Flight passenger diversion prediction method, system, device and medium

Abstract

The present invention discloses a method, system, device and medium for predicting the diversion of flight passengers. The method includes: determining a flight to be analyzed corresponding to a high-speed rail line to be opened; determining a first historical benchmark flight and a corresponding first historical high-speed rail line according to the flight to be analyzed; predicting the passenger loss information of the flight to be analyzed after the opening of the high-speed rail line to be opened according to the passenger loss information of the first historical benchmark flight after the opening of the first historical high-speed rail line. Based on the impact of the historically opened high-speed rail lines on the passenger loss of flights at historical benchmark airports, the impact of the currently opened high-speed rail lines on the passenger loss of the current benchmark airport is deduced in advance. This enables the airport to have sufficient time for operation adjustment.

Description

Technical Field

[0001] The present invention relates to the field of computers, and particularly to a method, system, device and medium for predicting the diversion of flight passengers. Background Art

[0002] In recent years, with the rapid development of our country and the continuous improvement of people's living standards, the travel demand of passengers has gradually increased, and the society's demand for air transportation has grown day by day. At the same time, China has vigorously developed the construction of domestic high-speed railways, continuously increased investment in railway construction, and promoted the completion of the Chinese high-speed rail network.

[0003] Due to the advantages of low ticket price, high punctuality rate, and little impact from weather, and large transportation volume and high departure frequency, high-speed rail has had an impact on the civil aviation market along its line since its birth. Civil aviation transportation is fast and has a more complete transportation network, and has more advantages in long-distance and international transportation. The rapid development of high-speed rail in China has brought new challenges and opportunities to airports. The opening of high-speed rail has taken away most of the air passengers. Since the first high-speed rail was opened in 2008, the passenger flow has increased rapidly, and it has exceeded the total civil aviation passenger volume in only 4 years.

[0004] For a long time, airports have lacked a perfect methodology to predict the loss of passengers in advance when new high-speed rails are opened. Since most airports lack in-depth exploration of passengers' travel needs and data support, when making flight adjustments and fare adjustment strategies, they mainly rely on experience and make further adjustments after the opening of high-speed rail. This decision-making behavior has a certain lag and lacks the grasp of future passengers' travel needs. After users form fixed travel habits, more users will be lost instead. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defect that there is no perfect methodology to predict the loss of passengers in advance when a new high-speed rail is opened in the prior art, and to provide a method, system, device and medium for predicting the diversion of flight passengers.

[0006] The present invention solves the above technical problem through the following technical solutions:

[0007] A method for predicting the diversion of flight passengers, the method for predicting the diversion of flight passengers includes the following steps:

[0008] Determine the flight to be analyzed corresponding to the high-speed rail line to be opened; wherein, the departure place and destination of the flight to be analyzed correspond to the departure place and destination of the high-speed rail line to be opened;

[0009] Determine the first historical benchmark flight and the corresponding first historical high - speed rail line according to the flight to be analyzed; wherein, the departure and destination of the first historical benchmark flight correspond to those of the corresponding first historical high - speed rail line; and the difference between the proportion of in - province passengers of the first historical benchmark flight before the opening of the first historical high - speed rail line and the proportion of in - province passengers of the flight to be analyzed is less than the first proportion threshold; the difference between the proportion of out - of - province passengers of the first historical benchmark flight before the opening of the first historical high - speed rail line and the proportion of out - of - province passengers of the flight to be analyzed is less than the second proportion threshold; the difference between the flight distance of the first historical benchmark flight and the flight distance of the flight to be analyzed is less than the flight distance threshold.

[0010] Predict the passenger loss information of the flight to be analyzed after the opening of the to - be - opened high - speed rail line according to the passenger loss information of the first historical benchmark flight after the opening of the first historical high - speed rail line.

[0011] Preferably, the passenger loss information includes: the direct - flight passenger loss ratio, the stop - over passenger loss ratio, and the transfer passenger loss ratio.

