华东交通大学学报2025，Vol.42Issue(3)：57-66,10.
基于SMOTE算法的航班正常率预测
Flight Punctuality Rate Prediction Based on SMOTE Algorithm
摘要
Abstract
To achieve accurate prediction of the flight punctuality rate,a flight regularity prediction index system was constructed based on data statistics of flight delay reasons,includeing departure airport,destination airport,flow control information,and route characteristics.It proposes a SMOTE algorithm-based XGBoost classifica-tion prediction model(SM-XGBoost model)and a SMOTE algorithm-based LightGBM classification prediction model(SM-LightGBM model).Based on the actual data of major airports in East China,the validity and progres-siveness of the proposed model are verified.The results showed that the SM-XGBoost model and SM-LightG-BM model were significantly better than the decision tree and random forest models in terms of prediction accu-racy and error.In terms of stability of training set and test set,SM-LightGBM model is superior to the SM-XG-Boost model,with a maximum prediction accuracy of 88.2%for test set.This method provides a new analytical approach for predicting events in similar complex systems.关键词
SMOTE算法/航班正常率/XGBoost模型Key words
SMOTE algorithm/flight punctuality rate/XGBoost model引用本文复制引用
张嘉懿,胡明华,黄梵根..基于SMOTE算法的航班正常率预测[J].华东交通大学学报,2025,42(3):57-66,10.基金项目
国家自然科学基金项目(52472345) （52472345）
