CN115221220A - Multi-airport system identification method based on spatial organization characteristics - Google Patents

Abstract

The embodiment of the present invention discloses a multi-airport system identification method based on spatial organization characteristics. Wherein, the method includes: acquiring an airport point dataset and a city point dataset in a target area; filtering out a large airport point dataset from the airport point dataset according to a preset first traffic volume threshold; A distance threshold and the large-scale airport point dataset, and a large-scale city point dataset is selected from the city point dataset; according to a preset second distance threshold, a second traffic threshold, and the large-scale city point dataset , and filter out the multi-airport system of each large city from the airport point dataset. This embodiment effectively matches the airport and the city, and improves the accuracy of identification.

Description

Multi-airport system identification method based on spatial organization characteristics

technical field

Embodiments of the present invention relate to the field of air transportation, and in particular, to a method for identifying a multi-airport system based on spatial organization characteristics.

Background technique

The multi-airport system is a typical cross-regional service-oriented major infrastructure, which refers to an airport collection formed by two or more large-scale civil airports and other civil airports that provide commercial transportation services in a metropolitan area (corresponding to a large city).

In the prior art, it is difficult to effectively match airports and cities based on the identification of multi-airport systems based on airport distances; other methods mistakenly identify airport groups as multi-airport systems, resulting in low identification accuracy.

SUMMARY OF THE INVENTION

The embodiment of the present invention provides a multi-airport system identification method based on spatial organization characteristics, in order to overcome the problems existing in the related art, the airport and the city are effectively matched, and the accuracy of identification is improved.

In a first aspect, an embodiment of the present invention provides a multi-airport system identification method based on spatial organization characteristics, including:

Obtain airport point datasets and city point datasets within the target area;

According to the preset first traffic volume threshold, filter out a large-scale airport point dataset from the airport point dataset;

According to the preset first distance threshold and the large-scale airport point data set, filter out the large-scale city point data set from the city point data set;

According to the preset second distance threshold, the second traffic threshold, and the large city point data set, the multi-airport system of each large city is selected from the airport point data set.

In a second aspect, an embodiment of the present invention provides an electronic device, including:

one or more processors;

memory for storing one or more programs,

When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned method for identifying a multi-airport system based on spatial organization characteristics.

In a third aspect, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements the above-mentioned method for identifying a multi-airport system based on spatial organization characteristics.

The embodiment of the present invention establishes valid airport and city point data sets by screening airports and cities that meet corresponding conditions within a spatial range, analyzes and mines the spatial organization characteristics of a multi-airport system, and then identifies the multi-airport system. This embodiment effectively matches airports and cities, which greatly improves the accuracy of multi-airport system identification.

Description of drawings

In order to illustrate the specific embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the specific embodiments or the prior art. Obviously, the accompanying drawings in the following description The drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

FIG. 1 is a flowchart of a method for identifying a multi-airport system based on a spatial organization feature provided by an embodiment of the present invention.

FIG. 2 is an operation process diagram of a method for identifying a multi-airport system based on a spatial organization feature provided by an embodiment of the present invention.

FIG. 3 is an example diagram of the spatial distribution of each identified multi-airport system provided by an embodiment of the present invention.

FIG. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be described clearly and completely below. Obviously, the described embodiments are only some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.

In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. The indicated orientation or positional relationship is based on the orientation or positional relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the indicated device or element must have a specific orientation or a specific orientation. construction and operation, and therefore should not be construed as limiting the invention. Furthermore, the terms "first", "second", and "third" are used for descriptive purposes only and should not be construed to indicate or imply relative importance.

In the description of the present invention, it should also be noted that, unless otherwise expressly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it may be a fixed connection or a connectable connection. Detachable connection, or integral connection; may be mechanical connection or electrical connection; may be direct connection, or indirect connection through an intermediate medium, or internal communication between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood in specific situations.

FIG. 1 is a flowchart of a method for identifying a multi-airport system based on a spatial organization feature provided by an embodiment of the present invention. This method is suitable for the situation of real-time dynamic identification of regional airport system through airport data and city data. The method is executed by an electronic device, as shown in FIG. 1 , and specifically includes the following steps.

S110. Acquire an airport point dataset and a city point dataset in the target area.