[0012] The step of predicting the passenger loss information of the flight to be analyzed after the opening of the to - be - opened high - speed rail line according to the passenger loss information of the first historical benchmark flight after the opening of the first historical high - speed rail line specifically includes:

[0013] Predict the first direct - flight passenger loss ratio, the first stop - over passenger loss ratio, and the first transfer passenger loss ratio of the flight to be analyzed after the opening of the to - be - opened high - speed rail line according to the direct - flight passenger loss ratio, the stop - over passenger loss ratio, and the transfer passenger loss ratio of the first historical benchmark flight after the opening of the first historical high - speed rail line.

[0014] Preferably, before the step of determining the flight to be analyzed corresponding to the to - be - opened high - speed rail line, there is also a step:

[0015] Determine the historically opened high - speed rail lines as candidate high - speed rail lines, the first historical high - speed rail line is one of the candidate high - speed rail lines, and determine the historical benchmark flights corresponding to the historical high - speed rail lines;

[0016] Obtain the flight distance of the historical benchmark flight;

[0017] Obtain the number of passengers of the historical benchmark flight corresponding to the candidate high - speed rail line, and the number of passenger losses of the historical benchmark flight after the opening of the candidate high - speed rail line

[0018] Preferably, the flight passenger diversion prediction method further includes a step:

[0019] Predict the first passenger loss ratio of the flight to be analyzed after the opening of the high-speed rail line to be opened based on the passenger loss information of the first historical benchmark flight after the opening of the first historical high-speed rail line;

[0020] Determine the second historical benchmark flight and the corresponding second historical high-speed rail line according to the flight to be analyzed;

[0021] Among them, the difference between the proportion of in-province passengers of the second historical benchmark flight before the opening of the second historical high-speed rail line and the proportion of in-province passengers of the flight to be analyzed is less than the first proportion threshold; the difference between the proportion of out-of-province passengers of the second historical benchmark flight before the opening of the second historical high-speed rail line and the proportion of out-of-province passengers of the flight to be analyzed is less than the second proportion threshold;

[0022] The difference between the flight distance of the second historical benchmark flight and the flight distance of the flight to be analyzed is greater than the flight distance threshold;

[0023] Predict the second passenger loss ratio of the flight to be analyzed after the opening of the high-speed rail line to be opened according to the passenger loss ratio of the second historical benchmark flight after the opening of the second historical high-speed rail line;

[0024] If the flight distance of the second historical benchmark flight is greater than that of the first historical benchmark flight, and the first passenger loss ratio is greater than the second passenger loss ratio, it is determined that the selection of the proportion threshold and / or the flight distance threshold is incorrect, and the first proportion threshold and / or the second proportion threshold and / or the flight distance threshold are adjusted accordingly;

[0025] Or, if the flight distance of the second historical benchmark flight is less than that of the first historical benchmark flight, and the first passenger loss ratio is less than the second passenger loss ratio, it is determined that the selection of the proportion threshold and / or the flight distance threshold is incorrect, and the first proportion threshold and / or the second proportion threshold and / or the flight distance threshold are adjusted accordingly.

[0026] Preferably, the flight passenger diversion prediction method further includes the steps of:

[0027] According to at least one of the changes in the passenger source volume, seat occupancy rate, transport capacity, number of flight schedules, and price difference between high-speed rail fares and air tickets of the first historical benchmark flight after the opening of the first historical high-speed rail line, correspondingly predict at least one of the trends of changes in the passenger source volume, seat occupancy rate, transport capacity, number of flight schedules, and price difference between high-speed rail fares and air tickets of the flight to be analyzed after the opening of the high-speed rail line to be opened.

[0028] Preferably, the flight passenger diversion prediction method further includes the steps of:

[0029] Provide corresponding flight change suggestions for the departure airport of the flight to be analyzed according to at least one of the changes in the number of passengers from different sources, the load factor, the transport capacity, the number of flight schedules, and the price difference trend between high-speed rail fares and air tickets, as well as the travel search popularity and flight search popularity of passengers.

[0030] Specifically, the flight passenger diversion prediction method may further include the following steps: obtaining the passenger data of the flight to be analyzed;

[0031] Among them, the passenger data includes the permanent addresses of each passenger;

[0032] If the permanent address of the passenger is not in the province where the departure place of the flight to be analyzed is located, then it is determined that the passenger is an out-of-province passenger;

[0033] If the permanent address of the passenger is in the province where the departure place of the flight to be analyzed is located, then it is determined that the passenger is an in-province passenger.

