# Airport Field Path Optimization Method Based on Conflict Hotspot Avoidance Mechanism
## 4 Example Analysis
The actual operation data of Shenzhen Airport on 17 January 2022, are used for the simulation experiments, and the inbound and outbound flight data of the airport flight area from 17:00 to 18:00 for one hour are selected, including the flight number, the aircraft type, the inbound and outbound, the use of the runway, and the stopping position, as well as the corresponding node number of the field network and the start of the taxi  
Figure 7: Predicted results of the risk index for each hotspot region. According to the field aircraft operation specification, set the field taxiing speed of the aircraft as 50 km/h and the field taxiing interval as 40 s. According to the flight base data, set the population size as 100, the maximum number of iterations as 150, the crossover probability constant \(k_{1}=k_{3}=1.0\), and the variance probability constant \(k_{2}=k_{4}=0.5\). To validate the effectiveness of the algorithm, the change of the fitness value in the process of algorithm evolution is analyzed, and the change of the fitness value is shown in Figure 8. From the figure, it can be seen that after evolution to 30 generations, the adaptation degree begins to increase; after evolution to 80 generations, the range of variation of the adaptation degree value begins to narrow; after evolution to 120 generations, the adaptation degree value is close to convergence and tends to stabilize. Without considering the conflicts between aircraft and the influence of critical conflict hotspot areas, a static path planning algorithm is used to plan a pre-gliding path for each aircraft and determine whether there are any potential conflicts in the subsequent gliding process. Under the condition of no conflict resolution, some of the aircraft taxi paths and path sequence crossing moments are shown in Table 2, where the aircraft crossing moments are recorded using seconds. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Serial** & **Flight** & **Aircraft** & **Inbound/** & **Runway/Node** & **Parking Spot/** & **Start of** \\
**Number** & **Number** & **Type** & **Outbound** & **Number** & **Node Number** & **Taxing Time** \\ \hline
1 & CES5758 & B738 & D & 33/10 & 354/170 & 17:00:58 \\
2 & EPA6282 & B738 & A & 34/96 & 336/164 & 17:02:46 \\
3 & SNG4345 & A320 & A & 33/10 & 503/155 & 17:05:57 \\
4 & CQH8882 & A320 & D & 33/10 & 318/173 & 17:05:57 \\
5 & CXA8317 & B738 & A & 33/10 & 566/156 & 17:09:02 \\  
A4314 & A332 & A & 33/10 & 567/157 & 17:58:56 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Shenzhen Airport flight data. According to the data in the table, it can be found that:  
(1) Aircraft CES2558, CES5348, and CSN6310 occupy the critical hotspot area HS64 successively, the taxiing time of inbound aircraft CES2558 is 293 s, the taxiing time of departing aircraft CES5348 is 315 s, and the taxiing time of departing aircraft CSN6310 is 276 s. Therefore, the aircraft CES5348 has the longest taxiing time, so the aircraft is given the priority to pass the hotspot area; the inbound aircraft CES2558 occupies the hotspot area first, and the cost of its deceleration and waiting strategy is much larger than that of the alternative path, so the aircraft CES2558 needs to choose the suboptimal alternative path to avoid the hotspot area HS64. (2) Aircraft CES5348 occupies the critical conflict hotspot area HS64 at the passing moment 2764, while aircraft CSN6310 also occupies the hotspot area at the passing moment 2791, and does not satisfy the field glide interval of the 40 s, so there is a risk of tailgating conflict between these two aircraft at the hotspot area HS64. (3) Aircraft CSN3210 occupies the critical conflict hotspot area HS23 at the passing moment 3676, while aircraft CCA4314 will also pass through the hotspot area at the passing moment 3696, and both of them do not meet the field taxi interval of 40 s, so there is a risk of tailgating conflict.