Abstract: In an example, a method for optimizing energy loading in airline operations is described. The method includes receiving a flight plan of an aircraft indicative of a plurality of landing sites and a corresponding plurality of flight legs. The method includes determining an energy load of the aircraft associated with each flight leg. The energy load corresponds to an amount of fuel used during the flight leg. The method includes calculating a fuel value score for refueling the aircraft at a landing site of each flight leg. The fuel value score relates to a refueling estimate at the landing site and a time for refueling at the landing site. The method includes determining a fueling plan based at least on the energy load of the aircraft at each flight leg and the fuel value score for refueling the aircraft at the landing site of each flight leg.

Abstract: A method is provided for predicting a reroute for a planned flight of an aircraft. The method includes building a machine learning model to predict a reroute on a future date of a planned flight of an aircraft. The machine learning model is built in a batch process that includes accessing reroute data and weather data, and performing a data wrangling of the reroute data and the weather data to produce a collection of data keyed by date. Candidate machine learning models are built using a training set produced from the collection of data. The candidate machine learning models are evaluated, and the machine learning model is selected from the candidate machine learning models based on the evaluation. And the machine learning model is output for deployment to classify the future date as having a reroute advisory issued, and predict a reroute on the future date.

Abstract: Disclosed herein are methods and systems for dynamically calculating a total fuel uplift quantity for an aircraft scheduled to fly a flight route. In one aspect, a method comprises: (a) polling a plurality of sources to receive data indicative of: (i) real-time weather conditions in remaining flight sectors in the flight route, and (ii) delay information in the remaining sectors; (b) calculating for the remaining sectors a respective fuel consumption factor; (c) based on (i) respective fuel quotations in the remaining sectors, (ii) the real-time weather conditions, and (iii) the delay information, generating a linear model for calculating a respective fuel uplift quantity at arrival stations in the remaining sectors; (d) calculating using the linear model the respective fuel uplift quantity at the arrival stations; and (e) periodically performing operations (a)-(d) to update a calculation of the respective fuel uplift quantities to account for changing factors.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve aircraft traffic control. An example apparatus includes a network interface to obtain air route traffic (ART) data associated with aircraft flying in airspace sectors during a first time period, a database controller to generate a first database entry by mapping extracted portions of the ART data to a first database entry field of the first database entry, and ART sector services to execute machine learning (ML) models using database entries to generate first aircraft traffic counts of the airspace sectors during the first time period, in response to selecting a first ML model of the ML models based on the first aircraft traffic counts, execute the first ML model to generate second aircraft traffic counts during a second time period, and transmit the second aircraft traffic counts to a computing device to cause an aircraft flight plan adjustment.

Abstract: Disclosed herein are methods and systems for dynamically calculating a total fuel uplift quantity for an aircraft scheduled to fly a flight route. In one aspect, a method comprises: (a) polling a plurality of sources to receive data indicative of: (i) real-time weather conditions in remaining flight sectors in the flight route, and (ii) delay information in the remaining sectors; (b) calculating for the remaining sectors a respective fuel consumption factor; (c) based on (i) respective fuel quotations in the remaining sectors, (ii) the real-time weather conditions, and (iii) the delay information, generating a linear model for calculating a respective fuel uplift quantity at arrival stations in the remaining sectors; (d) calculating using the linear model the respective fuel uplift quantity at the arrival stations; and (e) periodically performing operations (a)-(d) to update a calculation of the respective fuel uplift quantities to account for changing factors.

Abstract: Examples provide a method and apparatus for predicting airport utilization. A plurality of data sources provides raw flight data and weather data which is stored in a key value data store. The incoming raw data is cleaned, correlated and indexed. The processed data is stored in an interim data store. A plurality of competing predictive models use the processed data and historical data to generate predicted arrivals and departures for a selected runway during a selected time-period. The predicted data is compared to actual arrival and departures for the selected runway. The ML model generating predicted results most closely matching the actual arrival and departure data is selected. The selected ML model generates predicted future arrivals and future departures data for the runway based on real-time flight data and weather data for output via a user interface device.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve aircraft traffic control. An example apparatus includes a network interface to obtain air route traffic (ART) data associated with aircraft flying in airspace sectors during a first time period, a database controller to generate a first database entry by mapping extracted portions of the ART data to a first database entry field of the first database entry, and ART sector services to execute machine learning (ML) models using database entries to generate first aircraft traffic counts of the airspace sectors during the first time period, in response to selecting a first ML model of the ML models based on the first aircraft traffic counts, execute the first ML model to generate second aircraft traffic counts during a second time period, and transmit the second aircraft traffic counts to a computing device to cause an aircraft flight plan adjustment.

