CN115221220B - Multi-airport system identification method based on spatial organization characteristics - Google Patents
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Abstract
The embodiment of the invention discloses a multi-airport system identification method based on spatial organization characteristics. The method comprises 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. The embodiment effectively matches the airport and the city and improves the accuracy of identification.
Description
Technical Field
The embodiment of the invention relates to the field of air transportation, in particular to a multi-airport system identification method based on spatial organization characteristics.
Background
The multi-airport system is a typical large infrastructure of cross-regional service type, and refers to an airport assembly formed by 2 or more large civil airports and other civil airports providing commercial transportation service in a city circle (corresponding to a large city).
In the prior art, a multi-airport system is identified based on airport distance, so that airports and cities are difficult to be effectively matched; in the other method, the airport group is identified as a multi-airport system by mistake, and the identification accuracy is low.
Disclosure of Invention
The embodiment of the invention provides a multi-airport system identification method based on spatial organization characteristics, which aims to overcome the problems in the related technology, effectively matches airports and cities and improves the identification accuracy.
In a first aspect, an embodiment of the present invention provides a method for identifying a multi-airport system based on spatial organization characteristics, including:
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.
In a second aspect, an embodiment of the present invention provides an electronic device, including:
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 above-described method for multi-airfield system identification based on spatial organization features.
In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the above-mentioned multi-airport system identification method based on spatial organization characteristics.
The embodiment of the invention screens airports and cities meeting corresponding conditions in a space range, establishes an effective airport and city point data set, analyzes and excavates the space organization characteristics of a multi-airport system, and further identifies the multi-airport system. The embodiment effectively matches the airport and the city, and greatly improves the accuracy of identifying the multi-airport system.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a multi-airport system identification method based on spatial organization features according to an embodiment of the present invention.
Fig. 2 is an operation process diagram of a multi-airport system identification method based on spatial organization characteristics according to an embodiment of the present invention.
Fig. 3 is an exemplary diagram of the spatial distribution of the identified multi-airport systems, according to 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 Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall 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. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplification of description, but do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in a specific case to those of ordinary skill in the art.
Fig. 1 is a flowchart of a method for identifying a multi-airport system based on spatial organization characteristics according to an embodiment of the present invention. The method is suitable for the condition of dynamically identifying the regional airport system in real time through airport data and city data. The method is executed by an electronic device, and specifically includes the following steps, as shown in fig. 1.
S110, acquiring an airport point data set and a city point data set in the target area.
Optionally, the airport point data set includes: flight volume, passenger traffic throughput, freight throughput and longitude and latitude of the airport; the city point data set includes: city code, population, country of residence, and latitude and longitude.
Specifically, first, a target area to be studied is established, for example, if the global area is to be evaluated, the global area is determined as the target area. And then, acquiring the distribution position data of the airport to be evaluated and the distribution position data of the city in the target area, wherein the data are acquired from an International airport council ACI, an OurAirports data platform and a world city database. And finally, creating a corresponding airport point data set and a corresponding city point data set according to the data, and optionally, generating the airport point data set and the city point data set by adopting an ArcGIS software 'XY point-turning' tool.
And S120, screening out a large airport point data set from the airport point data set according to a preset first traffic threshold.
And screening out large airports with the traffic volume larger than a preset first 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. Optionally, the first traffic threshold is passenger traffic =1000 ten thousand persons/year, or freight traffic =100 ten thousand tons/year. More specifically, the airport point data point set is marked as A, the first traffic threshold value is marked as T, large airports with traffic larger than T are screened from A, and the data sets of the large airports form a large airport data set which is marked as A x.
S130, 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.
Optionally, first, taking each large airport in the large airport point data set as a starting point, and searching for at least one city in the city point data set, where a distance from the starting point is within a preset first distance threshold; 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.
More specifically, with each large airport in a as a starting point and a certain airport-city circle distance (i.e., a first distance threshold, denoted as R) as a radius, the first city with the largest population size is searched for in the city point data set C as a large city. And each large airport corresponds to a large city, and repeated parts in all the large cities are merged to obtain a large city point data set C.
S140, screening out a multi-airport system of each large city from the airport point data set according to a preset second distance threshold, a preset second traffic threshold and the large city point data set.
Optionally, first, taking each large city in the large city point data set as a starting point, and searching at least one airport whose distance from the starting point is within a preset second distance threshold 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.
