Hybrid AI-based Dynamic Re-routing Method for Dense Low-Altitude Air Traffic Operations

In this paper, we propose a rerouting method based on hybrid Artificial Intelligence (AI) algorithms for managing Unmanned Aircraft Systems (UAS) and Urban Air Mobility (UAM) traffic during their cruise and approach phases. The adopted approach capitalizes upon FourDimensional Trajectory (4DT) functionalities, supporting an uncertainty-resilient and flexible strategic deconfliction framework to improve the operational efficiency and security of Demand-Capacity Balancing (DCB) services. The objective is to accommodate future UAM and other autonomous vehicle-based business models by safely implementing traffic management in dense low-altitude airspace around cities and suburbs. The proposed UAS Traffic Management (UTM) system uses metaheuristic algorithm, especially the Tabu-search algorithm, to determine a global optimised rerouting solution. The calculated solutions can be continuously used as labelled data to train and optimise a machine learning process for real-time decision making, greatly improving the computational performance of intelligent UTM systems.