The pilot is the main person in charge of taxiing safety while moving on the airport surface. The visual separation and speed adjustment are directly related to safety and efficiency of airport surface operation. According to the actual taxiing procedures and airport control rules in China, this paper proposes a novel microscopic simulation model based on the pilots’ visual separation. This model is also built by refining the aircraft taxiing procedures at intersections. The observation range, the separation judgment, pilots’ visual distance, rate of proximity and the intention for speed governing are discussed as parameters in the model. The rules for aircraft separation judgment, pilots’ autonomous speed governing, and position updates are also set up and discussed. The proposed simulation can accurately simulate the acceleration and deceleration intentions under different motion trends while reproducing the motion process including the following acceleration, following deceleration and delayed deceleration caused by separation changes. The results demonstrate that the number of conflicts can be reduced to 50% based on visual separation adjustment of 50 s when the convergence angle is 30°. The pilot’s visual distance is inversely proportional to the fluctuation range of the speed of the rear aircraft, the proximity rate of the front and rear aircraft and the probability of conflict. The simulation results of this model conform to the actual taxiing routes and control rules, which provides technical support for improving the safety level of airport surface operation and presents certain reference value and practicability.

To explore the mixed traffic characteristics of vehicles and aircraft on the airport surface and solve the problem of real-time conflict detection at key intersections. According to the actual taxiing procedures and airport control rules in China, this paper focus on abstracting the mixed motion process of aircraft and vehicles in the maneuvering area, defining the convergent cross-safety operation scenario. To quantify the driver’s attention to safety separation and the degree of conservatism in adjusting speed, the vehicle deceleration rate and acceleration rate are defined with α as the exponent. Under the same spacing, the vehicle deceleration rate is directly proportional to α, and α is named the vehicle safety sensitivity. At the same time, the rules of speed change of vehicles and aircraft will be designed, and a convergent operation model of aircraft and vehicles will be proposed. Based on real-time speed and separation dynamic assessment of safety and efficiency under different traffic strategies, the computer simulation results show that vehicle safety sensitivity and deceleration rules determine the sequence of vehicles and aircraft when they are passing through in the short term and can affect the proportion of mixed traffic flow in the long run. The safety probability of vehicles passing the intersection first is negatively correlated with vehicle safety sensitivity, while the safety probability of aircraft passing the intersection first is correlated positively with vehicle safety sensitivity. The efficiency of passing without conflict with mixed traffic has an inverse relationship with vehicle safety sensitivity. When the vehicle safety sensitivity takes the value in the interval [0.4, 0.6], a mixed traffic flow with higher safety and efficiency, better stability, and a balanced ratio of locomotives can be obtained.

In this study, we propose an improved betweenness centrality (FDBC) algorithm to identify vulnerabilities in airport field operations, assist controllers in formulating emergency response strategies, and enhance the safety resilience of the maneuvering area network during external disturbances. The algorithm is developed by constructing a transportation network model for the airport movement area and extracting key nodes based on considerations of traffic flow and road class. Utilizing aircraft field operation rules, we define an assessment index for the average shortest path length of inbound and outbound activities. Comparative analyses of comprehensive resilience indexes under various recovery strategies are conducted to assess the network’s recovery ability (RA) and adaptability in the face of external disturbances. The goal is to identify the optimal recovery strategy that achieves a dynamic balance between development and safety. To validate our approach, we conducted an empirical study at an airport in southwest China, simulating the resilience of the airport movement area transportation network under varying road class coefficients, traffic flow weights, and road class weights. The results indicate that the airport movement area transportation network in the southwest region lacks small‐world and scale‐free network characteristics. The identification of important road intersections through the FDBC strategy proves to be more effective in discerning potential safety hazard orientations. Under the FDBC‐based recovery strategy, as traffic flow weights increase, the comprehensive resilience index of the network initially shows a smooth trend, followed by a decrease, then another period of smoothness before decreasing again. Increasing the road class coefficient can enhance the network’s recovery efficiency and resilience level, enabling the network to maintain a high level of recovery for an extended period. This is beneficial for enhancing the safety and stability of flight operations. However, in cases where traffic flow weights are high, the FDBC‐based recovery strategy, which solely considers traffic, exhibits a relatively slower network RA in response to degree‐valued perturbations. Overall, the traffic network in the airport movement area demonstrates the highest resilience under the FDBC‐based recovery strategy. It can rapidly transition the network from a failure state to a normal stable state, thereby improving emergency response efficiency and ensuring the safe operation of the traffic network in the airport movement area.

Dynamic aeronautical information is still widely disseminated through textual Notice to Air Missions (NOTAMs), while message-oriented storage cannot directly maintain the evolving states of affected objects. The objective of this study is to determine whether NOTAM-derived object events can be organized into traceable bitemporal states that support accurate and efficient current and historical access. To this end, this paper proposes BiTE, a bitemporal event-centered database framework that connects object-level event evidence, historical state versions, and materialized current-state projections. By integrating business and system time with NOTAM-specific lifecycle rules, BiTE supports state maintenance, historical reconstruction, and source traceability. A MongoDB-based prototype was evaluated using 44,591 NOTAMs from five major U.S. aerodromes. Independent manual validation showed 96.14–100% agreement across object identification and lifecycle-maintenance tasks. Across 1500 manually verified queries, BiTE achieved F1 scores of 99.43% and 98.66% for current-state and airport-overview retrieval, respectively, and a historical hit rate of 95.80%, outperforming representative message-oriented, relational-bitemporal, and RDF-based implementations. Mean query latency remained below 3.7 ms, while functionally equivalent ablations confirmed the performance contribution of the layered architecture. These results demonstrate that BiTE enables accurate, traceable, and efficient object-state management for dynamic aeronautical information.