Airline planning method, system and device and computer program product

Abstract

The invention relates to the technical field of aviation planning design, in particular to a route planning method, system and device and a computer program product, and the method comprises the steps: obtaining airport data and GDP data of a city where each airport is located; calculating a gravitation coefficient between airports according to the airport data and GDP data; and planning the route between the airports based on the gravity coefficient. The airline planning scheme provided by the invention is combined with feature analysis, an improved gravitational model is constructed, the method and the system for determining the gravitational relationship between the aviation network airports are provided for airline personnel, agents and airport personnel, and the problem of uncertainty of the gravitational relationship between the airports is solved. Through the pattern relationship between airports, flight data and the like, the difference of the transportation development level of each airport is found, the airport space structure is optimized, and support is provided for improving the airport service quality.

Description

Technical Field

[0001] The present disclosure relates to the technical field of aviation planning and design, and in particular to a route planning method, system, device and computer program product. Background Art

[0002] The nodes of the aviation network are composed of airports, and the relationship between airports has a profound impact on the aviation network. At present, the research on airports mainly focuses on the following aspects:

[0003] (1) Network structure: Currently, the route network structure of airports is mainly divided into hub-and-spoke, point-to-point or hybrid. Hub-and-spoke networks are usually built around one or several large hub airports, with small airports acting as feeder lines to transport passengers to the hubs, which then connect to long-distance or international routes. Point-to-point networks directly establish routes between multiple airports and are suitable for routes with large and stable demand.

[0004] (2) Market competitiveness: Hub airports are more competitive because they can offer more transfer options and more frequent flights, attracting more passengers. Small or regional airports may rely on specific local markets or tourist destinations and have relatively fewer routes and flight frequencies.

[0005] (3) Economic benefits: Large hub airports can generate economies of scale, reduce operating costs, and drive economic development in surrounding areas. Local governments and businesses may invest in small airports to promote the regional economy, but this often requires subsidies to maintain operations.

[0006] Currently, the research on the quantification of the relationship between airports mainly uses the following simple statistical indicators: the number of destinations accessible by the airport, the number of base airlines, flight frequency, the number of inbound and outbound passengers, etc. There is no model that takes into account the global network. Summary of the invention

[0007] The present disclosure aims to solve at least one of the technical problems in the above-mentioned technologies to a certain extent, and proposes a route planning method, comprising:

[0008] Obtain airport data and GDP data of the cities where each airport is located;

[0009] Calculate the attraction coefficient between the airports based on the airport data and GDP data;

[0010] The routes between the airports are planned based on the gravity coefficient.

[0011] Furthermore, the flight data includes: the distance between airports, the number of flights between airports, the number of airlines at airports, the number of boarding gates at airports, the passenger throughput of airports and the number of flights at airports.

[0012] Furthermore, the gravitational coefficient between the airfields is calculated based on the flight data and the GDP data, and the formula used includes:

[0013]

[0014] Among them, I i→j represents the gravitational coefficient between airport i and airport j; GDP i represents the GDP of the city where airport i is located; GDP j represents the GDP of the city where airport j is located; L i→j represents the number of flights between airport i and airport j; M i represents the principal component of airport data of airport i; M j represents the principal component of airport data of airport j; h ij represents the distance between airport i and airport j.

[0015] Furthermore, the principal component of the airport data is obtained by performing principal component analysis on the airport data, including:

[0016] Standardize each item of airport data for the target airport;

[0017] Construct a correlation coefficient matrix based on the standardized airport data;

[0018] Solving the characteristic roots and eigenvectors of the covariance matrix corresponding to the correlation coefficient matrix;

[0019] The principal components of the airport data are selected according to the characteristic roots and the characteristic vectors.

[0020] Furthermore, the routes between the airports are planned based on the gravity coefficient, including:

[0021] Sort and grade the attraction coefficients between the target airport and other airports;

[0022] Plan routes between the target airport and other airports based on the gravity coefficient level.

[0023] The present disclosure also proposes a route planning system, comprising:

[0024] The data processing module is configured to: obtain airport data and GDP data of the cities where each airport is located;

[0025] A gravity calculation module is configured to: calculate the gravity coefficient between the airports according to the airport data and the GDP data;

[0026] The route planning module is configured to plan routes between airports based on the gravity coefficient.

