Abstract: A method automatically distributes aeronautical information. A source dataset for the aeronautical information is identified. The source dataset is in a common data format and has changes. A destination dataset for the aeronautical information is identified. The destination dataset is in the common data format. An overlap is determined between the source dataset and the destination dataset. The changes are present in the overlap between the source dataset and the destination dataset. A destination change dataset is generated in a destination format specific to the destination dataset. The destination change dataset comprises the changes present in the overlap. The destination change dataset in the destination format is sent to a destination database storing the destination dataset in the destination format.

Abstract: An automated environment for aeronautical information services includes converting a first data set from a first data structure to a common data structure, automatically creating a common database based at least on the common data structure, converting a second data set from a second data structure to the common data structure, analyzing a difference between the first converted data set and the second converted data set to determine a relevance metric, and, if the relevance metric meets a relevance threshold, storing at least a portion of the second converted data set at a filtered database, the filtered database separate from the common database, wherein the portion is associated with the difference.

Abstract: The present disclosure provides a system that includes an ingress module arranged to receive flight data comprising a descent rate value and an altitude value; a data store comprising descent rate threshold values of at least one aircraft model, the descent rate threshold values associated, in the data store, with pressure altitude values; a descent classification module that is operably coupled to the ingress module and the data store and adapted to compare the descent rate value with the descent rate threshold values; an alert module adapted to provide, in response to the descent classification module comparing the descent rate value that exceeds at least one of the descent rate threshold values, an alert signal to a graphic user interface that is operably coupled to the alert module.

Abstract: A flight planning system and method include one or more control units configured to determine, from ensemble data, a plurality of flight plans for an aircraft from a departure location to an arrival location. The ensemble data includes an aggregation of data from multiple data sources. In at least one example, the one or more control units are further configured to determine a preferred flight plan from the plurality of flight plans.

Abstract: Vehicle trajectory tracking using mobile devices includes receiving, from a vehicle in transit, over a time period associated with a portion of the transit, sensor data values from a plurality of mobile electronic devices aboard the vehicle. Vehicle trajectory tracking also includes generating vehicle state variables based at least on the sensor data values and applying statistical processing to the vehicle state variables to generate vehicle trajectory data.

Abstract: A device includes one or more processors configured to receive an indication that a geographical area is expected to be regulated during a particular time window. The one or more processors are also configured to perform a comparison of the geographical area to route segments of a filed flight plan of a flight of an aircraft. The one or more processors are further configured to, based on determining that a route segment of the filed flight plan traverses at least a portion of the geographical area during the particular time window, determine alternate flight plans that do not traverse the geographical area during the particular time window. The one or more processors are also configured to select one of the alternate flight plans that satisfies a selection criterion as an updated flight plan of the flight, and to send the updated flight plan to a second device.

Abstract: Apparatuses, methods, systems, and program products are disclosed for dynamic selection of an aeronautical data provider. An apparatus includes a processor and a memory that stores code executable by the processor to receive a first stream of real-time aeronautical data from a first aeronautical data provider, receive at least one second stream of real-time aeronautical data from at least one second aeronautical data provider simultaneously with the first stream, provide data from the first stream and the at least one second stream to a decision model for predicting which data has a higher accuracy, and switch using data from the first stream to data from one of the at least one second streams in response to the one of the at least one second streams having a higher predicted accuracy than the first stream.

Abstract: A system (1) is provided. The system comprises two primary sensor units (10) and two secondary sensor units (20). The secondary sensor units are configured to receive ultrasonic pulses during time windows, wherein a time window of the time windows comprises a corresponding transmit time of predetermined transmit times. The system is configured to determine a time-of-flight of an ultrasonic pulse of the ultrasonic pulses transmitted at a transmit time of the transmit times based on when the ultrasonic pulse was received during the corresponding time window. The system is further configured to determine a distance between two of the sensor units based on the determined time-of-flight between said sensor units. The system is configured to determine the positions of the sensor units in real-time based on measured movements and the determined distances.