Optionally, the airport point data set includes: the airport's flight volume, passenger throughput, cargo throughput, and longitude and latitude; the city point data set includes: city code, population, country, and longitude and latitude.

Specifically, first of all, the target area to be studied is established. For example, if you want to evaluate the world, the world is determined as the target area. Then, the distribution location data of the airports to be evaluated and the city distribution location data in the target area are obtained. Optionally, the data are collected from the Airports Council International (ACI), the OurAirports data platform, and the world city database. Finally, create corresponding airport point datasets and city point datasets based on these data. Optionally, use the "XY to point" tool of ArcGIS software to generate airport point datasets and city point datasets.

S120. According to a preset first traffic volume threshold, filter out a large-scale airport point dataset from the airport point dataset.

From the airport point data set, a large airport whose traffic volume is greater than a preset first traffic volume threshold is screened out, and the large airport point data set is constituted by the data set of the large airport. Optionally, the first traffic volume threshold is passenger traffic volume=10 million person-times/year, or freight traffic volume=1 million tons/year. More specifically, the airport point data point set is denoted as A, the first traffic volume threshold is denoted as T, and large airports with a traffic volume greater than T are screened out from A, and the large airport data set is composed of the data set of large airports, denoted as A*.

S130. According to a preset first distance threshold and the large-scale airport point data set, filter out a large-scale city point data set from the city point data set.

Optionally, first, take each large airport in the large airport point data set as a starting point, and search for at least one city in the city point data set whose distance from the starting point is within a preset first distance threshold; select The city with the largest population in the at least one city is used as a large city corresponding to each large airport; the data sets corresponding to the repeated large cities are merged to obtain a large city point data set.

More specifically, starting from each large airport in A*, and taking a certain airport-metropolitan distance (ie, the first distance threshold, denoted as R) as the radius, the city point dataset C is searched for the one with the largest population size. The first city, as a large city. Each large airport corresponds to a large city, and the repeated parts of all large cities are merged to obtain a large city point dataset C*.

S140. According to the preset second distance threshold, the second traffic threshold, and the large city point data set, filter out the multi-airport system of each large city from the airport point data set.

Optionally, first, take each large city in the large city point dataset as a starting point, and search from the airport point dataset for at least one airport whose distance from the starting point is within a preset second distance threshold; An airport whose traffic volume is greater than the second traffic volume threshold is selected from the at least one airport to form a multi-airport system of each large city.

More specifically, take each large city in the large city point dataset C* as the starting point, take the second distance threshold d as the radius, and search the airport point dataset A for all airports corresponding to the second distance threshold, by searching for The multiple airports whose traffic volume meets the second traffic volume threshold (α) form a multi-airport system of the current city. Usually, each large city is regarded as a metropolitan area m, and there is a multi-airport system with each metropolitan area as the core.

Further, if an airport _ak in the target area falls within the service space range d _j of the metropolitan area m _j (ie, within the above range), denote δ _jk =1, otherwise δ _jk =0. According to the definition of the multi-airport system, assuming that the traffic volume threshold of the multi-airport system is α, the following judgment relationship is established:

γ _k is the value of the traffic judging function of the airport a _k as a member airport of the multi-airport system. For a certain metropolitan area m _j , the corresponding number of airports S _j in the multi-airport system has the following relationship:

If S _j ≥ 2, it means that there is a multi-airport system in the corresponding metropolitan area M _j .

In this embodiment, the airports and cities that meet the corresponding conditions are screened in the spatial range, an effective airport and city point data set is established, the spatial organization characteristics of the multi-airport system are analyzed and excavated, and the multi-airport system is further identified. This embodiment effectively matches airports and cities, which greatly improves the accuracy of multi-airport system identification.

The above technical solution of the present invention will be described in detail below with a specific embodiment.

The target region of this embodiment is the world, including 6 regions of Africa, Asia Pacific, Europe, Latin America-Caribbean, Middle East, and North America, covering 182 countries/regions. The data source mainly includes two parts:

(1) Global airport data. The airport data comes from the 2019 edition of the World Airport Traffic Dataset (https://store.aci.aero/product/annual-world-airport-traffic-dataset-2019/) of the Airports Council International (ACI). It contains the flight volume, passenger volume and cargo volume data of more than 2,500 airports around the world in 2018. It is the most authoritative airport traffic data set in the aviation industry. In addition, the airport point data comes from the OurAirports data platform (https://ourairports.com/).