[0027] Furthermore, the gravity calculation module uses the formula used to calculate the gravity coefficient between the airfields according to the flight data and the GDP data, including:

[0028]

[0029] Among them, I i→j represents the gravitational coefficient between airport i and airport j; GDP i represents the GDP of the city where airport i is located; GDP j represents the GDP of the city where airport j is located; L i→j represents the number of flights between airport i and airport j; M i represents the principal component of airport data of airport i; M j represents the principal component of airport data of airport j; h ij represents the distance between airport i and airport j.

[0030] Furthermore, the route planning module executes a method comprising:

[0031] Sort and grade the attraction coefficients between the target airport and other airports;

[0032] Plan routes between the target airport and other airports based on the gravity coefficient level.

[0033] The present disclosure also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program or instruction, and when the computer program or instruction is executed by the processor, it is at least used to implement the above-mentioned route planning method.

[0034] The present disclosure also proposes a computer-readable storage medium, in which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, it is at least used to implement the above-mentioned route planning method.

[0035] The present disclosure also proposes a computer program product, which is stored in a computer-readable storage medium, and is characterized in that when the computer program product is executed by a processor, it is at least used to implement the above-mentioned route planning method.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] The route planning scheme provided in this disclosure combines feature analysis to construct an improved gravity model, providing airline personnel, agents and airport personnel with a method and system for determining the gravity relationship between airports in an aviation network, solving the uncertainty problem of the gravity relationship between airports. Through the pattern relationship between airports, flight data, etc., the differences in the transportation development levels of various airports are discovered, the airport spatial structure is optimized, and support is provided for improving the quality of airport services.

[0038] Other features and advantages of the present disclosure will be described in the subsequent description, and partly become apparent from the description, or be understood by practicing the present disclosure. The purpose and other advantages of the present disclosure can be realized and obtained by the structures particularly pointed out in the written description and the drawings. The technical solution of the present disclosure is further described below through drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0040] Figure 1 The flow chart of construction and evaluation of the gravity model between airports given in the embodiment;

[0041] Figure 2 A schematic diagram of a correlation coefficient matrix is ​​provided for an embodiment;

[0042] Figure 3 A schematic diagram of total variance explanation of principal component analysis given in the embodiment;

[0043] Figure 4 A schematic diagram of the ingredient matrix provided for the embodiment;

[0044] Figure 5 A schematic diagram of a route planning system provided in an embodiment;

[0045] Figure 6 A schematic diagram of an electronic device provided for an embodiment;

[0046] Figure 7 A schematic diagram of a computer-readable storage medium is provided for an embodiment. DETAILED DESCRIPTION

[0047] The present disclosure is described below in conjunction with the accompanying drawings. The preferred embodiments described herein are only used to illustrate and explain the present disclosure and are not used to limit the present disclosure.

[0048] The present disclosure proposes a route planning method, the method comprising:

[0049] Obtain airport data and GDP data of the cities where each airport is located;

[0050] Calculate the attraction coefficient between the airports based on the airport data and GDP data;

[0051] The routes between the airports are planned based on the gravity coefficient.

[0052] Furthermore, the flight data includes: airport passenger throughput, airport flight volume, airport airline number, airport boarding gate number, airport distance between airports and flight volume between airports.

[0053] Furthermore, the gravitational coefficient between the airfields is calculated based on the flight data and the GDP data, and the formula used includes:

[0054]

[0055] Among them, I i→j represents the gravitational coefficient between airport i and airport j; GDP i represents the GDP of the city where airport i is located; GDP j represents the GDP of the city where airport j is located; L i→j represents the number of flights between airport i and airport j; M i represents the principal component of airport data of airport i; M j represents the principal component of airport data of airport j; h ij represents the distance between airport i and airport j.

[0056] Furthermore, the routes between the airports are planned based on the gravity coefficient, including:

[0057] Sort and grade the attraction coefficients between the target airport and other airports;

[0058] Plan routes between the target airport and other airports based on the gravity coefficient level.

[0059] According to some embodiments of the present disclosure, Figure 1 As shown in the figure, the process of constructing and evaluating the gravity model between airports includes:

[0060] The first step is data acquisition. Collect and organize flight information related data, query requests in real time, obtain airport passenger throughput information and GDP data of the cities where each airport is located according to the different role permissions registered by the user, build a database based on the above flight data and GDP data, obtain and refresh the above data regularly, and store the query results.