[0034] As a second aspect of the present invention, the present invention provides a flight passenger diversion prediction system, characterized in that the flight passenger diversion prediction system includes: a flight to be analyzed module, a passenger diversion basis module, and a passenger diversion prediction module;

[0035] The flight to be analyzed module is used to determine the flight to be analyzed corresponding to the high-speed rail line to be opened; wherein, the departure place and destination of the flight to be analyzed correspond to the departure place and destination of the high-speed rail line to be opened.

[0036] The passenger diversion basis module is used to determine the first historical benchmark flight and the corresponding first historical high-speed rail line according to the flight to be analyzed; wherein, the departure place and destination of the first historical benchmark flight correspond to the departure place and destination of the corresponding first historical high-speed rail line;

[0037] And the difference between the proportion of in-province passengers of the first historical benchmark flight before the opening of the first historical high-speed rail line and the proportion of in-province passengers of the flight to be analyzed is less than the first proportion threshold; the difference between the proportion of out-of-province passengers of the first historical benchmark flight before the opening of the first historical high-speed rail line and the proportion of out-of-province passengers of the flight to be analyzed is less than the second proportion threshold;

[0038] The difference between the flight distance of the first historical benchmark flight and the flight distance of the flight to be analyzed is less than the flight distance threshold;

[0039] The passenger diversion prediction module is used to predict the first passenger loss ratio of the flight to be analyzed after the opening of the high-speed rail line to be opened according to the passenger loss ratio of the first historical benchmark flight after the opening of the first historical high-speed rail line.

[0040] As a third aspect of the present invention, the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and the electronic device is used for the flight passenger diversion prediction method described above.

[0041] As a fourth aspect of the present invention, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned flight passenger diversion prediction method is realized.

[0042] The positive and progressive effects of the present invention are as follows: Based on the impact of the historically opened high-speed rail lines on the passenger loss of the historically comparable airports, the impact of the currently opened high-speed rail lines on the passenger loss of the currently comparable airport is deduced in advance, so that the airport has enough time for operation adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic flowchart of the flight passenger diversion method in Embodiment 1 of the present invention.

[0044] Figure 2 It is a schematic structural diagram of the flight passenger diversion system in Embodiment 2 of the present invention.

[0045] Figure 3 It is a schematic structural diagram of the electronic device that executes the flight passenger diversion method in Embodiment 1 in Embodiment 3 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] The present invention will be further described below by way of embodiments, but the present invention is not limited to the scope of the described embodiments.

[0047] Embodiment 1

[0048] Please refer to Figure 1 , this embodiment provides a flight passenger diversion prediction method, including the following steps:

[0049] S1. Determine the flight to be analyzed corresponding to the high-speed rail line to be opened;

[0050] Among them, the departure place and destination of the flight to be analyzed correspond to the departure place and destination of the high-speed rail line to be opened.

[0051] S2. Determine the first historical comparable flight and the corresponding first historical high-speed rail line according to the flight to be analyzed;

[0052] Among them, the departure and destination of the first historical benchmark flight correspond to those of the corresponding first historical high-speed rail line; and the difference between the proportion of in-province passengers of the first historical benchmark flight before the opening of the first historical high-speed rail line and that of the flight to be analyzed is less than the first proportion threshold; the difference between the proportion of out-of-province passengers of the first historical benchmark flight before the opening of the first historical high-speed rail line and that of the flight to be analyzed is less than the second proportion threshold; the difference between the flight distance of the first historical benchmark flight and that of the flight to be analyzed is less than the flight distance threshold;

[0053] S3. Predict the passenger loss information of the flight to be analyzed after the opening of the to-be-opened high-speed rail line based on the passenger loss information of the first historical benchmark flight after the opening of the first historical high-speed rail line.

[0054] In the flight distance in this embodiment, it refers to the flight distance of the flight.

