# Air Traffic Trajectory Operation Mode Mining Based on Clustering
## 4 Simulation
### Validation of Density Peak Clustering Effect
The truncation distance of DPCA was chosen as \(d_{c}=3.8\), while the choice of the center metric parameter is more flexible and was determined by the number of target cluster classes. Combined with the original trajectory pattern, the number of target cluster classes here should be 5 or 6. Figure 13 shows the clustering effect when the number of clusters was 5. Figure 12: DTW + DBSCAN clustering result. Figure 13: OFAE + DPCA clustering result. The OFAE depolarization autoencoder mainly reconstructs the upper area of Figure 13 with a longitude greater than 114\({}^{\circ}\) and a latitude less than 22.5\({}^{\circ}\). It can be seen that after the removal of abnormal tracks and the condensation of normal tracks, the traffic flow potential in this region was more obvious, and DPCA could well-extract the potential traffic flow in this region. However, similar to DBSCAN, the red clusters on the right cannot be further subdivided. Even if the number of target clusters was set to 6, the separated red clusters were not the red clusters in the current Figure 13. This is due to the rough distance measurement. In fact, after compression and reconstruction, the overall trajectory tends to be more cohesive. At the same time, the DPCA algorithm is more flexible than DBSCAN, which is essentially the identification of the center of clusters, and it is not necessary to use the same scanning radius for each cluster class. Even if there are relatively similar cluster classes, DPCA can effectively distinguish them as long as different centers can be identified. Next, we attempted to use the DTW iterative distance measurement matrix for the reconstructed sample set, and to cluster with DPCA again to further reduce the truncation distance, \(d_{c}=3.2\), to separate the red cluster. The number of cluster centers was 6, and the clustering results in Figure 14 were obtained. It can be seen that traffic flows with different densities were successfully distinguished and extracted after compression and reconstruction of AE, fine measurement of DTW, and the DPCA clustering algorithm. As an example, we applied the standard instrument approach procedure and the area navigation approach procedure under the runway configuration of Baiyun Airport 01/02L/02R, as shown in Figure 15, and the light blue and yellow clusters and the aeronautical chart approach from the left and right sides. The number of flights arriving at the airport from the ATAGA and IGONO waypoints is large, the traffic flow is large, the track is more scattered, and the remaining clusters can accurately correspond to the approach procedures arriving from these two waypoints. It can be seen that the OFAE + DTW + DPCA algorithm proposed in this paper has strong robustness in the face of complex operating scenarios in the terminal area. Combined with the contour coefficient, the performance of each algorithm is summarized in Table 3.  
It can be seen that the combined algorithm of OFAE and DPCA took less time, had a better clustering effect than the traditional combined algorithm, and did not conflict with DTW. The reconstructed aggregation trajectory can also use fine distance measurement to achieve a better effect. Finally, the combined algorithm of OFAE + DTW + DPCA was successfully used to effectively extract each traffic flow. Figure 14: Final clustering result. Figure 15: Baiyun Airport runway 01/02L/02R standard arrival flight procedures. (**A**) Standard instrument approach procedure. (**B**) Regional area navigation arrival procedure.