[0061] The second step is to build a gravity model. The gravity model is widely used in the connection of network space structure. The gravity model can be used to measure the gravity generated by the airport nodes in the network. The classic gravity model is expressed as follows:

[0062] I ij =(GM i M j ) / d ij 2

[0063] Among them, I ij represents the gravitational force between airport i and airport j; G represents the gravitational coefficient, which is usually 1; M i represents the passenger throughput of airport i, M j represents the passenger throughput of airport j; d ij represents the distance between airport i and airport j.

[0064] In the classical gravity model, each parameter value can achieve better results in specific situations. However, in practical applications, not every usage environment meets the parameter selection criteria. Therefore, it is necessary to improve the parameter selection according to the model usage environment.

[0065] Furthermore, the principal components of airport data are obtained by performing principal component analysis on the airport data, including:

[0066] Standardize each item of airport data for the target airport;

[0067] Construct a correlation coefficient matrix based on the standardized airport data;

[0068] Solving the characteristic roots and eigenvectors of the covariance matrix corresponding to the correlation coefficient matrix;

[0069] Calculate the cumulative contribution rate of each airport data according to the characteristic root and the characteristic vector;

[0070] The principal components of the airport data are determined based on the cumulative contribution rates.

[0071] According to some embodiments of the present application, the principal component analysis method is used to extract the principal components of the airport data parameters. Define x1 as the passenger throughput, x2 as the number of flights, x3 as the number of airlines, and x4 as the number of boarding gates. Construct a sample matrix based on the original data, and use SPSS software to standardize the original data to eliminate the influence of the variables in terms of level and dimension. Calculate the correlation coefficient matrix based on the standardized sample matrix, and calculate the characteristic roots and eigenvectors of the covariance matrix corresponding to the correlation coefficient matrix. Calculate the principal component sharing rate and cumulative contribution rate, and sort them from large to small, and select the first few as principal components. The processing results are as follows: Figure 2-Figure 4 shown. Figure 2 It is a correlation coefficient matrix (correlation matrix), and the values ​​in the table represent the correlation of the airport data parameters corresponding to the grid. Figure 3 is the total variance explained by this principal component analysis, Figure 4The final component matrix (loading matrix) is shown in Figure 2. In summary, after analysis using SPSS software, only one component eigenvalue is greater than 1 and its contribution rate is 94.313%, so a principal component, namely x1 passenger throughput, is extracted.

[0072] This embodiment improves the parameters from four aspects: (1) the actual flight distance between airports. In actual flight, the distance from one airport to another is not equal to the theoretical straight-line distance. Compared with the theoretical straight-line distance, the actual flight distance is more valuable for reference. (2) Airport passenger throughput, which can better describe the comprehensive quality of each airport. (3) The number of flights between airports. (4) The GDP of the city where the airport is located. GDP is considered to be an important indicator. A larger GDP usually means that the city has a larger economic scale, more economic activities, richer resources, etc. According to the assumption of the gravity model, the single trade flow or mutual attraction between two cities is proportional to their respective economic scale (expressed in GDP). In other words, the larger the economic scale of the city, the stronger the mutual attraction between them may be, and the more frequent or closer the economic connection, trade exchanges, personnel flow, etc. between them may be. The addition of GDP to the gravity model is mainly to measure the impact of the economic scale or economic strength between the cities where the two airports are located on the interaction or connection between the airports. Therefore, adding the GDP of the city where the airport is located as a parameter for airport gravity calculation can better reflect the gravitational relationship between airports. In summary, in this embodiment, the formula for the attraction value between airports is calculated based on four factors: city GDP, airport passenger throughput, number of flights between airports, and actual flight distance, as shown below:

[0073]

[0074] Among them, I i→j represents the gravitational coefficient between airport i and airport j; GDP i represents the GDP of the city where airport i is located; GDP j represents the GDP of the city where airport j is located; L i→j represents the number of flights between airport i and airport j; h ij represents the actual flight distance between airport i and airport j, M i represents the passenger throughput of airport i; M j represents the passenger throughput of airport j.

[0075] The third step is to calculate the gravity between every two airports. Based on parameters such as airport passenger throughput, actual flight distance, etc., the gravity within the area is calculated through a model.

