A dynamic control method for airport ground movement optimization considering adaptive traffic situation and data-driven conflict priority

The primary objective of this study is to address the taxiing uncertainty in airport ground movement optimization with a novel dynamic control method considering adaptive traffic situation and data-driven conflict priority. To better characterize the airport surface traffic situation, a refined link-level unimpeded taxi time (UTT) estimation method is designed to improve the UTT prediction accuracy, and a comprehensive airport operation evaluation method is developed to identify similar surface traffic situation scenarios. Then, a data-driven method is proposed to discover contributing factors to conflict priority assignment and obtain more realistic conflict resolution behaviors. Finally, the adaptive traffic situation and updated conflict priority approach are embedded into the proposed dynamic airport ground movement optimization framework. One month aircraft trajectory data is collected from a representative mega airport in China to illustrate the procedure. The results reveal that the dynamic control method could achieve better performance of airport ground movement than sequential approach through timely path-adjustment and effective collaboration with other aircraft. Moreover, the adaptive traffic situation approach could achieve better trade-off between computation efficiency and optimal solution, and the updated conflict priority approach tends to coordinate the taxiing aircraft to the route plan with lower additional taxi time and achieves a more balanced utilization of taxiway network. The study provides a promising approach to generate real-time conflict-free trajectories for airport operation in future surface trajectory-based operations (TBO) scenarios.