicted sector crossing information 48 to the response filter component 24. In this regard, the predicted sector crossing information 48 includes predicted four-dimensional entry and exit points (e.g., latitude, longitude, altitude, and time) for the airspace sectors along the predicted route of the requested flight 34.
As shown, the trajectory modeling component 16 and the sector crossing component 18 may be part of another air traffic control related system 60. One example of a suitable system 60 is the Lockheed Martin User Request Evaluation Tool (LM URET) system 60. Such a system 60 has been installed in Air Route Traffic Control Centers (ARTCCs) and includes trajectory modeling and sector crossing components 16, 18 suitable for interfacing with or incorporating into the air traffic demand prediction system 10. In other embodiments, the trajectory modeling component 16 and/or the sector crossing component 18 may be components that are only included within the air traffic demand prediction system 10.
The response filter component 24 receives the predicted sector crossing information 48 from the sector crossing component 18. The response filter component 24 operates to filter the predicted sector crossing information 48 to obtain filtered predicted sector crossing information 50. In this regard, the filtered predicted sector crossing component filters the predicted sector crossing information 48 to format times and durations into a standard format and to remove duplicate or otherwise unnecessary sector crossing information.
Using historical departure delay time data 52 as an input, the departure time prediction component 22 generates predicted departure time information 54 for the requested flight 34. In this regard, the predicted departure time information 54 may include a temporal interval during which the requested flight is predicted to depart. A departure time prediction process that may be utilized by the departure time prediction component 22 to generate the predicted departure time information 54 is described in connection with
The filtered predicted sector crossing information 50 and the predicted departure time information 54 are input to the graph generation component 26. Using these inputs, the graph generation component 26 generates a temporal constraint graph 56 representing predicted time intervals for various segments of the requested flight 34 (e.g., predicted early, middle and late entry times into and exit times from various sectors to be traversed by the requested flight 34) along its predicted route.
In one embodiment, the temporal constraint graph 56 generated for each segment of the predicted route may be a Tachyon graph. Tachyon is a computer software implementation of a constraint-based model for representing and reasoning about qualitative and quantitative aspects of time. The Tachyon software may also be referred to herein as the Tachyon temporal reasoner. The Tachyon temporal reasoner was developed by General Electric Global Research Center (GE GRC). In other embodiments, software and/or hardware providing sufficiently similar functionality may be employed in place of the Tachyon temporal reasoner. An exemplary Tachyon graph 56 is depicted and described in connection with
The graph generation component 26 and the Tachyon graph(s) 56 generated thereby may comprise a demand model generation component 62. In other embodiments, the demand model generation component 62 may include additional elements. The output from the demand model generation component 62 (e.g., graph(s) 56) is used to update the demand model 28 that is provided to the demand model interface 30 for presentation to and interaction therewith by a user of the air traffic demand system 10. In this regard, the demand model 28 represents how many flights will be in various sectors of the airspace during the time period of interest. The demand model 28 is updated to incorporate information about the sectors expected to be traversed by the requested flight 34 and predicted time intervals that the requested flight 34 is expected to be in such sectors along with similar information for all other requested flights analyzed for the time period of interest. In this regard, one or more additional requested flights (e.g., obtained from the flight schedule 32) may be analyzed by the air traffic demand prediction system 10 to generate the demand model 28 for all of the requested flights during the time period of interest.
The matches returned by the query or queries are organized into clusters based on proximity of geographic position fixes associated with each flight represented in the historical data 38. The clusters are created apriori and the matches returned by the query or queries are sorted according to the historical flight clusters created apriori. For example, as illustrated in
A cluster selected in accordance with the case-based retrieval process undertaken by the air traffic demand prediction system 10 may be visualized by plotting rectangular boundaries (bounding boxes) around geographical position fixes (lat/long points) of the seed flight. In this regard,
The historically similar cases are used to generate a delay distribution 408. As shown, the delay distribution 408 may be represented by a curve showing the number of historically similar cases versus the temporal delay. A predicted delay interval 410 may then be established. In this regard, the delay interval 410 may be established using, for example, one standard deviation from the mean of the distribution.
The delay distribution 408 and predicted delay interval 410 are input to a departure delay evaluation module 412. The departure delay evaluation module 412 outputs a temporal prediction interval 414. The temporal prediction interval 414 comprises a predicted early departure time (earlyStart or ES) and a predicted late departure time (lateStart or LS) for the requested flight 34. In this regard, ES may be obtained by subtracting one standard deviation from the mean departure time of the delay distribution and LS may be obtained by adding one standard deviation to the mean departure time of the delay distribution.
In the embodiment of
A representation of initial node 502A temporal constraints associated with requested flight 34 is shown in the graph 56 of
The Tachyon temporal reasoner is used to propagate the relevant constraints for each node 502A-502D to obtain the graph 56 associated with each node 502A-502D. In this regard,
The airspace information pane 702A displays information identifying one or more sectors within an airspace and one or more requested flights within the airspace that have been processed by the air traffic demand prediction system 10 to include such flights in the demand model 28. In the example of the
The sector information pane 702B displays information relating to a selected sector (e.g., selected by clicking on its name in the airspace information pane 702A or on its location in the airspace map pane 702F). Information displayed in the sector information pane 702B may include, for example, total sector load, average sector load and enroute sector load information. In the example of
The flight information pane 702C displays information relating to a requested flight processed by the air traffic demand prediction system 10. Information displayed in the flight information pane 702C may include, for example, flight number, airline, aircraft type and flight plan (e.g., air speed, cruise level, departure airport, scheduled departure date/time, destination airport, and scheduled arrival date/time) information. In the example of
The events information pane 702D displays information relating to one or more events that may take place for a requested flight (e.g., the requested flight for which information is displayed in the flight information pane 702C). In this regard, the information displayed for each event may include a number of parameters such as, for example, an event type, the flight identifier (e.g., “EGF264”), a sector (e.g., “ZCM25”) in which the event occurs, and the time of the event. Examples of event types include predicted low (earliest), medium, and high (latest) times of entry of the flight into a sector and exit of the flight from a sector.
The control panel pane 702E displays information identifying one or more available air traffic demand predictions (or runs) associated with one or more airspaces. In the example of
The airspace map pane 702F displays a two-dimensional airspace map depicting the boundaries of the various sectors within the airspace associated with the run selected for execution in the control panel pane 702E. The sector selected for display in the sector information pane 702B may be highlighted on the map displayed within the airspace map pane 702F. In the example of
While various embodiments of the present invention have been described in detail, further modifications and adaptations of the invention may occur to those skilled in the art. However, it is to be expressly understood that such modifications and adaptations are within the spirit and scope of the present invention.