[0076] The fourth step is to evaluate the gravity intensity values ​​between airports. The evaluation results are low gravity value range, medium gravity value range, and high gravity value range. According to different evaluation results, there will be different evaluations on the routes. For example, routes evaluated as high gravity values ​​are usually suitable for large jet aircraft to meet strong passenger flow needs.

[0077] The fifth step is to plan routes based on the gravity between airports. An important step in route planning is to maximize economic benefits while ensuring that the number of flights meets passenger demand. This goal can be achieved by optimizing routes based on the calculation results of the gravity model.

[0078] The following table shows the flight data and corresponding GDP data given in an embodiment of the present disclosure:

[0079]

[0080]

[0081] Table 1

[0082] Based on the above data, it can be calculated that the attraction between Beijing / Capital and Shenzhen / Bao'an airports is 165; the attraction between Shenzhen / Bao'an and Chengdu Tianfu airports is 98; the attraction between Chengdu / Tianfu and Chongqing / Jiangbei airports is 2; the attraction between Chongqing / Jiangbei and Kunming / Changshui airports is 22; the attraction between Kunming / Changshui and Xi'an / Xianyang airports is 45; the attraction between Xi'an / Xianyang and Hangzhou / Xiaoshan airports is 43; the attraction between Hangzhou / Xiaoshan and Nanjing / Lukou airports is 1.

[0083] In this embodiment, a relative comparison method is used to evaluate the gravity intensity value between airports, that is, for any airport, the gravity value between it and other airports is ranked. The gravity value in the first 25% range is relatively high, indicating that the passenger demand between the two airports is very strong. Such routes are usually the main routes between some major domestic hub airports. Such routes are usually suitable for large jet aircraft to meet strong passenger demand. The gravity value in this range is relatively small, indicating that the passenger demand between the two airports is relatively low; the gravity value in the middle 50% range is at a medium level, indicating that the passenger demand between the two airports is relatively stable. Such routes are usually some medium-distance routes with strong regionality, or trunk routes between some medium-sized cities. Such routes can consider using medium-sized jet aircraft to meet certain passenger demand; the last 25% of such routes are usually some short-distance routes with weak regionality, or branch routes in remote areas. Such routes are usually not suitable for large aircraft to fly, and smaller models such as turboprop aircraft can be considered.

[0084] The strength of the gravity value needs to be analyzed and judged in combination with the actual situation. There are relative comparison method, historical change method, absolute value division method and other standard division methods. This embodiment uses the relative comparison method for division. However, in actual application, it is also necessary to consider the specific market environment and policy factors to formulate and select appropriate division standards, and adjust the division standards in a timely manner.

[0085] In addition, an important step in route planning is to meet the passenger flow demand with the appropriate number of flights. According to the calculation results of the gravity model, the route can be optimized and adjusted. First, high gravity value routes can be identified. High gravity value routes usually represent trunk routes with strong passenger flow demand and should be given priority. Consider increasing flight density to meet strong passenger flow demand. This can be achieved by increasing flight frequency or using larger aircraft. Optimize medium gravity value routes. For routes with gravity values ​​in the medium range, appropriate adjustments should be made according to specific circumstances. According to the changes in passenger flow in peak and off-season, the flight density can be reasonably adjusted. Appropriately increase flights during the peak season and appropriately reduce flights during the off-season to improve resource utilization. Reduce low gravity value routes. For routes with lower gravity values, passenger flow demand is usually weak, and flight density should be appropriately reduced. Consider using smaller aircraft or reducing flight frequency to control costs. It is also possible to temporarily suspend some routes with the lowest gravity values ​​according to specific circumstances. The gravity value between airports also needs to be regularly evaluated and dynamically adjusted. The actual passenger flow changes of each route need to be continuously monitored, and flight plans should be adjusted in a timely manner according to passenger flow changes to ensure the rationality of resource allocation. The trend of gravity value changes should be regularly evaluated, and forward-looking route network planning should be carried out accordingly.

[0086] Based on the same technical concept, such as Figure 5 As shown, the present disclosure also proposes a route planning system, including: a data processing module, configured to obtain flight data of each airport and GDP data of the city where each airport is located; a gravity calculation module, configured to calculate the gravity coefficient between airports based on the flight data and GDP data; a route planning module, configured to plan routes between airports based on the gravity coefficient.

