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Advances in Earth Science >

2019 , Vol. 34 >Issue 8: 879 - 888

DOI: https://doi.org/10.11867/j.issn.1001-8166.2019.08.0879

The Heat Airspace Cloud Map in Terminal Airspace of Airports Based on Air Passenger Flow and a Case Study in Beijing International Airport

  • Xinru Du ,
  • Zi Lu ,
  • Yaqing Dong ,
  • Jianghui Ding ,
  • Dianshuang Wu
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  • 1. School of Resource and Environment Sciences，Hebei Normal University，Shijiazhuang 050024，China
    2. Hebei Key Laboratory of Environmental Change and Ecological Construction, Shijiazhuang 050024, China
    3. Decision Systems &-Service Intelligence Lab, Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, New South Wales, 2000, Australia
Du Xinru(1989-), female, Jinzhong City, Shanxi Province, Ph. D student. Research areas include aviation geography and development of airspace utilization. E-mail:duxinru0224@126.com

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)

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Abstract

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.

Key words： Density of air passenger flow; Heat airspace; Heat cloud map of flight trajectories; Trajectories grid; Beijing International Airport

Cite this article

Xinru Du , Zi Lu , Yaqing Dong , Jianghui Ding , Dianshuang Wu . The Heat Airspace Cloud Map in Terminal Airspace of Airports Based on Air Passenger Flow and a Case Study in Beijing International Airport[J]. Advances in Earth Science, 2019 , 34(8) : 879 -888 . DOI: 10.11867/j.issn.1001-8166.2019.08.0879

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