[0055] In this embodiment, for example, the departure place of the to-be-opened high-speed rail is Shanghai and the destination is Zhengzhou; then the departure place of the flight to be analyzed is Shanghai and the destination is Zhengzhou. The flight distance, proportion of in-province passengers, and proportion of out-of-province passengers of the first historical benchmark flight all meet the requirements. (That is, the difference between the proportion of in-province passengers of the first historical benchmark flight before the opening of the first historical high-speed rail line and that of the flight to be analyzed is less than the first proportion threshold; the difference between the proportion of out-of-province passengers of the first historical benchmark flight before the opening of the first historical high-speed rail line and that of the flight to be analyzed is less than the second proportion threshold; the difference between the flight distance of the first historical benchmark flight and that of the flight to be analyzed is less than the flight distance threshold). At this time, the departure place of the first historical benchmark flight is Shanghai and the destination is Beijing; the departure place of the first historical high-speed rail line is Shanghai and the destination is Beijing. In this embodiment, if the number of passengers of the first historical benchmark flight is 1000 and the number of lost passengers is 200, then the passenger loss ratio of the first historical benchmark flight after the opening of the first historical high-speed rail line can be obtained as 20%. Then, through this ratio, it can be inferred that the passenger loss ratio of the flight to be analyzed after the opening of the corresponding high-speed rail line is 20%, and based on the original number of passengers of the flight to be analyzed, the number of lost passengers of the flight to be analyzed after the opening of the to-be-opened high-speed rail line can be inferred.

[0056] In this embodiment, the first proportion threshold, the second proportion threshold, and the flight distance threshold can be selected according to the actual situation. And, in this embodiment, the passenger loss ratio of the flight to be analyzed is not necessarily a numerical value, but can also be a preset interval.

[0057] Specifically, the passenger loss information may include: the direct flight passenger loss ratio, the stopover passenger loss ratio, and the transfer passenger loss ratio;

[0058] The step of predicting the passenger loss information of the flight to be analyzed after the opening of the to-be-opened high-speed rail line based on the passenger loss information of the first historical benchmark flight after the opening of the first historical high-speed rail line specifically includes:

[0059] Predict the first non-stop passenger loss ratio, the first stopover passenger loss ratio, and the first transfer passenger loss ratio of the flight to be analyzed after the opening of the to-be-opened high-speed rail line according to the non-stop passenger loss ratio, the stopover passenger loss ratio, and the transfer passenger loss ratio of the first historical benchmark flight after the opening of the first historical high-speed rail line.

[0060] In this embodiment, the passenger loss information is divided into non-stop passengers, stopover passengers, and transfer passengers. Since the impacts of high-speed rail on these three types of passengers are different, the loss ratio of each type of passenger of the first historical benchmark flight after the opening of the first historical high-speed rail line is used to infer the loss ratio of each type of passenger of the flight to be analyzed after the opening of the to-be-opened high-speed rail line.

[0061] By using this embodiment, by predicting the loss ratios of different passengers after the opening of the to-be-opened high-speed rail line, the passenger loss situation can be predicted more accurately, and more accurate data can be provided for subsequent flight adjustments at the airport.

[0062] Specifically, before the step of determining the flight to be analyzed corresponding to the to-be-opened high-speed rail line, the following steps may also be included:

[0063] Determine the historically opened high-speed rail lines as candidate high-speed rail lines, the first historical high-speed rail line is one of the candidate high-speed rail lines, and determine the historical benchmark flights corresponding to the historical high-speed rail lines;

[0064] Obtain the flight distance of the historical benchmark flight;

[0065] Obtain the number of passengers of the historical benchmark flight corresponding to the candidate high-speed rail line, and the number of passenger losses of the historical benchmark flight after the opening of the candidate high-speed rail line

[0066] In this embodiment, all historically opened high-speed rail lines are determined as candidate high-speed rail lines, and the historical benchmark flights corresponding to all candidate high-speed rail lines are determined;

[0067] Obtain the number of passengers of the historical benchmark flight corresponding to the candidate high-speed rail line, and the number of passenger losses of the historical benchmark flight after the opening of the historical high-speed rail line.

