基于有效中转时间预测的不正常航班恢复技术

不正常航班恢复问题研究通常基于固定航班中转时间，忽视了实际航班中转时间的改变对航班恢复带来的影响。对此，依据全国235个机场的全部运营航班数据抽取机场-航班特征，构建了基于LightGBM的航班中转时间预测模型，预测航班的有效中转时间，数值结果显示，航班中转时间预测模型预测的均方根误差为6.783 min。构造了基于有效中转时间的不正常航班恢复模型，并针对性地设计了求解该模型的列向量生成算法，构造的模型通过取消、改变计划时间、更换飞机等方式，分别在最小化航班延误时间、取消个数、换飞机个数的目标下，解决机场流量下降、机场关闭、飞机维修等不正常条件下的航班恢复问题。通过航空公司实际运行数据测试证明，基于有效中转时间预测的不正常航班恢复技术有效，在大规模航班恢复的情况下，可以将总延误时间减少34.2%。将列向量生成算法与时空网络算法的结果进行对比，所提出的恢复方法能降低航班恢复代价。 

Irregular flight recovery technique based on accurate transit time prediction

In previous studies, the general method for flight recovery problem used fixed flight transit time, rather than considered the result of flight transit time changes in real airports. We propose a LightGBM model to predict accurate transit time based on the airport-flight features from total 235 airports and all flights in China. The numerical results show that our model has 6.783 minutes root mean square error using real flights data. We construct an irregular flight recovery model based on effective transit time, and specifically design a column vector generation algorithm to solve this model. This algorithm can solve the problem of airport traffic flow decrease, airport closure, aircraft maintenance and other irregular conditions under the goal of minimizing flight delays, the number of cancellations, and the number of aircraft changes by canceling, changing the planned time, and replacing aircraft. Tests on actual operating data of airlines prove that the irregular flight recovery method based on transit time prediction is effective. The real case of large-scale flight delays test shows the total delay time can be reduced by 34.2%. The comparison between the spatio-temporal network algorithm and the column vector generation algorithm shows that the proposed flight recovery method also can reduce the recovery cost under the premise of the same recovery result.