# Airport Field Path Optimization Method Based on Conflict Hotspot Avoidance Mechanism
## 4 Example Analysis  
Shenzhen Bao'an International Airport is selected for the validation analysis, and the airport field topology structure map is shown in Figure 6. Combined with the historical data, the potential conflict hotspot areas are numbered 20, 23, 27, 40, 43, 58-59, 61-65, 69-70, 72, 78, 98, 106, 108-109, 111, 124, 135, 143, and 194, with the historical flight data of January 2022 at Shenzhen Bao'an Airport as the basis for the subsequent validation analysis. Obtain 1 month of traffic data of the potential conflict hotspot area, and take 15 min as the step segment. Take the average 15 min traffic data \(V_{1}\), use the principal component analysis method, combined with the degree centrality \(V_{2}\), median centrality \(V_{3}\), proximity centrality \(V_{4}\), eigenvector centrality \(V_{5}\), the loss of network efficiency \(V_{6}\), the loss of the largest subgraphs \(V_{7}\), the number of conflicts than the number of \(V_{8}\), and other indicators, to find out the risk index. The specific methods of calculation are shown in Equations (21)-(28), where \(N\) is the number of nodes, \(D_{i}\) is the degree of nodes \(i\), \(l_{jk}(i)\) is the number of shortest paths from node \(j\) to node \(k\) through node \(i\), and \(l_{jk}\) is the total number of shortest paths from node \(j\) to node \(k\). \(d_{ij}\) is the shortest distance from node \(i\) to node \(j\), \(\lambda_{\max}\) is the maximal eigenvalue of the network adjacency matrix \(A\), and its corresponding maximal eigenvector is \(\mathrm{x}=[x_{1},x_{2},\cdots,x_{n}]^{T}\), \(E\) is the network efficiency, and \(E_{i}\) is the new network efficiency after the deletion of node \(i\) and its connected edges. \(N^{\prime}\) is the number of nodes contained in the maximum connected subgraph of the original network, \(N^{\prime}_{i}\) and is the number of nodes contained in the maximum connected subgraph after the deletion of node \(i\). \(C_{i}\) is the number of abnormal active targets passed by a node \(i\) in a given period, \(\widehat{N}\) is the total number of active targets in the field topology network in a given period, and \(CI\) is the risk index [34]. The flow and risk index are used as input features. The output feature is the predicted value of the risk index, which is used to train the model and output the results. The results are obtained as shown in Figure 7, which are divided into four intervals of [0.1-0.6], [0.6-0.9], [0.9-1.2], and [1.2-1.5] according to the risk index, which indicates the four risk levels--the low likelihood of conflict, the average likelihood of conflict, the high likelihood of conflict--which are represented by labels of  
Figure 6: Shenzhen Bao’an International Airport field topography structure plan. different colors. From Figure 7, it can be seen that hotspot 23 maintains a high conflict risk over a long period, and hotspots 62 and 64 maintain a high conflict risk during certain hours of the day, so hotspots 23, 62, and 64 can be considered as critical conflict hotspot areas, while hotspots 108 and 109 have conflicts occurring during some periods, but they maintain them for a short period and have a low risk level, so they cannot be recognized as critical hotspot areas. \[V_{2}=\frac{D_{i}}{N-1} \tag{21}\]  
\[V_{3}=\sum_{i\
eq j\
eq k}\frac{l_{jk}(i)}{l_{jk}} \tag{22}\]  
\[V_{4}=\frac{N-1}{\sum\limits_{j\
eq 1}^{N}d_{ij}} \tag{23}\]  
\[V_{5}=x_{i}=\frac{1}{\lambda_{\max}}\sum\limits_{j=1}^{n}\mathbf{A}_{ij}x_{j} \tag{24}\]  
\[\left\{ \begin{array}{l}E=\frac{1}{N(N-1)}\sum\limits_{i\
eq j}\frac{1}{d_{ ij}}\\ V_{6}=\frac{E-E_{i}}{E}\end{array} \right. \tag{25}\]  
\[V_{7}=\frac{N^{\prime}-N_{i}^{\prime}}{N^{\prime}} \tag{26}\]  
\[V_{8}=\frac{C_{i}}{N} \tag{27}\]  
\[CI=0.159V_{1}+0.118V_{2}+0.139V_{3}+0.088V_{4}+0.147V_{5}+0.128V_{6}+0.068V_{7 }+0.153V_{8} \tag{28}\]  
According to the above experimental results, the key hotspot areas 23, 62, and 64 are obtained.