(2) World city data. The city data comes from the World Cities Database (https://simplemaps.com/data/world-cities), which covers the population and spatial data of nearly 41,000 major cities/town units around the world. Since the database uses real population clusters rather than administrative divisions as city/town units, estimation biases that may be caused by different standards for dividing city boundaries are avoided. At the same time, the World Cities Database is constructed on the basis of data provided by the National Geospatial-Intelligence Agency, the US Geological Survey, the US Census Bureau and NASA, so it has high reliability.

(3) Other data come from the World Bank (https://data.worldbank.org/) and OpenStreetMap (http://www.openstreetmap.org/).

After obtaining the data source, determine the various thresholds in the method. Optionally, the first distance threshold is equal to the second distance threshold, which is used to represent the urban space service distance threshold (R); the first distance threshold is greater than the second distance threshold, wherein the first distance threshold is represented by: The second distance threshold is used to characterize the traffic threshold (T) of the large airport system, and the second distance threshold is used to characterize the traffic threshold (α) of the multi-airport system.

Specifically, the first traffic volume threshold is determined in the following manner. According to the relevant standards of the Federal Aviation Administration of the United States, the annual flight volume threshold of large airports in the metropolitan airport system is 100,000 sorties. According to this standard, the passenger throughput is about 10 million. my country's "National Comprehensive Airport System Classification Framework" believes that the passenger throughput of large airports should account for more than 1% of the country. Since the annual passenger throughput of my country's airports in 2018 was 1.264 billion passengers, the threshold for passenger throughput of large airports is about 12 million. . According to the above two classification standards, and with reference to the conversion method of passenger and cargo business volume "1 passenger volume = 0.1 ton freight volume", it is proposed to take 10 million passengers per year or 1 million tons of annual cargo throughput as the first business volume threshold , which is the large airport traffic threshold (T). Large airports respectively concentrated 72.62% of the global air passenger traffic and 84.94% of the air cargo traffic in 2018, occupying a key position in the global civil aviation system.

The first distance threshold and the second distance threshold are determined in the following manner. Existing studies have not reached a consensus on the threshold of urban space service based on air transportation functions, and have used distances such as 100 kilometers, 1.5 hours, and 3 hours as the service radius. However, this type of service radius is more suitable for national-scale research. In my country, passengers within 100 kilometers of the airport have only average evaluations of the convenience of air services. At the city (or metropolitan area) scale, considering the economic, social and environmental effects of the airport (or airport) itself and the entire city, it is appropriate that the distance between the airport and the city center is within 30 kilometers; at the same time, the world's 66 major The distance from the airport to the city center is less than 70 kilometers. In view of this, 70 kilometers is used as the first distance threshold and the second distance threshold, ie, the urban space service distance threshold (R).

The second traffic volume threshold is determined in the following manner. Referring to the relevant parameters used by the FAA (the annual number of boarding passengers is greater than 250,000) and related research (the annual number of seats is greater than 600,000), the second traffic volume threshold (that is, the multi-airport system traffic volume threshold α) is set as The annual passenger throughput of 500,000 people or the annual cargo throughput of 50,000 tons is included in the identification category.

After each threshold is determined, the following operations are performed: ① Take the annual passenger throughput of 10 million people or the annual cargo throughput of 1 million tons as the threshold, and select the large-scale airport dataset from the airport point dataset. ②The core city with the largest population is searched with a large airport as the center and a radius of 70 kilometers, and a large city data set is obtained. ③ Take a large city as the starting point and a radius of 70 kilometers to search for civil airports, and use two or more civil airports within the range as the screening criteria to obtain a list of core cities and their covered airports. ④Data cleaning to get a list of multi-airport systems. The above operation process is shown in Figure 2.

FIG. 3 is an example diagram of the spatial distribution of the identified multi-airport systems provided by the embodiment of the present invention. As shown in FIG. 3 , a total of 61 multi-airport systems in the world have been identified, including 146 civil airports. important position.