Abstract: Disclosed herein are methods and systems for dynamically calculating a total fuel uplift quantity for an aircraft scheduled to fly a flight route. In one aspect, a method comprises: (a) polling a plurality of sources to receive data indicative of: (i) real-time weather conditions in remaining flight sectors in the flight route, and (ii) delay information in the remaining sectors; (b) calculating for the remaining sectors a respective fuel consumption factor; (c) based on (i) respective fuel quotations in the remaining sectors, (ii) the real-time weather conditions, and (iii) the delay information, generating a linear model for calculating a respective fuel uplift quantity at arrival stations in the remaining sectors; (d) calculating using the linear model the respective fuel uplift quantity at the arrival stations; and (e) periodically performing operations (a)-(d) to update a calculation of the respective fuel uplift quantities to account for changing factors.

Abstract: Embodiments provide for vertical flight path optimization by generating a plurality of waypoints with allowable parameters for a flightpath at which a course, including elements for heading, altitude, and speed, of an aircraft is adjustable; calculating a number of unique trajectories available based on the plurality of waypoints; when the number of unique trajectories is greater than a threshold number of trajectories, performing a probabilistic assessment of the unique trajectories to identify an elite set of trajectories that include those trajectories with efficiency metrics within an upper range of a set of assessed trajectories; identifying mobile waypoints in trajectories of the elite set of trajectories; performing a global optimal path assessment, wherein positions of mobile waypoints are adjusted within an associated trajectory to identify an optimal trajectory for the aircraft on the flightpath; and providing the optimal trajectory to the aircraft to follow the flightpath according to the optimal t

Abstract: In an example, a method for optimizing energy loading in airline operations is described. The method includes receiving a flight plan of an aircraft indicative of a plurality of landing sites and a corresponding plurality of flight legs. The method includes determining an energy load of the aircraft associated with each flight leg. The energy load corresponds to an amount of fuel used during the flight leg. The method includes calculating a fuel value score for refueling the aircraft at a landing site of each flight leg. The fuel value score relates to a refueling estimate at the landing site and a time for refueling at the landing site. The method includes determining a fueling plan based at least on the energy load of the aircraft at each flight leg and the fuel value score for refueling the aircraft at the landing site of each flight leg.

Abstract: Disclosed herein are methods and systems for dynamically calculating a total fuel uplift quantity for an aircraft scheduled to fly a flight route. In one aspect, a method comprises: (a) polling a plurality of sources to receive data indicative of: (i) real-time weather conditions in remaining flight sectors in the flight route, and (ii) delay information in the remaining sectors; (b) calculating for the remaining sectors a respective fuel consumption factor; (c) based on (i) respective fuel quotations in the remaining sectors, (ii) the real-time weather conditions, and (iii) the delay information, generating a linear model for calculating a respective fuel uplift quantity at arrival stations in the remaining sectors; (d) calculating using the linear model the respective fuel uplift quantity at the arrival stations; and (e) periodically performing operations (a)-(d) to update a calculation of the respective fuel uplift quantities to account for changing factors.

Abstract: Embodiments provide for vertical flight path optimization by generating a plurality of waypoints with allowable parameters for a flightpath at which a course, including elements for heading, altitude, and speed, of an aircraft is adjustable; calculating a number of unique trajectories available based on the plurality of waypoints; when the number of unique trajectories is greater than a threshold number of trajectories, performing a probabilistic assessment of the unique trajectories to identify an elite set of trajectories that include those trajectories with efficiency metrics within an upper range of a set of assessed trajectories; identifying mobile waypoints in trajectories of the elite set of trajectories; performing a global optimal path assessment, wherein positions of mobile waypoints are adjusted within an associated trajectory to identify an optimal trajectory for the aircraft on the flightpath; and providing the optimal trajectory to the aircraft to follow the flightpath according to the optimal t

Abstract: An example method includes determining a probable flight trajectory for each respective flight; modifying the probable flight trajectory based on constraints imposed by Air Traffic Controller (ATC) rules to generate a modified flight trajectory; assigning respective arrival slots for modified flight trajectories; receiving trajectory exchange information indicating that two aircraft operators have exchanged two respective flight trajectories associated with two respective flights operated by the two aircraft operators; based on the trajectory exchange information, modifying: (i) the two respective flight trajectories, and (ii) respective arrival slots assigned to the two respective flight trajectories; and transmitting, by the computing device, the modified two respective flight trajectories to respective aircraft assigned to perform the two respective flights.

