The risk assessment of runway excursion accidents in the high-plateau airport is a significant part of the airport operations and risk management. This article proposes a method to evaluate the risk of runway excursion accidents in the high-plateau airport with the probability and severity estimations of runway excursion in the high-plateau airport. Firstly, the probability estimation is calculated by combining the correction model and the Bayesian network. The probability correction model considers the runway length required for takeoff and landing, specific ambient temperature, and wind speeds in the high-plateau airport. Then, a high-plateau airport simulation evacuation model of evacuation capacity is established by the VR experiment, and the severity of evacuation in the high-plateau airport is evaluated, combining the endurance of fire products. Finally, based on probability and severity, the quantitative calculation value of risk is given. We also utilize the model on a case study to find the effect of temperature, wind speed, and altitude on this risk index. The results show that the risk of runway excursion accidents in the high-plateau airport is greatly affected by temperature and wind speed. The experimental airport's risk value in February is about 11.8 times of that in September, and the risk value of the high-plateau airport is 7.32 times higher than that in a plain airport. The model successfully simulates the various scenarios at a high-plateau airport and other airports at different altitudes. It is proved that the fire risk of high-plateau airport runway excursion accidents should be paid attention to and provides scientific guidance for the airport's aviation safety management based on the actual characteristics of a high-plateau airport.

This study applied modern econometric models to analyze the factors affecting the number of unsafe events (NUE) in the airport flight area using time series data from 1993 to 2017. Influencing factors considered in this article include gross domestic product (GDP) per capita (GDPPC), household consumption level (HCL), the civil aviation passenger turnover (CAPT), the number of civil aviation transport aircraft (NCATA), the total population of the whole country, and the number of civil aviation employment (NCAE). First, the Johansen cointegration test results demonstrate that HCL, NCATA, and NCAE have long‐term effects on unsafe events—namely, a 1% increase in HCL corresponds to an average 0.262% increase in NUE. In addition, a 1% increase in NCATA correlates to an average 2.339% increase, and a 1% increase in NCAE corresponds to an average 2.202% decrease in NUE with fixed variables. The analysis results based on the vector error correction model suggest that CAPT has short‐term effects on unsafe events and positively correlates to unsafe events. The results of the impulse response function also indicate that the impact of NUE in the previous period on the change of NUE gradually weakens and finally tends to be stable. Finally, NCATA is found to promote the change of NUE significantly. Similarly, the results of variance decomposition also indicate that NCATA has the greatest contribution to the change of NUE, followed by NUE in the previous period. The findings reveal the effects of key factors on the change of unsafe events in airport flight areas, thus providing a valuable theoretical basis for preventing the occurrence of unsafe incidents.