More specifically, each large city in the large city point data set C is taken as a starting point, the second distance threshold d is taken as a radius, all airports corresponding to the second distance threshold are searched in the airport point data set a, and the searched airports with the traffic satisfying the second traffic threshold (α) form a multi-airport system of the current city. Usually, each large city is regarded as a city circle m, and a multi-airport system exists with each city circle as a core.
Further, if a certain airport a in the target area k Circle of falling into city m j Service space range d j Within (i.e., within the above range), δ is recorded jk =1, otherwise δ jk And =0. According to the definition of a multi-airport system, assuming that the traffic threshold value of the multi-airport system is alpha, establishing the following judgment relation:
γ k is an airport a k And the function value is judged as the traffic of the member airports of the multi-airport system. For a certain urban circle m j Corresponding airport number S in a multi-airport system j The following relationships exist:
if S is j More than or equal to 2, the corresponding urban circle M is represented j There is a multi-airport architecture.
In the embodiment, airports and cities meeting corresponding conditions are screened in a space range, an effective airport and city point data set is established, the space organization characteristics of a multi-airport system are analyzed and mined, and further the multi-airport system is identified. The embodiment effectively matches the airport and the city, and greatly improves the accuracy of identifying the multi-airport system.
The above technical solution of the present invention is described in detail by a specific example.
The region of interest for this example is global and includes 6 regions of africa, asia-pacific, europe, latin american-caribbean, the middle east and north america covering 182 countries/regions. The data source mainly comprises two parts:
(1) Global airport data. Airport data is derived from 2019 version of world airport traffic data set (https:// store. ACI. Aero/product/annual-world-airport-traffic-set-2019 /) of International Airport Council (ACI), contains flight volume, passenger volume and cargo volume data of 2500 global Airports in 2018, and is the airport traffic data set which is the most authoritative for the aviation industry at present. Further, airport point location data is sourced from the OurAirports data platform (https:// outlorts. Com /).
(2) World city data. City data comes from the world city database (https:// simplemaps. Com/data/world-cities), covering population and space data of nearly 4.1 million major city/town units worldwide. Since the database uses real population aggregations rather than administrative divisions as city/town units, estimation bias that may be caused by non-uniform city boundary division criteria is avoided. Meanwhile, the world city database is constructed on the basis of data provided by the U.S. national geographic information service, the U.S. geological survey, the U.S. census and the U.S. aerospace service, so that the world city database has higher reliability.
(3) Other data originates from world banks (https:// data. World bank. Org /) and OpenStreetMap (http:// www. Opentreetmap. Org /) and so on.
After the data source is obtained, various threshold values in the method are determined. Optionally, the first distance threshold is equal to the second distance threshold, for characterizing a metro space service distance threshold (R); the first distance threshold is greater than the second distance threshold, wherein the first distance threshold is used to characterize a large airport traffic threshold (T) and the second distance threshold is used to characterize a traffic threshold (a) for a multi-airport architecture.
Specifically, the first traffic threshold is determined in the following manner. The united states Federal Aviation Administration (Federal Aviation Administration) related standard indicates that the annual flight number threshold of a large airport in an urban circle airport system is 10 ten thousand, and the passenger throughput is about 1000 ten thousand by conversion according to the standard. According to the 'national comprehensive airport system classification framework' in China, the passenger throughput of a large airport is considered to account for more than 1 percent of the proportion of the whole country, and the passenger throughput of the large airport is about 1200 ten thousand because the passenger throughput of the airport in 2018 in China is 12.64 hundred million people in all years. According to the two classification standards, and referring to a conversion method of passenger and cargo traffic volume "1 man times passenger transport volume =0.1 ton cargo transport volume", the passenger throughput of 1000 ten thousand times per year or the cargo throughput of 100 ten thousand times per year is supposed to be used as a first traffic threshold value, namely a large airport traffic threshold value (T). The large airports respectively concentrate 72.62% of air passenger traffic and 84.94% of air cargo traffic in the world in 2018, and occupy 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 achieved consensus on the metro space service threshold problem based on air transport functionality, using 100 km, 1.5 hours, 3 hours, and so on as the service radius. However, such service radiuses are more suitable for national-scale research, and in China, the evaluation of the convenience of aviation service by passengers within a range of 100 kilometers from an airport is only general. On a city (or metropolitan circle) scale, airports within 30 km from the center of the city are suitable in view of their economic, social and environmental effects from the entire city; meanwhile, 66 major airports in the world are all less than 70 kilometers away from the center of the city. In view of this, 70 km is used as the first and second distance thresholds, i.e. the metro space service distance threshold (R).
