## 1 Introduction  
Flight safety is a primary focus of the civil aviation industry [1, 2]. According to Boeing's statistical summary of commercial jet airplane accidents from 1959 to 2018 [3], the approach and landing are the flight phases during which safety incidents are most likely to occur. The landing phase only accounts for an average of 1% of the total flight time; however, the occurrence rate of flight incidents for this phase is as high as 24%. Therefore, the landing phase is a critical phase for ensuring flight safety. Runway overrun is a flight safety incident in which an aircraft fails to slow down in time after landing, resulting in it running off of the runway. This may cause serious economic losses to airlines and even endanger the lives of passengers in severe cases. According to the runway safety accident analysis report of the International Air Transport Association (IATA) [4] published in 2015, runway overrun incidents accounted for 44% of the 78 runway excursion incidents that occurred from 2010 to 2014, and they accounted for 100% of the five fatal accidents. Therefore, it is crucial to study the risk of runway overrun for ensuring civil aviation safety. The QAR (quick access recorder), as an airborne data recorder, can collect and store multidimensional flight parameter data during a flight. Currently, QAR devices have been widely installed on various types of aircraft. It has been proven by practical experience that the QAR can effectively collect thousands of real-time parameters during flight. These flight parameters can reflect the condition of the external environment, flight attitude, aircraft status, pilot operations, etc. Therefore, they provide objective evidence for flight technical evaluations, safety incident investigations, and the elimination of potential flight safety hazards, which can yield better flight safety. Consequently, research related to civil aviation flight safety based on QAR data has attracted significant attention from researchers in recent years [5; 6; 7]. Regarding the risk of runway overrun for commercial aircraft, existing research can be broadly categorized into two types. The first type of research focuses on post hoc analysis of runway overrun incidents based on historical accident data [8; 9; 10; 11; 12]. These studies mainly employ probabilistic statistical analysis methods, combined with accident investigation reports, to analyze the causes of runway overrun accidents and propose corresponding improvement measures. The main problem with this type of research is that there is a limited number of accident data samples. Therefore, it is difficult to fully explore valuable information within vast QAR data, thus leading to significant limitations. The second type of research focuses on flights where runway overrun incidents did not occur and uses QAR data to analyze the relative risk levels of different flights. The main issue with this type of research is that the risk assessment metrics are oversimplistic and fail to consider the dynamic runway overrun risk due to the pilot's deceleration operation after touchdown. To address the aforementioned issues, we propose a new dynamic risk assessment model for runway overrun in this paper. This model fully considers the dynamic risk from the moment the aircraft touches down until it exits the runway, including the impact of the pilot's braking operations. It provides a more comprehensive approach to assess runway overrun risk. We first correct the landing trajectory data of the aircraft so that the aircraft position relative to the runway can be accurately determined. Then, we design an algorithm to recognize and extract the autobrake level of the aircraft during the landing phase. Based on the recognized autobrake level, a dynamic assessment model for runway overrun risk is constructed. Then, relevant risk factors for runway overrun are extracted by applying feature engineering methods. Furthermore, classification and regression models are employed to classify risky flights and predict risk metrics.