[0087] Furthermore, the gravity calculation module uses the formula used to calculate the gravity coefficient between the airfields according to the flight data and the GDP data, including:

[0088]

[0089] Among them, I i→j represents the gravitational coefficient between airport i and airport j; GDP i represents the GDP of the city where airport i is located; GDP j represents the GDP of the city where airport j is located; L i→j represents the number of flights between airport i and airport j; Mi represents the principal component of airport data of airport i; M j represents the principal component of airport data of airport j; h ij represents the distance between airport i and airport j.

[0090] Furthermore, the data processing module executes a method comprising:

[0091] Sort and grade the attraction coefficients between the target airport and other airports;

[0092] Plan routes between the target airport and other airports based on the gravity coefficient level.

[0093] like Figure 6 As shown, the present disclosure also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program or instruction, and when the computer program or instruction is executed by the processor, it is at least used to implement the above-mentioned route planning method.

[0094] like Figure 7 As shown, the present disclosure also proposes a computer-readable storage medium, in which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, it is at least used to implement the above-mentioned route planning method.

[0095] The present disclosure also proposes a computer program product, which is stored in a computer-readable storage medium, and is characterized in that when the computer program product is executed by a processor, it is at least used to implement the above-mentioned route planning method.

[0096] It is obvious that a person skilled in the art can make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is also intended to include these modifications and variations.

Claims

1. A route planning method, characterized in that: include: Obtain airport data and GDP data of the cities where each airport is located; Calculate the attraction coefficient between the airports based on the airport data and GDP data; The routes between the airports are planned based on the gravity coefficient.

2. The method according to claim 1, characterized in that The flight data include: the distance between airports, the number of flights between airports, the number of airlines at airports, the number of boarding gates at airports, the passenger throughput of airports and the number of flights at airports.

3. The method according to claim 2, characterized in that The formula used to calculate the attraction coefficient between the airfield and the GDP data includes: Among them, I i→j represents the gravitational coefficient between airport i and airport j; GDP i represents the GDP of the city where airport i is located; GDP j represents the GDP of the city where airport j is located; L i→j represents the number of flights between airport i and airport j; M i represents the principal component of airport data of airport i; M j represents the principal component of airport data of airport j; h ij represents the distance between airport i and airport j.

4. The method according to claim 3, characterized in that The principal components of the airport data are obtained by performing principal component analysis on the airport data, including: Standardize each item of airport data for the target airport; Construct a correlation coefficient matrix based on the standardized airport data; Solving the characteristic roots and eigenvectors of the covariance matrix corresponding to the correlation coefficient matrix; The principal components of the airport data are selected according to the characteristic roots and the characteristic vectors.

5. The method according to claim 3, characterized in that The routes between the airports are planned based on the gravity coefficient, including: Sort and grade the attraction coefficients between the target airport and other airports; Plan routes between the target airport and other airports based on the gravity coefficient level.

6. A route planning system, characterized in that: include: The data processing module is configured to: obtain airport data and GDP data of the cities where each airport is located; A gravity calculation module is configured to: calculate the gravity coefficient between the airports according to the airport data and the GDP data; The route planning module is configured to plan routes between airports based on the gravity coefficient.

7. The system according to claim 6, characterized in that The gravity calculation module uses the formula used to calculate the gravity coefficient between the airfields according to the flight data and the GDP data, including: Among them, I i→j represents the gravitational coefficient between airport i and airport j; GDP i represents the GDP of the city where airport i is located; GDP j represents the GDP of the city where airport j is located; L i→j represents the number of flights between airport i and airport j; M i represents the principal component of airport data of airport i; M j represents the principal component of airport data of airport j; h ij represents the distance between airport i and airport j.

8. The system according to claim 6, characterized in that The route planning module executes a method comprising: Sort and grade the attraction coefficients between the target airport and other airports; Plan routes between the target airport and other airports based on the gravity coefficient level.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program or instruction, which, when executed by a processor, is used to implement at least the method according to any one of claims 1 to 5.

10. A computer program product, the computer program product being stored in a computer-readable storage medium, characterized in that: When the computer program product is executed by a processor, it is used to implement at least the method according to any one of claims 1 to 5.

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