Abstract: A computing system obtains flight information comprising a plurality of waypoints for each of a plurality of aircraft flight paths, and detects a violation of aircraft separation requirements at a given time instance. Each waypoint specifies an altitude, a longitudinal position, a latitudinal position, a velocity, and a time instance. Detecting the violation comprises selecting a set of time-correlated waypoints from the flight information, each time-correlated waypoint specifying the given time instance. The detecting further comprises selecting a set of altitude-correlated waypoints from the set of time-correlated waypoints, each of the altitude-correlated waypoints being vertically-separated from at least one other altitude-correlated waypoint by less than a threshold vertical separation.

Abstract: A system for equivalence class analysis-based automated requirements-based test case generation includes a control processor, a data store containing textual design requirements, a textual converter unit structured to convert the textual design requirements to a machine-readable version of design requirements, a requirement partition unit configured to partition the machine-readable design requirements into one or more sets of related design requirements, an equivalence class partition unit configured to process the machine-readable design requirements and input/output variables into a set of equivalence classes, an equivalence class analyzer unit structured to analyze the set of equivalence classes to generate equivalence class tests and identify uncovered input space, and a boundary class analyzer unit structured to identify boundaries of the equivalence classes and generate boundary value tests and robustness tests.

Abstract: A system for equivalence class analysis-based automated requirements-based test case generation includes a control processor, a data store containing textual design requirements, a textual converter unit structured to convert the textual design requirements to a machine-readable version of design requirements, a requirement partition unit configured to partition the machine-readable design requirements into one or more sets of related design requirements, an equivalence class partition unit configured to process the machine-readable design requirements and input/output variables into a set of equivalence classes, an equivalence class analyzer unit structured to analyze the set of equivalence classes to generate equivalence class tests and identify uncovered input space, and a boundary class analyzer unit structured to identify boundaries of the equivalence classes and generate boundary value tests and robustness tests.

Abstract: An example method includes determining a probable flight trajectory for each respective flight; modifying the probable flight trajectory based on constraints imposed by Air Traffic Controller (ATC) rules to generate a modified flight trajectory; assigning respective arrival slots for modified flight trajectories; receiving trajectory exchange information indicating that two aircraft operators have exchanged two respective flight trajectories associated with two respective flights operated by the two aircraft operators; based on the trajectory exchange information, modifying: (i) the two respective flight trajectories, and (ii) respective arrival slots assigned to the two respective flight trajectories; and transmitting, by the computing device, the modified two respective flight trajectories to respective aircraft assigned to perform the two respective flights.

Abstract: An example method includes determining a probable flight trajectory for each respective flight; modifying the probable flight trajectory based on constraints imposed by Air Traffic Controller (ATC) rules to generate a modified flight trajectory; assigning respective arrival slots for modified flight trajectories; receiving trajectory exchange information indicating that two aircraft operators have exchanged two respective flight trajectories associated with two respective flights operated by the two aircraft operators; based on the trajectory exchange information, modifying: (i) the two respective flight trajectories, and (ii) respective arrival slots assigned to the two respective flight trajectories; and transmitting, by the computing device, the modified two respective flight trajectories to respective aircraft assigned to perform the two respective flights.

Abstract: An example method includes determining a probable flight trajectory for each respective flight; modifying the probable flight trajectory based on constraints imposed by Air Traffic Controller (ATC) rules to generate a modified flight trajectory; assigning respective arrival slots for modified flight trajectories; receiving trajectory exchange information indicating that two aircraft operators have exchanged two respective flight trajectories associated with two respective flights operated by the two aircraft operators; based on the trajectory exchange information, modifying: (i) the two respective flight trajectories, and (ii) respective arrival slots assigned to the two respective flight trajectories; and transmitting, by the computing device, the modified two respective flight trajectories to respective aircraft assigned to perform the two respective flights.

Abstract: A method includes generating a system model representative of a socio-technical system having a plurality of system parameters. The method further includes selecting one or more essential system parameters from the plurality of system parameters. The method also includes determining a plurality of probability distributions corresponding to the one or more essential system parameters. The method further includes determining at least one hyperbox using a sampling optimization technique based on the one or more essential system parameters. The at least one hyperbox is representative of a confidence region corresponding to a rare event of the socio-technical system. The method also includes determining a probability of the rare event using a variance reduction technique based on a plurality of particles obtained from the at least one hyperbox. The probability of the rare event is representative of a performance of the socio-technical system.