基于KNN和SVR的航班滑出时间预测
Flight Taxi-out Time Prediction Based on KNN and SVR

针对大型繁忙机场粗放式预估航班滑出时间可能带来的场面交通不畅、运行效率不高等问题，基于K最近邻（KNN）和支持向量回归（SVR），构建了离港航班滑出时间预测模型.该模型采用KNN方法，考虑滑行距离、滑出过程中同一跑道正在滑出航班数、撤轮档后15 min内推出航班数等因素，预测得到航班滑出期间使用同一跑道的起降航班数；基于该预测结果，结合滑出距离和撤轮档前15 min同一跑道平均滑出时间等因素，采用SVR预测航班滑出时间.使用首都机场航班运行数据对模型进行检验，结果表明：在误差范围为±3 min内，平均预测准确率可达79.86%.
：Aimed at resolving the possible ground traffic and low operational efficiency issues in a large busy airport caused by the simple aircraft taxi-out time estimation, a two-step aircraft taxi-out time prediction model was constructed based on K-nearest neighbours (KNN) and support vector regression (SVR). First, considering the influence of factors such as taxiing-out distance, the number of taxi-out flights using the same runway and the number of pushback complete flights launched within 15 min of the removal of chocks, the number of departures and arrivals using the same runway during the flight taxiing out was predicted based on KNN. Then, based on the prediction results and other influential factors such as the taxiing-out distance and the average taxi-out time using the same runway within 15 min of the removal of chocks, the taxi-out time was predicted based on SVR. The airport operation data was grouped by arrival and departure traffic flow and the prediction model of the taxi-out time was constructed separately for each group. The experimental results based on the actual operation data of Beijing Capital International Airport show that the average prediction accuracy of the proposed model is up to 79.86% within ±3 min