Air Traffic Trajectory Operation Mode Mining Based on Clustering
Abstract
:1. Introduction
2. Data Analysis and Processing
2.1. Data Analysis
2.2. Trajectory Correction Based on Operational Characteristics
3. Clustering Analysis-Based Aircraft Operation Pattern Mining
3.1. Trace Feature Compression
3.1.1. Autoencoder of Trajectory Based on L21-Norm Constraints
3.1.2. ADMM-Based Model Solving Algorithm
3.2. Historical Track Clustering
4. Simulation
4.1. Experimental Design
4.2. Validation of Density Peak Clustering Effect
5. Conclusions
- (1)
- In the cluster analysis, a combined model of OFAE + DPCA was proposed to make up for the problem that DTW is time-consuming and DBSCAN finds it difficult to distinguish clusters with large density differences in the traditional model, DTW + DBSCAN. The track compressed by OFAE can directly use the Euclidean distance measurement. DPCA can extract the density peak and directly classify the adjacent tracks. The combination of the two greatly saves time.
- (2)
- In the process of solving the sparse autoencoder, the non-convex optimization objective was processed by convex relaxation, which was transformed into the optimization problem of L21-norm, and the ADMM algorithm was used for the step-by-step solution. Compared with the Lagrange solution with strong constraints, the ADMM solution allowed the model to deviate from the constraints to a certain extent while reducing the error function. When the deviation was limited, it was more conducive to the optimal solution.
- (3)
- In future research, we will consider high-quality datasets obtained by classifying and identifying typical aircraft operating modes and establish a track prediction model to make the prediction results more targeted. In the face of abnormal mode data with few similar samples at the same time, it can also provide a comprehensive prediction based on similar patterns and improve the accuracy of track prediction.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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TFRN | Data Item | Information | Length |
---|---|---|---|
1 | 1062/010 | Data Source Identifier | 2 |
2 | - | Spare | - |
3 | 1062/015 | Service Identification | 1 |
4 | 1062/070 | Time of Track Information | 3 |
5 | 1062/105 | Calculated Track Position (WGS-84) | 8 |
6 | 1062/100 | Calculated Track Position (Cartesian) | 6 |
7 | 1062/185 | Calculated Track Velocity (Cartesian) | 4 |
FX | - | Field Extension Indicator | - |
8 | 1062/210 | Calculated Acceleration (Cartesian) | 2 |
9 | 1062/060 | Track Mode 3/A Code | 2 |
10 | 1062/245 | Target Identification | 7 |
11 | 1062/380 | Aircraft-Derived Data | 1+ |
12 | 1062/040 | Track Number | 2 |
13 | 1062/080 | Track Status | 1+ |
14 | 1062/290 | System Track Update Ages | 1+ |
FX | - | Field Extension Indicator | - |
1 | 25,751.42 | 23.38082 | 113.3027 | NA | 0 | −5 | NA | 0 | B763 | NA | NA |
2 | 25,751.42 | 23.38082 | 113.3027 | NA | 0 | −5 | NA | 0 | B763 | NA | NA |
3 | 25,751.73 | 23.3808 | 113.3027 | NA | 0 | −5 | NA | 0 | B763 | NA | NA |
4 | 25,751.73 | 23.3808 | 113.3027 | NA | 0 | −5 | NA | 0 | B763 | NA | NA |
5 | 25,751.42 | 23.30876 | 113.3027 | NA | 0 | −5 | NA | 0 | B763 | NA | NA |
6 | 25,751.42 | 23.30876 | 113.3027 | NA | 0 | −5 | NA | 0 | B763 | NA | NA |
7 | 25,751.73 | 23.30875 | 113.3027 | NA | −1 | −5 | NA | 0 | B763 | NA | NA |
8 | 25,751.73 | 23.30875 | 113.3027 | NA | −1 | −5 | NA | 0 | B763 | NA | NA |
9 | 25,751.42 | 23.30871 | 113.3027 | NA | −1 | −5 | NA | 0 | B763 | NA | NA |
10 | 25,751.42 | 23.30871 | 113.3027 | NA | −1 | −5 | NA | 0 | B763 | NA | NA |
11 | 25,751.73 | 23.3087 | 113.3026 | NA | −1 | −5 | NA | 0 | B763 | NA | NA |
12 | 25,751.73 | 23.3087 | 113.3026 | NA | −1 | −5 | NA | 0 | B763 | NA | NA |
13 | 25,751.42 | 23.30866 | 113.3026 | NA | −1 | −6 | NA | 0 | B763 | NA | NA |
14 | 25,751.42 | 23.30866 | 113.3026 | NA | −1 | −6 | NA | 0 | B763 | NA | NA |
15 | 25,751.73 | 23.30866 | 113.3026 | NA | −1 | −5 | NA | 0 | B763 | NA | NA |
16 | 25,751.73 | 23.30866 | 113.3026 | NA | −1 | −5 | NA | 0 | B763 | NA | NA |
Algorithm | Contour Factor | Algorithm Time Consumption | Effectiveness Evaluation |
---|---|---|---|
DTW + DBSCAN | 0.46 | High | It is not possible to distinguish between similar cluster classes and extracted low-density cluster classes. |
OFAE + DPCA | 0.65 | Low | Unable to distinguish between similar cluster classes. |
OFAE + DTW + DPCA | 0.73 | High | Effectively extracted |
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Tang, X.; Liu, Y.; Chen, K. Air Traffic Trajectory Operation Mode Mining Based on Clustering. Appl. Sci. 2022, 12, 5944. https://doi.org/10.3390/app12125944
Tang X, Liu Y, Chen K. Air Traffic Trajectory Operation Mode Mining Based on Clustering. Applied Sciences. 2022; 12(12):5944. https://doi.org/10.3390/app12125944
Chicago/Turabian StyleTang, Xinmin, Yusheng Liu, and Kefan Chen. 2022. "Air Traffic Trajectory Operation Mode Mining Based on Clustering" Applied Sciences 12, no. 12: 5944. https://doi.org/10.3390/app12125944