Airport arrival and departure movements are characterized by high dynamism, stochasticity, and uncertainty. Therefore, it is of paramount importance to predict and analyze surface taxi time accurately and scientifically. This paper conducts a comprehensive review of existing studies on surface taxi time prediction and analysis. Firstly, the overall research framework of surface taxi time prediction and analysis is categorized from three perspectives: taxi time type, movement type, and modeling method. Then, focusing on the two means of taxi time analytical modeling and simulation modeling, the existing mainstream models and methods are categorized, and the main ideas and scope of application of the various methods are analyzed. Finally, the paper presents the future development direction of surface taxi time prediction prospects. The research results are aimed at providing basic support and methodological guidance for reducing the uncertainty in airport surface operation and enhancing the level of control and decision-making ability of airport surface operation.

The disturbances, caused by resource competition, weather changes, and demand uncertainties, frequently occur and result in obvious fluctuations (e.g., the increase or decrease of demand and capacity) in demand and capacity. This work addresses the problem of aircraft rerouting and rescheduling in presence of disturbances, characterized by the increase or decrease of demand and capacity, at a multi-airport terminal maneuvering area (TMA). A multi-objective optimization model is proposed to minimize the total flight delays and total extra flying time, considering the aircraft wake turbulence separation, runway operation rules, air traffic control regulations, scheduling time windows, and airport/airspace capacity, etc. Based on the non-dominated sorting genetic algorithm (NSGA-II), we designed an efficient evolutionary algorithm to solve the proposed model and search the Pareto solutions. Five scenarios are designed to analyze and compare the operation performance under normal and disturbed conditions, using the classic First-Come-First-Served (FCFS) and optimized scheduling (OPTS) methods. A case study is conducted for Shanghai Pudong and Hongqiao Airports, and the computational results show that, compared with the FCFS method, the proposed OPTS method significantly performs a overall better performance in minimizing total flight delay and total extra flying time, while slightly inferior in optimizing the delay and extra flying time of one single flight.

With the continuous increase in airport operational pressure, frequent conflicts and declining efficiency in aircraft surface taxiing have become increasingly prominent. This paper addresses the problem of aircraft conflict resolution and taxi trajectory optimization in airport surface operations by constructing a surface trajectory optimization model that incorporates airline fairness and designing a corresponding Markov decision process. On this basis, an asynchronous guided hierarchical multi-agent reinforcement learning method is proposed to achieve the joint optimization of conflict-priority determination and aircraft taxi trajectory planning through upper- and lower-level coordinated decision-making. Simulation results under the selected peak-hour scenario show that, compared with the first-come, first-served (FCFS) method, the proposed approach improves the departure sequence, waiting time, and number of turns by 40%, 23.61%, and 7.52%, respectively, while also taking into account the interests of different airlines. Further sensitivity analysis indicates that, under disturbances such as increased flight volume and random stand changes, the model still maintains a certain degree of optimization capability and robustness within the same simulation framework, although the optimization performance declines as the disturbance level increases. Algorithm comparison and ablation experiments show that the proposed method has advantages over common baseline methods in convergence speed, training stability, and final optimization performance under the reported experimental settings. The results provide preliminary evidence for the feasibility of introducing fairness constraints into airport surface trajectory optimization under peak-hour operating conditions and offer a useful reference for airport surface conflict resolution and operational optimization.