[0068] In this embodiment, it is possible to obtain candidate high-speed rail lines and their corresponding historical benchmark flights. When there is a to-be-opened high-speed rail line and a corresponding flight to be analyzed, the data of the flight to be analyzed can be directly used to search in the already obtained historical benchmark flights, rather than obtaining all the historical benchmark flights again, which reduces the computing time and improves the efficiency during the operation.

[0069] Specifically, the flight passenger diversion prediction method may further include the steps of:

[0070] Predicting the first passenger loss ratio of the flight to be analyzed after the opening of the to-be-opened high-speed rail line according to the passenger loss information of the first historical benchmark flight after the opening of the first historical high-speed rail line;

[0071] Determining a second historical benchmark flight and its corresponding second historical high-speed rail line according to the flight to be analyzed;

[0072] Wherein, the difference between the proportion of in-province passengers of the second historical benchmark flight before the opening of the second historical high-speed rail line and the proportion of in-province passengers of the flight to be analyzed is less than the first proportion threshold; the difference between the proportion of out-of-province passengers of the second historical benchmark flight before the opening of the second historical high-speed rail line and the proportion of out-of-province passengers of the flight to be analyzed is less than the second proportion threshold;

[0073] The difference between the flight distance of the second historical benchmark flight and the flight distance of the flight to be analyzed is greater than the flight distance threshold;

[0074] Predicting the second passenger loss ratio of the flight to be analyzed after the opening of the to-be-opened high-speed rail line according to the passenger loss ratio of the second historical benchmark flight after the opening of the second historical high-speed rail line;

[0075] If the flight distance of the second historical benchmark flight is greater than that of the first historical benchmark flight, and the first passenger loss ratio is greater than the second passenger loss ratio, it is determined that the first proportion threshold and / or the second proportion threshold and / or the flight distance threshold are selected incorrectly, and the first proportion threshold and / or the second proportion threshold and / or the flight distance threshold are adjusted accordingly;

[0076] Or, if the flight distance of the second historical benchmark flight is less than that of the first historical benchmark flight, and the first passenger loss ratio is less than the second passenger loss ratio, it is determined that the first proportion threshold and / or the second proportion threshold and / or the flight distance threshold are selected incorrectly, and the first proportion threshold and / or the second proportion threshold and / or the flight distance threshold are adjusted accordingly.

[0077] In this embodiment, generally speaking, the farther the distance between the departure place and the destination of the opened high-speed rail line is, the farther the flight distance of the corresponding flight is, and the smaller the passenger loss of the flight is. Flights with different flight distances are used to verify whether the first proportion threshold, the second proportion threshold, and the flight distance threshold are correct. By changing the first proportion threshold, the second proportion threshold, and the flight distance threshold, an accurate first historical benchmark flight can be found.

[0078] For example, in the foregoing embodiment, the departure location of the flight to be analyzed is Shanghai, and the destination is Zhengzhou. The departure location of the first historical benchmark flight is Shanghai, and the destination is Beijing. It is analyzed that the passenger loss ratio is 20%. However, a second historical benchmark flight route and its corresponding second historical high-speed rail are selected. The departure location of the second historical benchmark flight route is Shanghai, and the destination is Shenyang. At this time, the flight distance of the second historical benchmark flight is greater than that of the first historical benchmark flight. If the passenger loss ratio predicted by the first historical benchmark flight is 20%, and if the passenger loss ratio predicted by the second historical benchmark flight is greater than 20%, then it can be considered that there is a problem with the standard of the first historical benchmark flight. That is to say, at least one of the first ratio threshold, the second ratio threshold, and the flight distance threshold is problematic and needs to be adjusted.

[0079] Specifically, the flight passenger diversion prediction method can, according to at least one of the changes in the passenger source volume, seat occupancy rate, transport capacity, number of flight schedules, and price difference between high-speed rail fares and air tickets after the opening of the first historical high-speed rail line for the first historical benchmark flight, correspondingly predict at least one of the changes in the passenger source volume, seat occupancy rate, transport capacity, number of flight schedules, and price difference between high-speed rail fares and air tickets for the flight to be analyzed after the opening of the high-speed rail line to be opened.

[0080] In this embodiment, it is possible to predict the changes in the passenger source volume, seat occupancy rate, transport capacity, number of flight schedules, and price difference between high-speed rail fares and air tickets, and predict the changes in each parameter of the flight to be analyzed.

