Semantic Airport Surface Trajectory Representation for Outlier Detection
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Solution Overview
Problem
Airport surface traffic management faces challenges in accurately representing aircraft taxiing paths due to incomplete data, noise, and complexity, making it difficult to detect outliers and ensure safety and efficiency.
Innovation Solution
The method involves converting digital surface map topology information into semantic representations of aircraft taxiing paths, using road segment geographic information to calculate similarity and identify patterns through hierarchical clustering, and detecting outliers by comparing input paths against established patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simplified representations of aircraft taxiing paths are used, then processing efficiency is improved, but measurement precision and reliability of outlier detection deteriorate
Solution Approach 1:
The patent segments the taxiing path into discrete road segments from the airport surface map, creating a structured representation that balances simplicity with precision. Each road segment serves as a discrete unit that can be efficiently processed while maintaining the geometric and topological characteristics necessary for accurate outlier detection.
Solution Approach 2:
The patent transforms the continuous taxiing path coordinates into discrete road segment identifiers and introduces semantic parameters (entry/exit points, direction, connected segments) that enable efficient processing while preserving the information needed for precise outlier detection through pattern matching.
2Loss of information
If manual designation or clustering is performed on taxiing path points, then semantic path representation is achieved, but loss of information occurs due to data missing and deviation
Solution Approach 1:
The patent introduces the airport surface map as an intermediary reference framework. Instead of directly processing noisy raw taxiing path points, the system uses the map's road segments as a mediator to represent the path, filtering out noise and data gaps while maintaining information completeness through the map's structured geometry.
Solution Approach 2:
The patent performs preliminary actions by pre-establishing the airport surface map with all road segments, intersections, and taxiways before processing taxiing paths. This pre-structured framework enables subsequent path representations to directly reference known geometric entities, avoiding the need for complex post-processing to fill data gaps or correct deviations.
3Manufacturing precision
If manual labeling of taxiing paths is performed, then path representation is obtained, but manufacturing precision deteriorates due to accuracy errors in manual labeling
Solution Approach 1:
The system uses automated algorithms to perform what would otherwise require manual labeling. The taxiing path is automatically represented by matching recorded aircraft positions against the airport surface map, eliminating manual intervention and its associated accuracy errors while maintaining operational simplicity through automated processing.
4Reliability
If real-time outlier detection is implemented, then safety is improved, but device complexity increases due to pattern recognition requirements
Solution Approach 1:
The patent segments both the airport surface map and taxiing paths into discrete road segments, enabling efficient comparison and pattern recognition. This segmentation allows real-time detection by breaking down complex path matching into simpler segment-by-segment comparisons against stored patterns.
Solution Approach 2:
The system dynamically processes taxiing paths by continuously comparing incoming path data against the pre-established airport surface map and stored patterns. This dynamic approach enables real-time outlier detection while keeping complexity manageable through efficient data structures and comparison algorithms.
Data Source
AI summary
An airport surface semantic path representation and an outlier taxiing pattern detection method for airport operation management, semantic conversion is realized based on dynamic features in an aircraft taxiing process, and after combining with surface map topology information, an airport surface aircraft semantic path representation is obtained; road segment geographic information is used to allocate a road segment weight to improve a longest common sub-sequence method, thereby constructing a flight path similarity matrix and using hierarchical clustering to realize surface taxiing pattern recognition; finally, by calculating the internal similarity of the identified patterns, the maximum outlier for identifying outlier patterns is obtained; after calculating the similarity between subsequent input flight paths and each pattern, as well as comparing a corresponding outlier with the maximum reachable outlier, the prewarning of outlier flight paths is achieved, the safety of surface management is ensured, and the effective surface control is realized.