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: 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: A method of flight data prediction for an aircraft includes receiving, at a device, arrival input data for a flight of the aircraft to an arrival airport. The arrival input data includes flight plans for a plurality of aircraft expected to arrive at or depart from the arrival airport during an arrival time range, predicted weather information for the arrival airport during the arrival time range, and facilities data for the arrival airport. The method includes generating, at the device based on the arrival input data, one or more arrival sets for the aircraft. Each arrival set comprising a predicted gate arrival time for the aircraft at an arrival gate using a particular runway and a probability associated with the aircraft using the particular runway. The method also includes generating, at the device, an output based on at least one of the one or more arrival sets.

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: A device for flight data prediction includes a memory, a communication interface, and one or more processors. The memory is configured to store a departure airport map. The communication interface is configured to receive location data for a plurality of aircraft expected to arrive at or depart from a departure airport during a particular time range. The one or more processors are configured to predict, based on the location data and the departure airport map, a taxi duration or a fuel usage of a first aircraft of the plurality of aircraft. The one or more processors are also configured to generate an output based on the taxi duration or the fuel usage.

Abstract: A parking stand prediction system uses statistical analysis and historical data to provide an available parking stand for an aircraft at an arrival airport. The system receives real-time surveillance data relating to airlines, on-air flights, and airline and airport ground services. The surveillance data is sorted or pre-filtered according to airport. The system also receives airport map or layout data. Parking stand identification logic is applied to the surveillance data, airport map data, and historical surveillance data. Portions of this data are stored in a parking stand database which is continuously updated with current surveillance and parking stand availability data. A user inputs parking stand search parameter data into the system and receives a parking stand prediction for the aircraft of interest at an arrival airport.

Abstract: A method and system for autonomously operating an aircraft. The method comprises a pre-flight training step comprising: retrieving recorded surveillance data of a plurality of flights corresponding to at least one aircraft type and at least one route; inferring aircraft intent from the recorded surveillance data; computing reconstructed trajectories using the inferred aircraft intent; selecting a training dataset comprising aircraft intent and reconstructed trajectories of flights corresponding to a particular aircraft type and route; and applying a machine learning algorithm on the training dataset to obtain a mapping function between aircraft states and actions.

Abstract: A computer-implemented method and a system for providing passenger information performing the tasks of sending, from a passenger electronic device (110), personal data, device geolocation and device time-stamp information; obtaining the passenger itinerary and confirming passenger identity, by a managing unit (206), based upon a match between the personal data, passenger electronic device geolocation and time-stamp; information contained in the obtained passenger itinerary; accessing to updated air traffic information relevant to the passenger upon confirming the passenger identity and sending, by the managing unit (206), a passenger report comprising flight status information for display on the passenger electronic device (110).

Abstract: A method and system for autonomously operating an aircraft. The method comprises a pre-flight training step comprising: retrieving recorded surveillance data of a plurality of flights corresponding to at least one aircraft type and at least one route; inferring aircraft intent from the recorded surveillance data; computing reconstructed trajectories using the inferred aircraft intent; selecting a training dataset comprising aircraft intent and reconstructed trajectories of flights corresponding to a particular aircraft type and route; and applying a machine learning algorithm on the training dataset to obtain a mapping function between aircraft states and actions.

Abstract: A computer-implemented method, system, and computer program product for managing passenger information. Communication between an electronic device of a user and an airline data server is established. The user is identified as a passenger for a departing flight at an airport. Flight status information associated with the passenger is retrieved via the airline data server. Passenger location information is retrieved with the electronic device. An estimated time for the passenger to reach the boarding gate is computed based on the passenger location information and the flight status information. A probability of the passenger missing the flight is computed based on the estimated time for the passenger to reach the boarding gate and the flight status information. A notification is produced according to the computed probability.

Abstract: A computer-implemented method and a system for providing passenger information performing the tasks of sending, from a passenger electronic device (110), personal data, device geolocation and device time-stamp information; obtaining the passenger itinerary and confirming passenger identity, by a managing unit (206), based upon a match between the personal data, passenger electronic device geolocation and time-stamp; information contained in the obtained passenger itinerary; accessing to updated air traffic information relevant to the passenger upon confirming the passenger identity and sending, by the managing unit (206), a passenger report comprising flight status information for display on the passenger electronic device (110).