[0081] Specifically, the flight passenger diversion prediction method may further include the steps of:

[0082] According to at least one of the trends of changes in the passenger source volume, seat occupancy rate, transport capacity, number of flight schedules, and price difference between high-speed rail fares and air tickets, as well as the travel search popularity and flight search popularity of passengers, provide corresponding flight change suggestions for the departure airport of the flight to be analyzed.

[0083] In this embodiment, for example, when at least one of the passenger source volume of an airport decreases, the seat occupancy rate decreases, the number of flight schedules changes less, and the transport capacity becomes smaller, and it is found that the search popularity from the city where the airport is located to a certain region increases, then it is possible to recommend to the airport to change the original flight to a flight to that region to make up for the vacant transport capacity.

[0084] If the trend of the price difference between high-speed rail fares and air tickets becomes larger, then it can be considered that more and more customers are attracted by high-speed rail. At this time, it can be recommended that the airport reduce the number of flight schedules of this flight. And on this basis, obtain the travel search popularity and flight search popularity, and recommend that the airport change the original flight to a flight to the region where the search popularity increases.

[0085] Specifically, the flight passenger diversion prediction method may further include the following steps: obtaining passenger data of the flight to be analyzed;

[0086] Among them, the passenger data includes the permanent residence addresses of each passenger;

[0087] If the permanent residence address of a passenger is not the province where the departure place of the flight to be analyzed is located, it is determined that the passenger is an out-of-province passenger;

[0088] If the permanent residence address of a passenger is the province where the departure place of the flight to be analyzed is located, it is determined that the passenger is an in-province passenger.

[0089] In this embodiment, it is possible to determine whether a passenger is an out-of-province passenger or an in-province passenger of the departure place of the flight through the permanent residence address of the passenger. The permanent residence address can be judged through the IP address when the passenger places an order and the common travel address. Through this embodiment, it is possible to accurately determine the proportion of out-of-province passengers and in-province passengers of the flight.

[0090] Embodiment 2

[0091] Please refer to Figure 2 , this embodiment provides a flight passenger diversion prediction system, and the flight passenger diversion prediction system includes: a flight to be analyzed module 201, a passenger diversion basis module 202, and a passenger diversion prediction module 203;

[0092] The flight to be analyzed module 201 is used to determine the flight to be analyzed corresponding to the high-speed rail line to be opened; among them, the departure place and destination of the flight to be analyzed correspond to the departure place and destination of the high-speed rail line to be opened

[0093] The passenger diversion basis module 202 is used to determine the first historical benchmark flight and the corresponding first historical high-speed rail line according to the flight to be analyzed; among them, the departure place and destination of the first historical benchmark flight correspond to the departure place and destination of the corresponding first historical high-speed rail line;

[0094] And the difference between the proportion of in-province passengers of the first historical benchmark flight before the opening of the first historical high-speed rail line and the proportion of in-province passengers of the flight to be analyzed is less than the first proportion threshold; the difference between the proportion of out-of-province passengers of the first historical benchmark flight before the opening of the first historical high-speed rail line and the proportion of out-of-province passengers of the flight to be analyzed is less than the second proportion threshold;

[0095] The difference between the flight distance of the first historical benchmark flight and the flight distance of the flight to be analyzed is less than the flight distance threshold;

[0096] The passenger diversion prediction module 203 is used to predict the first passenger loss ratio of the flight to be analyzed after the opening of the high-speed rail line to be opened according to the passenger loss ratio of the first historical benchmark flight after the opening of the first historical high-speed rail line.

[0097] Specifically, the passenger loss information includes: the direct flight passenger loss ratio, the stopover passenger loss ratio, and the transfer passenger loss ratio;

[0098] The passenger flow diversion prediction module 203 can also be used to predict the first direct flight passenger loss ratio, the first stopover passenger loss ratio, and the first transfer passenger loss ratio of the flight to be analyzed after the opening of the high-speed rail line to be opened, based on the direct flight passenger loss ratio, the stopover passenger loss ratio, and the transfer passenger loss ratio of the first historical benchmark flight after the opening of the first historical high-speed rail line.

