Optimization of Path Planning Algorithm in Intelligent Air Traffic Management System

The construction of a smart aviation transportation system can improve the informatization level and decision support ability of aviation traffic management and services, and is an effective way to improve aviation traffic management, reduce traffic accidents, and improve people's quality of life. In air transportation, due to factors such as severe weather, there are repeated routes in the air transportation route, resulting in problems such as long transportation distance and reduced transportation efficiency. In intelligent aviation path planning, ant colony optimization (ACO) is a popular path solving strategy and has been widely applied. This paper proposes an air transportation path planning method based on deep learning (DL) and ACO for intelligent aviation path planning. By combining Convolutional Neural Networks (CNN) and Long Short Term Memory Networks (LSTM), accurate prediction of the shortest path in complex environments has been achieved. Under various constraints, real-time optimization of ACO is used to obtain the optimal solution of the model and achieve air transportation path planning. The simulation results show that the algorithm designed in this article has good feasibility in complex environments and can save costs.