To improve the accuracy and reliability of flight transit time prediction during airport peak periods， a dynamic flight transit time prediction method combining the unbiased kernel density estimation （UKDE） and the extreme gradient boosting（XGBoost） models is proposed. Firstly， considering the continuous and uncertain changes in the flight density of the model input variables， the UKDE method is used to estimate the flight density of the airport as a dynamic indicator. Secondly， the quantum particle swarm optimization （QPSO） method is introduced to optimize the XGBoost model. Finally， the changes in the input characteristics before and after the occurrence of the preceding flight delays are considered， and the initial prediction results are modified to obtain the two-stage prediction results. The final results verify that the average absolute error of the prediction in peak hours is 7.365 min， which is better than those of random forest（RF）， particle swarm optimization（PSO）-XGBoost and XGBoost， and the average absolute error of the modified prediction results is reduced by 3.373 min. The model input parameters， in descending order of sensitivity， are flight density， early arrival time of preceding flights and delayed arrival time.