# Airport Field Path Optimization Method Based on Conflict Hotspot Avoidance Mechanism
## 1 Introduction
Some scholars consider the maximum traversal time constraints, turn penalties, and other cost functions of the improved A* algorithm, solve the aircraft glide path optimization model based on the improved A* algorithm, and combine with the sliding time window to achieve intelligent conflict identification and relief, and obtain a conflict-free and smooth glide path [9; 10; 11; 12]. In addition, in 2019, [PERSON] et al. proposed a dynamic taxi speed adjustment strategy for aircraft, using state space, action space, and reward function models to make speed adjustment decisions, to achieve path optimization with the goal of conflict-free and minimum taxiing time for aircraft [13]. In 2021, [PERSON] et al. constructed an airport taxi time prediction model, combining a rolling time window and a fuzzy time window for taxi path optimization [14]. In the same year, [PERSON] et al. analyzed the Q-learning path planning algorithm and A* path planning algorithm, and based on this, they made a plan for the field taxi path [15]. [PERSON] et al. developed an aircraft taxi guidance system based on WORLD WIND and constructed an airport field structural model to achieve the path optimization of field taxi paths [16]. [PERSON] et al. constructed an airport field taxi optimization model based on two-layer planning and used an iterative heuristic algorithm to solve taxi conflicts, which significantly outperformed the first-come-first-served scheduling scheme under the premise of no conflicts and improved the overall operational efficiency [17]. In 2022, [PERSON] et al. evaluated the risk of airport field taxiways and divided the risk intervals by fusing the airport field Petri net model and the risk probability model of taxiing conflicts, and used an ant colony algorithm integrating the Stackelberg game idea to optimize the taxiing paths of aircraft to reduce the risk level of the airport taxiways [18]. That same year, [PERSON] et al. used a new ant colony pheromone initialization method, pheromone allocation mechanism, and pheromone updating strategy based on the wolf predation principle, designing a multi-strategy particle swarm-ant colony hybrid optimization algorithm for solving the airport taxi planning model and a conflict adjustment strategy based on the speed-first and first-come-first-served principles, which effectively avoid taxiway conflicts and conflict propagation [19]. Then, [PERSON] et al. used a multi-objective A* algorithm combined with a genetic algorithm to generate and optimize aircraft ground trajectories, solve the trajectory conflict problem, and adapt to environmental change conditions in real time [20]. In 2023, [PERSON] et al. improved the Q-Learning algorithm by optimizing Q-table exploration, resetting the initial Q-table values, introducing a dynamic exploration factor, and introducing a conflict avoidance strategy to plan taxi paths for aircraft in highly dynamic and stochastic airport environments [21]. In the same year, [PERSON] et al. proposed an improved A* algorithm, which applies an improved heuristic function to increase the search efficiency and improve path optimization [22]. The above studies mainly achieved conflict-free path planning for aircraft by improving the path planning algorithm, but they did not consider the limitations of the ground operation environment and the spatial and temporal distribution of aircraft taxiing. On this basis, in 2019, [PERSON] et al. used the Markov decision process to model the field aircraft operation process and used a convolutional neural network to learn the airport ground operation environment and flight time constraints to achieve conflict-free flight path planning and conflict resolution [23]. In 2022, [PERSON] et al.