Alternate Flight Plans Using Deviation Prediction and Route Clustering
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Solution Overview
Problem
Existing air traffic management systems rely on static flight plans that fail to adapt to real-time variations in weather, traffic, and other factors, leading to inefficiencies, increased fuel consumption, and potential delays due to manual interventions.
Innovation Solution
A system that generates alternate flight paths using historical flight data to predict deviations from submitted plans, clustering similar horizontal and vertical flight paths, and providing efficient alternate routes based on real-time information and efficiency criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predetermined static flight plans are used, then flight plan simplicity is maintained, but adaptability to real-time variations in weather, traffic, and other factors deteriorates
Solution Approach 1:
The system transitions from static predetermined flight plans to dynamic flight plans that automatically adapt to real-time variations in weather, traffic, and other factors. The dynamic flight plan system continuously monitors conditions and adjusts the flight path, altitude, and speed to optimize performance while maintaining simplicity through automated processes.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring real-time weather, traffic, and flight conditions, then using this information to automatically adjust flight plans. This closed-loop approach ensures adaptability to changing conditions while reducing the need for manual intervention and maintaining system simplicity through automated decision-making.
2Use of energy by moving object
If static models are used for flight planning, then system simplicity is maintained, but fuel efficiency deteriorates due to suboptimal routes
Solution Approach 1:
The system uses dynamic optimization to continuously adjust flight parameters (route, altitude, speed) based on real-time conditions, enabling fuel-efficient routing without requiring complex manual planning. The automated dynamic adjustment process achieves optimal fuel consumption while maintaining system simplicity through algorithm-driven decision-making.
Solution Approach 2:
The system optimizes fuel consumption by dynamically changing flight parameters including route coordinates, altitude levels, and speed profiles based on real-time weather, traffic, and aircraft performance data. These parameter adjustments are automatically calculated to minimize fuel consumption while maintaining operational simplicity through centralized control.
3Loss of time
If pre-defined flight plans are used, then initial planning simplicity is maintained, but timeliness deteriorates due to manual intervention requirements
Solution Approach 1:
The system implements self-service capabilities by automatically detecting changes in weather, traffic, and other conditions, then autonomously generating and implementing alternative flight plans without requiring manual pilot or controller intervention. This automated self-adjustment significantly reduces response time to unforeseen changes while the system complexity is managed through standardized algorithms and procedures.
Solution Approach 2:
The system performs preliminary actions by pre-calculating multiple alternative flight paths and parameters before departure and during flight, so that when changes occur, the system can immediately implement the most appropriate pre-computed alternative. This advance preparation enables rapid response to unforeseen conditions while maintaining operational simplicity through pre-planned options.
4Productivity
If manual air traffic control adjustments are made, then real-time adaptability is improved, but productivity deteriorates due to manual intervention delays
Solution Approach 1:
The system replaces manual air traffic control adjustments with automated self-service capabilities that continuously monitor conditions and automatically implement optimal flight plan modifications. This eliminates manual intervention delays and improves flight operations efficiency while the standardized automated processes maintain ease of operation through user-friendly interfaces and automated decision-making.
Solution Approach 2:
The system substitutes manual mechanical operations (pilot and controller actions) with automated electronic systems that calculate and implement flight plan adjustments in real-time. This replacement of manual operations with automated computing significantly improves productivity and response time while the system maintains ease of operation through automated interfaces and standardized procedures.
Data Source
AI summary
A device includes one or more processors configured to determine a probability that an airplane will follow a submitted flight path. The one or more processors are further configured to in response to a determination that the probability is less than a threshold, provide one or more alternate flight paths to a device.