Resumen
With the escalating complexity of surface operations at large airports, the conflict risk for aircraft taxiing has correspondingly increased. Usually, the Air Traffic Controllers (ATCOs) generate route, speed and holding instructions to resolve conflicts. In this paper, we introduce a conflict resolution framework that incorporates prior knowledge by integrating a Multi-Layer Perceptron (MLP) neural network into the Monte Carlo Tree Search (MCTS) approach. The neural network is trained to learn the allocation strategy for waiting time extracted from actual aircraft taxiing trajectory data. Subsequently, the action probability distribution generated with the neural network is embedded into the MCTS algorithm as a heuristic evaluation function to guide the search process in finding the optimal conflict resolution strategy. Experimental results show that the average conflict resolution rate is 96.8% in different conflict scenarios, and the taxiing time required to resolve conflicts is reduced by an average of 42.77% compared to the taxiing time in actual airport surface operations.