Flight Data Prediction for Taxi Duration and Fuel Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current fuel loading for aircraft is inefficient due to reliance on estimated taxi times that do not account for real-time conditions, leading to excessive fuel consumption and weight, which can be mitigated by predicting taxi durations and fuel usage based on actual airport conditions.
Innovation Solution
A device and method that utilize a departure and arrival airport taxi duration data model, incorporating flight plans, weather conditions, and aircraft information to predict taxi times and fuel usage, with the ability to update models based on detected taxi times for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pilots add extra fuel to the fuel usage estimate to ensure sufficient fuel for the flight, then the reliability of fuel sufficiency is improved, but the weight of the aircraft increases resulting in higher fuel consumption
Solution Approach 1:
The system implements feedback by continuously monitoring real-time taxi conditions, aircraft location, and actual taxi time, then using this information to dynamically adjust the fuel usage estimate. The prediction system compares estimated versus actual taxi parameters and refines future estimates based on this feedback loop, reducing the need for excessive fuel margins while maintaining reliability.
Solution Approach 2:
The patent transitions from static fuel estimation based on fixed averages to dynamic estimation that adapts to real-time conditions. The system continuously updates taxi time predictions based on current airport conditions, aircraft movement data, and historical patterns, allowing the fuel estimate to dynamically adjust to actual operational requirements rather than relying on conservative fixed margins.
2Ease of operation
If pilots use fixed taxi time average or prior experience to estimate taxi time, then the ease of operation is improved, but the measurement precision of taxi time estimate deteriorates
Solution Approach 1:
The system introduces an intermediary prediction system that acts between the pilot's simple estimation need and the complex reality of varying taxi conditions. This intermediary automatically processes multiple data sources including real-time aircraft location, airport map data, weather conditions, and historical taxi patterns to generate precise predictions without requiring pilot complexity, maintaining ease of operation while improving accuracy.
Solution Approach 2:
The patent replaces the manual mechanical estimation process (pilot experience and judgment) with an automated computational system. The prediction system uses algorithmic processing of location data, airport maps, and operational conditions to substitute human estimation with machine-based calculation, achieving higher precision while requiring minimal pilot intervention.
3Measurement precision
If the system predicts taxi duration and fuel usage based on real-time conditions and airport maps, then the measurement precision of fuel usage estimate is improved, but the device complexity increases
Solution Approach 1:
The prediction system is designed as a multi-functional integrated platform that simultaneously performs multiple tasks: tracking aircraft location, processing airport map data, analyzing weather conditions, calculating taxi time predictions, and generating fuel usage estimates. By consolidating these functions into a single universal system, the patent manages complexity while delivering comprehensive precise predictions across all operational parameters.
Solution Approach 2:
The system performs preliminary actions by pre-loading airport map data, pre-processing historical taxi patterns, and pre-establishing prediction models before actual taxi operations begin. This preliminary preparation allows the system to quickly generate accurate predictions during real-time operations without requiring complex processing during critical decision windows, effectively managing computational complexity.
Data Source
AI summary
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.