A system and computer implemented method for predicting a flight arrival time of a given aircraft flight, between an origin airport and a destination airport, of a given aircraft based on a set of features is disclosed. The method comprises determining a predicted time delay of a flight departure time of the given aircraft flight from the origin airport by processing a first plurality of features of the set of features using a first predictive model; determining a predicted time duration of the given aircraft flight from the origin airport to an entry point of a standard terminal arrival route for the destination airport; and determining a predicted time duration of the given aircraft flight from the entry point of the standard terminal arrival route for the destination airport to landing on a runway of the destination airport.

A system and computer implemented method for predicting a flight arrival time of a given aircraft flight, between an origin airport and a destination airport, of a given aircraft based on a set of features is disclosed. The method comprises determining a predicted time delay of a flight departure time of the given aircraft flight from the origin airport by processing a first plurality of features of the set of features using a first predictive model; determining a predicted time duration of the given aircraft flight from the origin airport to an entry point of a standard terminal arrival route for the destination airport; and determining a predicted time duration of the given aircraft flight from the entry point of the standard terminal arrival route for the destination airport to landing on a runway of the destination airport.

Various embodiments of the present disclosure provide techniques for avoiding wake turbulence during flight take-off operations and during flight landing operations. The techniques may include receiving first flight take-off operational data associated with a first flight take-off operation, the first flight take-off operational data comprising departure path data and take-off location data for the first flight take-off operation; determining, based on one or more of the departure path data or take-off location data for the first flight take-off operation, optimal flight take-off operational data for a second flight take-off operation following the first flight take-off operation, the optimal flight take-off operational data for the second flight take-off operation comprising one or more of (i) predicted departure path data or (ii) predicted take-off location data for the second flight take-off operation; and providing the optimal flight take-off operational data for performance of the second flight take-off operation.

Various embodiments of the present disclosure provide techniques for avoiding wake turbulence during flight take-off operations and during flight landing operations. The techniques may include receiving first flight take-off operational data associated with a first flight take-off operation, the first flight take-off operational data comprising departure path data and take-off location data for the first flight take-off operation; determining, based on one or more of the departure path data or take-off location data for the first flight take-off operation, optimal flight take-off operational data for a second flight take-off operation following the first flight take-off operation, the optimal flight take-off operational data for the second flight take-off operation comprising one or more of (i) predicted departure path data or (ii) predicted take-off location data for the second flight take-off operation; and providing the optimal flight take-off operational data for performance of the second flight take-off operation.

A moving body operation management device makes safe and efficient take-off and landing possible when a plurality of drones are approaching one take-off and landing port. Moving bodies are loaded with a conveyance target which is an article or a person, and move from a departure location to an arrival location in accordance with an instructed route. A movement time prediction calculation unit calculates the movement times of the moving bodies; and an occupation probability calculation unit calculates the probability that a take-off and landing port which the moving bodies take off from and land at will be occupied by the moving bodies on the basis of movement time uncertainty, which is calculated using information that affects the operation of the moving bodies. A usage plan is optimized by an optimization unit so that the take-off and landing port will be occupied by the moving bodies at all times.

A moving body operation management device makes safe and efficient take-off and landing possible when a plurality of drones are approaching one take-off and landing port. Moving bodies are loaded with a conveyance target which is an article or a person, and move from a departure location to an arrival location in accordance with an instructed route. A movement time prediction calculation unit calculates the movement times of the moving bodies; and an occupation probability calculation unit calculates the probability that a take-off and landing port which the moving bodies take off from and land at will be occupied by the moving bodies on the basis of movement time uncertainty, which is calculated using information that affects the operation of the moving bodies. A usage plan is optimized by an optimization unit so that the take-off and landing port will be occupied by the moving bodies at all times.

Unmanned Aerial Vehicles also known as UAVs or Drones, either autonomous or remotely piloted, are classified as drones by the US Federal Aviation Administration (FAA) as weighing under 212 pounds. The system described herein details Autonomous Flight Vehicles (AFV) which weigh over 212 pounds but less than 1,320 pounds which may require either a new classification or a classification such as Sport Light Aircraft, but without the requirement of a pilot due to the safe autonomous flight system such as the Safe Temporal Vector Integration Engine or STeVIE. Safe Autonomous Light Aircraft (SALA) are useful as drone carriers, large scale air package or cargo transport, and even human transport depending on the total lift capability of the platform.

Unmanned Aerial Vehicles also known as UAVs or Drones, either autonomous or remotely piloted, are classified as drones by the US Federal Aviation Administration (FAA) as weighing under 212 pounds. The system described herein details Autonomous Flight Vehicles (AFV) which weigh over 212 pounds but less than 1,320 pounds which may require either a new classification or a classification such as Sport Light Aircraft, but without the requirement of a pilot due to the safe autonomous flight system such as the Safe Temporal Vector Integration Engine or STeVIE. Safe Autonomous Light Aircraft (SALA) are useful as drone carriers, large scale air package or cargo transport, and even human transport depending on the total lift capability of the platform.

A flight support system is a flight support system that supports flight of an aircraft. The flight support system includes: a database that stores flight management manuals each of which includes before-takeoff check items that are different among airframe types; and processing circuitry connected to the database. The processing circuitry is configured to: receive a takeoff permission request of a target aircraft from a user terminal; acquire the airframe type of the target aircraft; acquire actual data regarding the before-takeoff check item corresponding to the target aircraft; refer to the database and compare the actual data with the before-takeoff check item of the flight management manual corresponding to the airframe type of the target aircraft; and transmit output information to the user terminal, the output information including information regarding a result of the comparison.

A flight support system is a flight support system that supports flight of an aircraft. The flight support system includes: a database that stores flight management manuals each of which includes before-takeoff check items that are different among airframe types; and processing circuitry connected to the database. The processing circuitry is configured to: receive a takeoff permission request of a target aircraft from a user terminal; acquire the airframe type of the target aircraft; acquire actual data regarding the before-takeoff check item corresponding to the target aircraft; refer to the database and compare the actual data with the before-takeoff check item of the flight management manual corresponding to the airframe type of the target aircraft; and transmit output information to the user terminal, the output information including information regarding a result of the comparison.