In summary, research on runway excursions covers multiple aspects and can generally be divided into two categories: studies based on accident report data and those based on flight data. This section provides a brief introduction to research conducted in these two areas. Okafor et al. [4] conducted a statistical analysis of summaries, accident reports, and perspectives from stakeholders to identify the influencing factors of runway excursions. They discovered that accidents occur when there is a combination of human and engineering system failures. Analyzing accident reports has always been crucial for ensuring safety and preventing such incidents. These reports are typically presented in textual form, requiring the extraction and analysis of key information. Chang et al. [5] employed the SHELLO model to classify human-induced risk factors and formulated four prioritized risk management strategies for airline pilots. These strategies aim to reduce the occurrence of runway excursions. Zhang et al. [6] established a predictive model for runway excursions, enabling the anticipation of such incidents. They validated their model using data from aviation companies. Wang Lei, Ren Young, et al. [7] explored the impact of crucial flare operations conducted by pilots on long-distance landings and hard landing events using QAR data. Through variance analysis, they identified flight parameter characteristics during the initiation of flaring and throughout the entire flaring process that are associated with abnormal landing events. They found that flare operations have a significant impact on landing distance and vertical landing acceleration. Li et al. [8] proposed a landing trajectory correction method that integrates ground speed and runway position information. They established a dynamic off-runway risk assessment model based on QAR data, uncovering key factors related to off-runway risk during the landing phase. In the study of exceedance issues, Lv et al. [9] introduced a novel approach that utilizes QAR data on a large scale. They categorized flight cases using exceedance danger lines and ultimately employed three machine learning models to examine the relationship between exceedance risk indices and flight parameters. Distefano et al. [10] used multiple correspondence analysis to determine the correspondence between various aspects of offset runway excursions, identifying variable combinations worthy of further study and providing a method for runway risk assessment for civil aviation organizations. Wang et al. [11] constructed a safety control structure diagram for offset runway excursions, identifying unsafe control behaviors and analyzing their key causes. Odisho et al. [12] applied machine learning to establish a predictive model for perceiving unstable approach risks, thereby reducing the occurrence of offset runway excursions by predicting the risk of unstable approaches. Olive et al. [13] utilized S-mode data to analyze the contributing factors of offset runway excursions, proposing a risk assessment model for such occurrences. Mascio et al. [14] calculated risk values for over 1300 points around runways using preliminary data provided by airport management agencies, evaluating the current safety level and providing a risk map to identify the areas with the highest risk of offset runway excursions. Distefano and others [15] employed association rule methods and the Apriori algorithm to determine that the most important variables for all types of offset runway excursions are aircraft type, with events of severity classified as major and hazardous being associated with small aircraft, while events classified as catastrophic are associated with medium to large aircraft. Ketabdari et al. [16] used simulators to model the impact of various meteorological variables on aircraft operations, indicating that gusts, wind speed, and particularly crosswinds are the primary weather factors influencing offset runway excursions. Vorobyeva et al. [17] integrated data from various sources to enhance understanding of runway surface conditions in order to reduce the risk of offset runway excursions, strengthening flight safety and environmental protection. Mauro et al. [18] analyzed the functional complexity faults leading to offset runway excursions and discussed detection and handling prior to events. Their research indicates that enhancing system safety technology may only increase system complexity, inevitably leading to more frequent faults. Therefore, in-depth analysis and management of subsystems are crucial for addressing functional complexity faults. Wang and Holzafel [19] established a model to investigate offset runway excursions and abnormal runway contact, analyzing landing gear stress and validating the model using QAR data.

The existing research has the following limitations: Regarding studies based on historical accident data, they can only analyze the trends of accidents and their influencing factors, while the available information from accident data is limited. Studies based on QAR data usually have a broader focus on stages of research without considering the varying impacts of operations at each specific stage of flight.

In addition to research on mitigating runway excursions, other studies on aviation safety primarily focus on flight risks, predictive warnings, and feature extraction. In the realm of a quantitative assessment of hard landing risk, Wang Lei et al. [20] applied statistical modeling to establish a quantitative assessment model for heavy landing risk, evaluating the likelihood and severity of occurrences based on parameter distribution functions and algorithms and calculating risk levels. They constructed a landing overrun analysis model based on QAR data, utilizing Bayesian network [21] analysis and employing GTT algorithms for parameter learning to establish a landing overrun risk Bayesian network model. Wang Le et al. [22] divided 128 cases of QAR data into two groups based on the identification thresholds for normal landings and long landings, conducted inter-group comparisons of flight parameters, established logistic regression and linear regression models, and analyzed the potential flight operations that lead to performance differences in long landing events, ultimately identifying key operations that affect landing distance during flare. Wang et al. [23] also conducted a study on the correlation between age and flight safety performance, revealing a direct impact of age on the exceedance rate of pilots aged 41–45 and 56–60. Zhang et al. [24] utilized the Apriori algorithm to mine association rules within exceedance events and their correlation with environmental conditions, finding that speed exceedances during the approach process at 500–50 feet are more likely to lead to speed deviations at altitudes below 50 feet during the approach process. Li et al. [25] developed an interpretable model called IMTCN, which can accurately pinpoint key flight parameters and the corresponding moments with the greatest impact on safety incidents. Chen et al. [26] proposed a risk assessment method for deep learning using LSTM-DNN with variable fuzzy recognition of entropy weight, targeting high-altitude approach and landing risks.

In the field of trend prediction and warning, Westphal et al. [27] developed an aircraft cockpit risk sustained warning system based on fuzzy logic. Huang et al. [28,29] proposed dynamic linked list QAR decoding technology, intelligently diagnosing and predicting aircraft system faults. Zhao et al. [30] established a geometric determination method and introduced a hidden Markov model backpropagation neural network to infer and predict the status of aircraft flight. Utilizing ACARS, Lu et al. [31] established a high-precision trajectory data prediction model. Fu et al. [32] utilized long short-term memory networks to establish a flight trajectory prediction model for time series flight data. Qian et al. [33] proposed a flight trajectory prediction method based on composite gated recurrent units, achieving flight trajectory prediction. Through simulation analysis, it was discovered that compared with gated recurrent units and long short-term memory network models, it has the smallest error.

In the field of feature extraction and parameter estimation, Wang et al. [34] integrated the extended Kalman filter and improved Bryson–Frazier smoother algorithm based on QAR data. This, in turn, enhanced the precision of metric estimation. On the other hand, Korsun et al. [35] used satellite navigation systems and wind speed identification methods to estimate the measurement errors of the angle of attack and sideslip angle during flight tests. Lastly, Sun et al. [36] proposed the use of a differential testing analysis method for extracting pertinent information from exceedance events.