收稿日期: 2019-04-13
修回日期: 2019-07-26
网络出版日期: 2019-10-11
基金资助
国家自然科学基金项目“数据通信支持的空域资源配置模型与机制”(41671121);河北省研究生创新资助项目“面向三大复合门户枢纽机场的空域资源动态配置模型与应用”(CXZZBS2018107)
The Heat Airspace Cloud Map in Terminal Airspace of Airports Based on Air Passenger Flow and a Case Study in Beijing International Airport
Received date: 2019-04-13
Revised date: 2019-07-26
Online published: 2019-10-11
Supported by
the National Natural Science Foundation of China "The model and mechanisms of airspace configuration supported by data communication"(41671121);The Graduate Student Innovation Fund Project of Hebei Province "Dynamic configuration model and application of airspace for three compound hub airports"(CXZZBS2018107)
大型枢纽机场终端空域航空流密度计算与热点空域识别是智能化时代一个新的挑战性研究课题，旨在利用航迹大数据自动生成航空流并解读其运行规律。针对机场终端空域航空流密度及其与空域资源占用之间的关系问题，设计了一个机场终端空域航空流量热区云图模型，以北京首都国际机场为案例，构建了由飞行航迹点构成的航空流经度、纬度和高度基本参数以及角度（转向）、速度（速差）额外参数与时间参数的时空数据集，通过航迹聚类和航迹点次数叠加生成4D流量热区云图，进而用细胞单元对应的基本参数和时间参数属性识别了热点空域范围，又用航迹网格识别了额外参数的变化以补充解释其影响，最后用概率密度拟合验证了4D识别的结果。这项研究识别出北京首都机场局部进近空域的热区分布和2个高度层上的热点空域峰值以及飞行转向、速差的影响，揭示出由飞行占用时长差异引起的热点空域范围变化规律。应用4D流量热区云图模型实现了细致准确的信息构建、热点空域变化的阶梯性表达、时空密度及其范围的多参数可视化，可辅助自动动态空域分区和空域资源配置决策，对缓解当前空中交通需求和空域资源限制的矛盾具有一定参考意义。
杜欣儒 , 路紫 , 董雅晴 , 丁疆辉 . 机场终端空域航空流量热区云图模型及其北京首都国际机场案例研究[J]. 地球科学进展, 2019 , 34(8) : 879 -888 . DOI: 10.11867/j.issn.1001-8166.2019.08.0879
The calculation of air passenger flow density and the recognition of heat airspace in terminal areas of large hub airports is a new challenging research in the intelligent era, that is, using big data can automatically generate air passenger flow and basic rules. Aimed for the air passenger flow density in airport and its relationship between occupation and airspace, based on the establishment of the Beijing International Airport 1 h flight, which consists of basic parameters-latitude, longitude and height, additional parameters-dogleg and speed of trajectories, time parameters, a spatio-temporal data set by clustering trajectories and calculation of aircraft trajectories points was made up. Then, heat cloud map of flight trajectories under 4D conditions was generated. Cell was used to identify the basic parameters and time parameter of heat airspace; grid graphs of flight trajectories were used to identify additional parameters and explain the influence on heat airspace; probability fitting graphs were used to verify the accuracy of 4D results. The conclusion is as follows: there are two areas of Beijing International Airport, which have the high density and at two different heights there also exist hot peaks; flight trajectories and speed of trajectories also affect the heat airspace. The variation of heat airspace caused by different flight occupancy time in 4D recognition was revealed. The method realized the 4D heat cloud map of flight trajectories, which is better for detailed and accurate information construction, expression of spatial changes, and more the parameters of density and visualization of temporal and spatial range, which can assist the automatic dynamic airspace sectorization and decision making on airspace allocation, and also have a definite reference meaning in alleviating the contradiction between the current air traffic demand and limited airspace.
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