Abstract: A system includes one or more control units configured to determine one or more fuel consumption models for an aircraft. A method includes determining, by one or more control units, one or more fuel consumption models for an aircraft. A non-transitory computer-readable storage medium comprising executable instructions that, in response to execution, cause one or more control units comprising a processor, to perform operations including determining one or more fuel consumption models for an aircraft.

Abstract: A computing device is provided comprising a processor and a memory storing instructions executable by the processor. The instructions are executable by the processor to monitor location information for a plurality of aircraft in a geographic region. Geospatial data is received for the geographic region. The geospatial data comprises, for each of one or more physical objects in the geographic region, a virtual representation of the physical object. The instructions are further executable to determine that the location information meets a threshold difference compared to expected location information for a selected virtual representation of a physical object. An indication of an anomaly is output based at least on the determination that the location information meets the threshold difference.

Abstract: A system includes one or more control units configured to determine one or more fuel consumption models for an aircraft. A method includes determining, by one or more control units, one or more fuel consumption models for an aircraft. A non-transitory computer-readable storage medium comprising executable instructions that, in response to execution, cause one or more control units comprising a processor, to perform operations including determining one or more fuel consumption models for an aircraft.

Abstract: The present disclosure provides for using machine learning to evaluate flight demographics and predict part consumption. Historical aircraft part consumption data indicating prior consumption of aircraft parts is accessed, and historical aircraft flight demographics associated with the historical aircraft part consumption data are determined. A machine learning model is trained based on the historical aircraft part consumption data and the historical aircraft flight demographics, and the machine learning model is deployed to predict future aircraft part consumption.

Abstract: Systems and methods for terminal procedure prediction for flight planning include obtaining, at a processor, input data corresponding to an upcoming flight of an aircraft; obtaining, at the processor, predicted data corresponding to the upcoming flight; and processing, at the processor, the input data and the predicted data to obtain a terminal procedure prediction associated with the upcoming flight, the terminal procedure prediction at least partially based on flight plans of historical flights having conditions similar to the input data and the predicted data.

Abstract: A method includes obtaining airport baseline data associated with an airport. The airport baseline data is descriptive of operational characteristics of the airport, infrastructure of the airport, or both. The method also includes modifying one or more parameters of the airport baseline data to generate candidate modification data. The method further includes providing model input data based on the candidate modification data as input to a trained machine learning model to generate forecast data indicating a predicted result of modification of the one or more parameters. The method also includes comparing the forecast data to one or more target values and generating a notification if the forecast data fails to satisfy the one or more target values.

Abstract: The present disclosure is directed to a method and system for monitoring and predicting greenhouse gas emissions for a flight of an aircraft. The system receives navigation and location data of a flight of an aircraft through one or more regions of travel such as an airspace. The system further obtains fuel consumption data of the flight of the aircraft through the one or more regions of travel. Using mathematical models, the system then determines the greenhouse gas emissions of the aircraft through the one or more regions of travel. The system then compares the greenhouse gas emissions of the aircraft with the navigation and location data to determine one or more correlations between the greenhouse gas emissions data and the one or more regions of travel. The system then outputs the correlations to various other systems (e.g., a display or machine learning system) for further analysis and processing.

Abstract: A method includes receiving a data source that includes information associated with one or more airports. The method also includes determining, using a first machine-learning model, a particular classification of the data source and scheduling information associated with the data source. The method further includes allocating, using a second machine-learning model, particular information in the data source to a particular airport. The particular airport is associated with a particular database, and the particular information is scheduled to be descriptive of a feature of the particular airport. The method also includes generating, using a third machine-learning model, an updated dataset based on the particular information. The current dataset is indicative of the feature of the particular airport. The method further includes updating the current dataset in the particular database with the updated dataset based on the scheduling information in response to a user verification.

Abstract: A method includes receiving, at one or more processors, airport runway geospatial data indicating a geospatial area associated with an airport runway and an altitude associated with the airport runway. The method also includes receiving aircraft location data associated with an aircraft. The aircraft location data indicates a position of the aircraft at different times and an altitude of the aircraft at different times. The method further includes determining whether the aircraft performed a touch-and-go operation on the airport runway based on the airport runway geospatial data and the aircraft location data. The touch-and-go operation includes the aircraft contacting the airport runway and subsequently taking off from the airport runway without stopping. The method also includes generating a notification in response to a determination that the aircraft performed the touch-and-go operation.

Abstract: The present disclosure provides for using machine learning to evaluate flight demographics and predict part consumption. Historical aircraft part consumption data indicating prior consumption of aircraft parts is accessed, and historical aircraft flight demographics associated with the historical aircraft part consumption data are determined. A machine learning model is trained based on the historical aircraft part consumption data and the historical aircraft flight demographics, and the machine learning model is deployed to predict future aircraft part consumption.

Abstract: A computing device is provided comprising a processor and a memory storing instructions executable by the processor. The instructions are executable by the processor to monitor location information for a plurality of aircraft in a geographic region. Geospatial data is received for the geographic region. The geospatial data comprises, for each of one or more physical objects in the geographic region, a virtual representation of the physical object. The instructions are further executable to determine that the location information meets a threshold difference compared to expected location information for a selected virtual representation of a physical object. An indication of an anomaly is output based at least on the determination that the location information meets the threshold difference.

Abstract: A method includes detecting, by a processing circuit, a high-speed rejected takeoff has occurred by determining an aircraft has accelerated to at least a first preset indicated airspeed value and then by determining the aircraft has decelerated below at least a second preset indicated airspeed value and the aircraft is on the ground. The method also includes detecting, by the processing circuit, an event other than the high-speed rejected takeoff has occurred by determining the aircraft has not accelerated to at least the first preset indicated airspeed value, or by determining the aircraft has accelerated to at least the first preset indicated airspeed value and the aircraft has not decelerated below at least the second preset indicated airspeed value, or by determining the aircraft is airborne.

Abstract: A method includes detecting, by a processing circuit, a high-speed rejected takeoff has occurred by determining an aircraft has accelerated to at least a first preset indicated airspeed value and then by determining the aircraft has decelerated below at least a second preset indicated airspeed value and the aircraft is on the ground. The method also includes detecting, by the processing circuit, an event other than the high-speed rejected takeoff has occurred by determining the aircraft has not accelerated to at least the first preset indicated airspeed value, or by determining the aircraft has accelerated to at least the first preset indicated airspeed value and the aircraft has not decelerated below at least the second preset indicated airspeed value, or by determining the aircraft is airborne.

Abstract: A system for determining a turnaround time for an aircraft at an airport includes one or more processors, one or more geospatial databases storing geospatial data, and a memory coupled to the one or more processors and the one or more geospatial databases. The memory stores data into a database and program code that, when executed by the one or more processors, causes the system to monitor a wireless data stream indicating aircraft location data. In response to determining the aircraft is idle and is located within an area where a unique parking stand is located, the system establishes a current position timestamp as an on-block time. In response to determining the aircraft is moving out of the unique parking stand, the system determines a position timestamp when the aircraft was last idle, and sets the position timestamp collected when the aircraft was last idle as an off-block time.