考虑风向概率特征的航班时刻优化方法  

A Flight Schedule Optimization Method Considering Characteristics of the Wind Direction's Probability

作　　者：王莉莉[1] 郭微萌 WANG Li-li;GUO Wei-meng(Air Traffic Management College,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学空中交通管理学院,天津300300

出　　处：《科学技术与工程》2024年第16期6951-6962,共12页Science Technology and Engineering

基　　金：国家自然科学基金委员会与中国民用航空局联合资助项目(U1633124);中国民航大学研究生科研创新资助项目(2022YJS088)。

摘　　要：在航班时刻表进行实际运行过程中,风向的变化对航班到达终端区共用航路点的时间造成影响,进而造成航路点的容量过载或容量浪费。因此,根据风向的统计概率对航班时刻进行调整,目的是制定在一定程度上能减少共用航路点的容量过载或容量浪费的航班时刻表。根据风向对离场航班跑道分配的影响提出基准风向的概念,并基于航季中各月份在过去5年间的机场基准风向概率预测了下一年各月的机场基准风向概率,并根据各月的基准风向概率特征进行聚类。在聚类结果的基础上,以风向变化对航路点流量的影响程度为目标函数,建立考虑风向概率特征的航班时刻优化模型,并将ε-约束法与改进粒子群优化算法(particle swarm optimization, PSO)结合提出ε-约束(ε-constraint method)-PSO组合算法实现多目标模型的求解,以北京终端区的离场航班为研究对象进行验证。结果表明:相比初始航班时刻表,共用航路点小时流量的最大值减少了12%,在不同基准风向时的共用航路点流量方差分别降低49%和56%;相比线性加权求和的方法,该方法可以实现共用航路点的溢出航班总量减少70%。结果表明:在考虑风向概率特征的条件下,该模型可以在一定程度上使共用航路点的流量更均衡,减少出现共用航路点容量过载或容量浪费的现象,减轻航路点流量受风向影响的程度。During the actual operation of flight schedules,the change of wind direction affects the arrival time of flights at the shared waypoints in the terminal area,which in turn causes capacity overload or capacity waste.Therefore,flight schedule is adjusted based on statistical probabilities of wind direction,with the aim of developing a flight schedule that can reduce to some extent the capacity overload or waste of shared waypoints.The concept of a benchmark wind direction was proposed based on the impact of wind direction on the allocation of runways for departing flights.Using the probability of the benchmark wind direction at the airport for each month in the past five years during the flight season,the probability of the benchmark wind direction for each month in the next year was predicted,and all the months were clustered by characteristics of the wind direction's probability.On the basis of the clustering results,taking the effect extent of wind direction change on waypoint's flow as the objective function,a flight schedule optimization model considering characteristics of wind direction's probability was established,and theε-constraint method was combined with an improved particle swarm algorithm(PSO)to solve the multi-objective model,which was calledε-constraint-PSO combination algorithm.The departure flights from the Beijing terminal area were used as the research object for verification.The results show that compared with the initial flight schedule,the maximum value of the hourly flow of shared waypoints decrease by 12%,and the variance of the shared waypoint flow at different benchmark wind directions decrease by 49%and 56%,respectively.Compared with the linear weighting method,this method can reduce the total number of overflow flights at shared waypoints by 70%.Research results indicate that considering the characteristics of the wind direction's probability,the model can to some extent achieve a more balanced flow of traffic at shared waypoints,reducing occurrences of capacity overload or waste at

关 键 词：航空运输工程 航班时刻 航班时刻表 粒子群优化算法(PSO) 共用航路点 

分 类 号：V351[航空宇航科学与技术—人机与环境工程]