FIG. 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. As shown in FIG. 4 , the device includes a processor 50, a memory 51, an input device 52, and an output device 53; the number of processors 50 in the device may be One or more, a processor 50 is taken as an example in FIG. 4; the processor 50, the memory 51, the input device 52 and the output device 53 in the device can be connected through a bus or other means, and the connection through a bus is taken as an example in FIG. 4 .

As a computer-readable storage medium, the memory 51 can be used to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the method for identifying a multi-airport system based on spatial organization features in the embodiment of the present invention. The processor 50 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 51, ie, realizes the above-mentioned method for identifying a multi-airport system based on spatial organization characteristics.

The memory 51 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. In addition, the memory 51 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, memory 51 may further include memory located remotely from processor 50, which may be connected to the device through a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

The input device 52 may be used to receive input numerical or character information and to generate key signal input related to user settings and function control of the device. The output device 53 may include a display device such as a display screen.

Embodiments of the present invention also provide a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements the method for identifying a multi-airport system based on a spatial organization feature of any embodiment.

The computer storage medium in the embodiments of the present invention may adopt any combination of one or more computer-readable mediums. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above. More specific examples (a non-exhaustive list) of computer readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), Erasable Programmable Read Only Memory (EPROM or Flash), fiber optics, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

A computer-readable signal medium may include a propagated data signal in baseband or as part of a carrier wave, with computer-readable program code embodied thereon. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device .

Program code embodied on a computer readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including object-oriented programming languages—such as Java, Smalltalk, C++, but also conventional procedural languages, or a combination thereof. Programming Language - such as the "C" language or similar programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or wide area network (WAN), or may be connected to an external computer (eg, through the Internet using an Internet service provider) connect).

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The technical solutions described in the foregoing embodiments can still be modified, or some or all of the technical features thereof can be equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention.

Claims (10)

1. A multi-airport system identification method based on spatial organization characteristics is characterized by comprising the following steps:

acquiring an airport point data set and a city point data set in a target area;

screening a large airport point data set from the airport point data set according to a preset first traffic threshold;

screening out a large city point data set from the city point data set according to a preset first distance threshold value and the large airport point data set;

and screening out a multi-airport system of each large city from the airport point data set according to a preset second distance threshold, a second traffic threshold and the large city point data set.

2. The method of claim 1, wherein the airport point data set comprises: flight volume, passenger traffic throughput, freight throughput, and latitude and longitude of the airport.

3. The method of claim 1, where the city point data set comprises: city code, population, country of residence, and latitude and longitude.

4. The method of claim 1, wherein the screening out a mainframe site data set from the airport point data sets according to a preset traffic threshold comprises:

and screening out large airports with the traffic volume larger than a preset traffic volume threshold value from the airport point data set, and forming the large airport point data set by the data set of the large airports.

5. The method of claim 1, wherein said screening out a large city point data set from said city point data set according to a preset first distance threshold and said large airport point data set comprises:

taking each large airport in the large airport point data set as a starting point, and searching at least one city with the distance from the starting point within a preset first distance threshold value in the city point data set;

selecting the city with the largest population scale in the at least one city as the large city corresponding to each large airport;

and merging the repeated data sets corresponding to the large cities to obtain the data sets of the large cities.

6. The method of claim 1, wherein said screening the airport point data sets for multi-airport systems for each large city based on a preset second distance threshold, a second traffic threshold, and the large city point data sets comprises:

taking each large city in the large city point data set as a starting point, and searching at least one airport with the distance from the starting point within a preset second distance threshold value from the airport point data set;

and selecting the airports with the traffic volume larger than a second traffic volume threshold value from the at least one airport to form a multi-airport system of each large city.

7. The method of claim 6, wherein said selecting airports from said at least one airport having traffic greater than a second traffic threshold, for forming multi-airport systems for each metropolitan area, comprises:

and if a plurality of airports with the traffic volume larger than a second traffic volume threshold exist in the at least one airport, forming a corresponding multi-airport system of the large city by the plurality of airports.

8. The method of claim 1, wherein the first distance threshold is equal to the second distance threshold; the first traffic threshold is greater than the second traffic threshold.

9. An electronic device, comprising:

one or more processors;

a memory for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the method for multi-airfield system identification based on spatial organisation features according to any one of claims 1 to 8.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the method for multi-airfield system identification based on spatial organisation features according to any one of claims 1 to 8.

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