[0099] Specifically, the flight passenger flow diversion prediction system can also include a historical benchmark flight module. The historical benchmark flight module is used to determine the historically opened high-speed rail lines as candidate high-speed rail lines, where the first historical high-speed rail line is one of the candidate high-speed rail lines, and to determine the historical benchmark flights corresponding to the historical high-speed rail lines;

[0100] Obtain the flight distance of the historical benchmark flight;

[0101] Obtain the number of passengers of the historical benchmark flight corresponding to the candidate high-speed rail line, and the number of passenger losses of the historical benchmark flight after the opening of the candidate high-speed rail line.

[0102] Specifically, the passenger flow diversion basis module 202 can also be used to predict the first passenger loss ratio of the flight to be analyzed after the opening of the high-speed rail line to be opened, based on the passenger loss information of the first historical benchmark flight after the opening of the first historical high-speed rail line;

[0103] Determine the second historical benchmark flight and the corresponding second historical high-speed rail line according to the flight to be analyzed;

[0104] Wherein, the difference between the proportion of in-province passengers of the second historical benchmark flight before the opening of the second historical high-speed rail line and the proportion of in-province passengers of the flight to be analyzed is less than the first proportion threshold; the difference between the proportion of out-of-province passengers of the second historical benchmark flight before the opening of the second historical high-speed rail line and the proportion of out-of-province passengers of the flight to be analyzed is less than the second proportion threshold;

[0105] The difference between the flight distance of the second historical benchmark flight and the flight distance of the flight to be analyzed is greater than the flight distance threshold;

[0106] The passenger flow diversion prediction module 203 can also be used to predict the second passenger loss ratio of the flight to be analyzed after the opening of the high-speed rail line to be opened, based on the passenger loss ratio of the second historical benchmark flight after the opening of the second historical high-speed rail line;

[0107] If the flight distance of the second historical benchmark flight is greater than that of the first historical benchmark flight, and the first passenger loss ratio is greater than the second passenger loss ratio, it is determined that the selection of the proportion threshold and / or the flight distance threshold is incorrect, and the first proportion threshold and / or the second proportion threshold and / or the flight distance threshold are adjusted accordingly;

[0108] Or, if the flight distance of the second historical benchmark flight is less than that of the first historical benchmark flight, and the first passenger loss ratio is less than the second passenger loss ratio, it is determined that the selection of the proportion threshold and / or the flight distance threshold is incorrect, and the first proportion threshold and / or the second proportion threshold and / or the flight distance threshold are adjusted accordingly.

[0109] Specifically, the passenger diversion prediction module 203 can also be used to predict at least one of the changes in the passenger volume, seat occupancy rate, transport capacity, number of flight schedules, and price difference between high-speed rail fares and air tickets of the flight to be analyzed after the opening of the high-speed rail line according to at least one of the changes in the passenger volume, seat occupancy rate, transport capacity, number of flight schedules, and price difference between high-speed rail fares and air tickets of the first historical benchmark flight after the opening of the first historical high-speed rail line.

[0110] Specifically, the passenger diversion prediction module 203 can also provide corresponding flight change suggestions for the departure airport of the flight to be analyzed according to at least one of the trends of changes in the passenger volume, seat occupancy rate, transport capacity, number of flight schedules, and price difference between high-speed rail fares and air tickets, as well as the travel search popularity and flight search popularity of passengers.

[0111] Specifically, the flight passenger diversion prediction system further includes a permanent address judgment module, and the permanent address judgment module is used to obtain the passenger data of the flight to be analyzed;

[0112] Wherein, the passenger data includes the permanent addresses of each passenger;

[0113] If the permanent address of the passenger is not the province where the departure place of the flight to be analyzed is located, it is determined that the passenger is an out-of-province passenger;

[0114] If the permanent address of the passenger is the province where the departure place of the flight to be analyzed is located, it is determined that the passenger is an in-province passenger.

[0115] The operating principle of this system is the same as that of the flight passenger diversion prediction method in Embodiment 1, and will not be elaborated here again.

