Long landings can reduce runway utilization and increase the probability of runway incursions and excursions. Previous studies on long landings often lacked support from actual operational data and primarily relied on event-triggering logic established by airlines for parameter exceedance detection and retrospective analysis. In response, a comprehensive risk prediction framework for aircraft long landings, supported by Quick Access Recorder (QAR) data, was constructed. The framework includes a data analysis pipeline, a sequence prediction model, and performance evaluation metrics for accident warning efficiency. Specifically, approximately 3 million rows of real QAR data were collected, and reasonable landing intervals were extracted based on pilots’ correct landing sightlines, attention allocation, and actual visual scenarios at departure heights. Gradient Boosting Decision Trees (GBDT) were employed to develop a method for extracting landing interval feature data, based on monitored parameters and ranges of landing distance. Additionally, the GBDT-Informer long-sequence time series prediction model was developed to forecast landing distance, accompanied by the construction of effective metrics for evaluating prediction performance. The results indicate that the GBDT-Informer model effectively models the temporal dimensions of landing intervals, accurately predicting ground speed (GS), radio altitude (RALT), and landing distance sequences. Compared to other prediction models, the GBDT-Informer model consistently achieved the smallest RMSE, MAE, and MAPE values, demonstrating high prediction accuracy. This predictive framework allows for the analysis of the coupling relationships among multiple parameters in flight data and their interrelations with exceedance anomalies. The findings can be applied in actual flight landings to promptly assess whether landing distances exceed limits, providing quick references for flight crews during landing or go-around decisions, thereby enhancing operational safety margins during the landing phase.

Aiming at the practical difficulties (e.g., high cost of full-scale tests) in testing the rolling resistance of aircraft wheels on unpaved runways, this study establishes a theoretical calculation formula for wheel rolling resistance based on the Bekker model, following an analysis of the development and application of wheel–soil interaction models. Global sensitivity analysis using the Sobol’ method was performed on the theoretical formula to derive a simplified calculation model. Aircraft load simulation tests under 80 kN, 100 kN, and 120 kN loading conditions were conducted using a specialized loading vehicle to determine parameters for the simplified prediction model. The resistance values obtained from this model were then applied to calculate aircraft takeoff roll distance. The accuracy of resistance estimation was verified by comparing the calculated results with takeoff distances reported in relevant literature. This research provides a novel approach for estimating wheel rolling resistance of transport aircraft on unpaved runways and offers valuable reference for determining the required length of unpaved runways.