En Route Rerouting Probability Analysis
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Solution Overview
Problem
Current air traffic routing systems, such as the NASA Dynamic Weather Routing tool, lack advanced optimization strategies for selecting the most efficient reroutes, often relying on selecting the first available route that meets a preset savings threshold, which may overlook higher savings opportunities and impose opportunity costs by not considering the probability of route acceptance and complex negotiation strategies.
Innovation Solution
A system that determines the probability of route acceptance based on factors like reroute complexity, controller workload, historical route frequency, and constraint proximity, using decision tree analyses to optimize rerouting decisions and convert time savings into fuel savings, while allowing for multiple reroute strategies and air-ground negotiations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the first available route meeting a preset savings threshold is selected, then the routing process is simple and quick, but higher savings opportunities may be overlooked and opportunity costs are incurred
Solution Approach 1:
The system pre-calculates and stores multiple candidate reroutes with their associated savings metrics before a routing decision is needed. When a reroute opportunity arises, the system has already prepared several viable options with computed savings, allowing for rapid selection of the optimal route without performing complex calculations at the moment of decision.
Solution Approach 2:
The routing system dynamically adjusts the selection criteria based on real-time conditions such as controller workload, weather forecasts, and aircraft performance. Instead of using a fixed threshold, the system adapts the savings threshold and selection parameters dynamically to balance between quick decision-making and capturing maximum savings opportunities.
2Reliability
If multiple factors like reroute complexity, controller workload, and historical frequency are considered, then the route acceptance probability is optimized, but the system complexity increases
Solution Approach 1:
The system segments the complex decision-making process into distinct modular components: a savings calculation module that computes potential fuel/time savings, a probability estimation module that assesses acceptance likelihood based on multiple factors, and a selection module that integrates these inputs. Each module handles specific calculations independently, making the overall complex system manageable and maintainable.
Solution Approach 2:
The system introduces an intermediary probability estimation layer between the raw routing options and the final selection. This intermediary component synthesizes multiple complex factors (controller workload, weather, historical data) into a single probability score, simplifying the decision process while maintaining high reliability through comprehensive factor consideration.
3Reliability
If conservative routing around forecasted weather is used, then flight safety is maintained, but route inefficiencies and increased fuel consumption occur
Solution Approach 1:
The system changes the parameter used for weather avoidance from fixed conservative buffers to dynamic, probability-based margins. Instead of always routing a fixed distance around forecasted weather, the system adjusts the avoidance margin based on weather confidence levels, aircraft performance, and real-time conditions, allowing safer and more efficient routes when appropriate.
Solution Approach 2:
The system performs preliminary weather impact assessments and prepares alternative routes with optimized weather avoidance strategies before departure. By pre-evaluating weather patterns and preparing efficient avoidance routes that balance safety and fuel consumption, the system reduces unnecessary fuel burn while maintaining safety standards.
Data Source
AI summary
A system is disclosed for providing rerouting information based, in part, on a probability of route acceptance. In accordance with further embodiments, the rerouting information is based, in part, on decision tree analyses involving decisions to request and decisions to not request a reroute.