Abstract: The disclosure herein describes use of an air corridor model to enable aerial vehicles to travel an air corridor while maintaining safe separation distance from other vehicles. A trained air corridor model associated with an air corridor is received by an aerial vehicle, wherein the trained air corridor model is configured to maintain safe separation distance between vehicles in the air corridor. The current position of the aerial vehicle in the air corridor is identified and positions of other agents in the air corridor are received. A next position of the aerial vehicle is determined based on the trained air corridor model, the current position, and the agent positions. The aerial vehicle is controlled to travel from the current position to the determined next position, whereby the aerial vehicle and other agents are enabled to efficiently use the airspace of the air corridor while maintaining safe distances between each other.

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: The disclosure herein describes use of an air corridor model to enable aerial vehicles to travel an air corridor while maintaining safe separation distance from other vehicles. A trained air corridor model associated with an air corridor is received by an aerial vehicle, wherein the trained air corridor model is configured to maintain safe separation distance between vehicles in the air corridor. The current position of the aerial vehicle in the air corridor is identified and positions of other agents in the air corridor are received. A next position of the aerial vehicle is determined based on the trained air corridor model, the current position, and the agent positions. The aerial vehicle is controlled to travel from the current position to the determined next position, whereby the aerial vehicle and other agents are enabled to efficiently use the airspace of the air corridor while maintaining safe distances between each other.

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.