Gao Wei, Pang Dingying
College of Air Traffic Management, Civil Aviation University of China, Tianjin, 300300, China.
Sci Rep. 2024 Aug 12;14(1):18715. doi: 10.1038/s41598-024-68884-9.
Slot structure is the equilibrium result of market demand side and slot resource supply side, while slot parameters reflect the operational support capacity of the aviation system. Time parameters reflect the operational support capability of the aviation system. Time structure should not only reflect changes in market demand, but also meet the constraints of operational efficiency. Constructing a reasonable 18-24 h timetable profile for busy airports that meets normal expectations for declared capacity and seasonal scheduling is a challenge in civil aviation slot management. This study utilizes historical data on airport flights and weather conditions to establish a regression prediction model for the time structure using K-means clustering and partial least squares regression. Additionally, ensemble learning is employed to forecast flight delay levels. The findings demonstrate that random forest yields favorable results in regression and prediction tasks, allowing for the integration of upper (good weather) and lower (severse weather) limits of the time profile with delay predictions as time parameter intervals. Consequently, the flights falling within these intervals achieve an average delay level of less than 15 min which meets the expectations of normal flight.
时隙结构是市场需求侧和时隙资源供给侧的均衡结果，而时隙参数反映了航空系统的运行保障能力。时间参数反映了航空系统的运行保障能力。时间结构不仅应反映市场需求的变化，还应满足运行效率的约束。为民用航空时隙管理中的繁忙机场构建一个合理的18至24小时时刻表轮廓，使其符合申报容量的正常预期和季节性调度，是一项挑战。本研究利用机场航班和天气状况的历史数据，采用K均值聚类和偏最小二乘回归建立时间结构的回归预测模型。此外，采用集成学习来预测航班延误水平。研究结果表明，随机森林在回归和预测任务中取得了良好的效果，能够将时刻表轮廓的上限（良好天气）和下限（恶劣天气）与作为时间参数区间的延误预测相结合。因此，落在这些区间内的航班平均延误水平小于15分钟，符合正常航班的预期。