The second traffic threshold is determined in the following manner. And converting by referring to relevant parameters used by FAA (the annual boarding passenger quantity is more than 25 ten thousand) and relevant research (the annual seat number is more than 60 ten thousand), and setting a second traffic threshold (namely a multi-airport system traffic threshold alpha) as the annual passenger throughput of 50 ten thousand or the annual cargo throughput of 5 ten thousand tons, wherein airports larger than the threshold are included in the identification category.
After determining each threshold, the following operations are performed: (1) and screening out a large airport data set in the airport point data set by taking the annual passenger transport throughput of 1000 ten thousand people or annual cargo transport throughput of 100 ten thousand tons as a threshold value. (2) And searching the core city with the largest population scale by taking the large airport as the center and taking 70 kilometers as the radius to obtain a large city data set. (3) And (3) searching the civil airports by taking the large city as a starting point and 70 kilometers as a radius, and obtaining a core city and a coverage airport list thereof by taking 2 or more civil airports appearing in the range as screening conditions. (4) And cleaning the data to obtain a multiairport system list. The above operation is shown in fig. 2.
Fig. 3 is an exemplary illustration of the spatial distribution of the identified multi-airport systems according to the embodiment of the present invention, as shown in fig. 3, a global identification is obtained for 61 multi-airport systems, including 146 civil airports, which have a significant position in global civil aviation transportation.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 4, the electronic 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, and one 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 apparatus may be connected by a bus or other means, which is exemplified in fig. 4.
The memory 51 is a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the multi-airport system identification method 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 software programs, instructions and modules stored in the memory 51, so as to implement the above-mentioned multi-airport system identification method 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. Further, 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 examples, the memory 51 may further include memory located remotely from the processor 50, which may be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 52 is operable to receive input numeric or character information and to generate key signal inputs relating to user settings and function controls of the apparatus. The output device 53 may include a display device such as a display screen.
The embodiment of the invention also provides a computer-readable storage medium, on which a computer program is stored, and the program is executed by a processor to implement the multi-airport system identification method based on spatial organization features of any embodiment.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate 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 for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. 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 latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention.
Claims (8)
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; specifically, each large airport in the large airport point data set is taken as a starting point, and at least one city with the distance from the starting point within a preset first distance threshold value is searched 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; merging the data sets corresponding to the repeated large cities to obtain a large city point data set;
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; specifically, each large city in the large city point data set is used as a starting point, and at least one airport with the distance from the starting point within a preset second distance threshold value is searched 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.
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, wherein the city point data set comprises: city code, population, country of residence, and latitude and longitude.
4. The method of claim 1, wherein said screening out a mainframe site data set from said airport point data sets according to a preset first traffic threshold comprises:
and screening out large airports with the traffic volume larger than a preset first 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 selecting of airports from said at least one airport having traffic greater than a second traffic threshold value comprises a multi-airport system for each large city, comprising:
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.
6. 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.
7. 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 6.
8. 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 6.
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Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114548811A (en) * | 2022-03-01 | 2022-05-27 | 中国民用航空总局第二研究所 | Airport accessibility detection method and device, electronic equipment and storage medium |
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Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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Non-Patent Citations (7)
| Title |
|---|
| Airport Choice in Multi-airport Regions: An Empirical Study for Chinese Metropolitan Area;Jiang-tao Liu;《IEEE Xplore》;20081028;全文 * |
| 世界机场空间格局及对中国的启示;王姣娥等;《世界地理研究》;20080915(第03期);全文 * |
| 区域多机场系统航线网络优化模型;刘欣仪等;《航空计算技术》;20200325(第02期);全文 * |
| 我国城市多机场体系发展模式探寻;赵民合;《中国民用航空》;20130901(第09期);全文 * |
| 新型城镇化建设与机场网络化发展:中小机场的战略价值;王云访等;《综合运输》;20170215(第02期);全文 * |
| 机场群研究进展与展望;莫辉辉 等;《地理科学进展》;20211028;全文 * |
| 民航机场布局方法探讨;王姣娥等;《中国民航飞行学院学报》;20091115(第06期);全文 * |
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