[0116] Embodiment 3

[0117] Figure 3A schematic structural diagram of an electronic device provided in this embodiment. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the flight passenger diversion prediction method in Embodiment 1 are implemented. Figure 3 The electronic device 30 shown is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0118] As Figure 3 shown, the electronic device 30 may be presented in the form of a general computing device, for example, it may be a server device. The components of the electronic device 30 may include, but are not limited to: the at least one processor 31 described above, the at least one memory 32 described above, and a bus 33 connecting different system components (including the memory 32 and the processor 31).

[0119] The bus 33 includes a data bus, an address bus, and a control bus.

[0120] The memory 32 may include volatile memory, such as a random access memory (RAM) 321 and / or a cache memory 322, and may further include a read-only memory (ROM) 323.

[0121] The memory 32 may further include a program / utilities 325 having a set (at least one) of program modules 324. Such program modules 324 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0122] The processor 31 executes various functional applications and data processing by running the computer program stored in the memory 32, such as the steps of the flight passenger diversion prediction method in Embodiment 1 of the present invention.

[0123] The electronic device 30 may also communicate with one or more external devices 34 (such as a keyboard, a pointing device, etc.). Such communication may be carried out through an input / output (I / O) interface 35. And, the device 30 for model generation may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 36. As shown in the figure, the network adapter 36 communicates with other modules of the device 30 for model generation through the bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in combination with the device 30 for model generation, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.

[0124] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / modules. Conversely, the features and functions of one unit / modules described above can be further divided and embodied by multiple units / modules.

[0125] Embodiment 4

[0126] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the flight passenger diversion prediction method in Embodiment 1 are implemented.

[0127] Among them, the more specific forms that the readable storage medium can adopt may include but are not limited to: portable disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0128] In a possible implementation manner, the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps of the flight passenger diversion prediction method in Embodiment 1.

[0129] Among them, the program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be completely executed on the user device, partially executed on the user device, executed as an independent software package, partially executed on the user device and partially executed on a remote device, or completely executed on a remote device.

[0130] Although the specific implementation manners of the present invention are described above, those skilled in the art should understand that this is only an example. The protection scope of the present invention is defined by the appended claims. Without departing from the principles and essence of the present invention, those skilled in the art can make various changes or modifications to these implementation manners, but these changes and modifications all fall within the protection scope of the present invention.

Claims

1. A method for predicting the diversion of flight passengers, characterized in that, The flight passenger diversion prediction method includes the following steps: Determine the flight to be analyzed corresponding to the high-speed rail line to be opened; wherein, the departure and destination of the flight to be analyzed are the same as those of the high-speed rail line to be opened. Determine the first historical benchmark flight and the corresponding first historical high-speed rail line according to the flight to be analyzed; wherein, the departure and destination of the first historical benchmark flight are the same as those of the corresponding first historical high-speed rail line; and the difference between the proportion of in-province passengers of the first historical benchmark flight before the opening of the first historical high-speed rail line and the proportion of in-province passengers of the flight to be analyzed is less than the first proportion threshold; the difference between the proportion of out-of-province passengers of the first historical benchmark flight before the opening of the first historical high-speed rail line and the proportion of out-of-province passengers of the flight to be analyzed is less than the second proportion threshold; the difference between the flight distance of the first historical benchmark flight and the flight distance of the flight to be analyzed is less than the flight distance threshold. Predict the passenger loss information of the flight to be analyzed after the opening of the high-speed rail line to be opened according to the passenger loss information of the first historical benchmark flight after the opening of the first historical high-speed rail line; specifically including: Obtain the number of passengers of the first historical benchmark flight before the opening of the first historical high-speed rail line and the number of lost passengers of the first historical benchmark flight after the opening of the first historical high-speed rail line. Calculate the passenger loss ratio of the first historical benchmark flight after the opening of the first historical high-speed rail line according to the number of passengers and the number of lost passengers. Set the passenger loss ratio as the passenger loss ratio of the flight to be analyzed after the opening of the high-speed rail line to be opened. Calculate the number of lost passengers of the flight to be analyzed after the opening of the high-speed rail line to be opened according to the passenger loss ratio.

2. The flight passenger diversion prediction method according to claim 1, characterized in that The passenger loss information includes: direct flight passenger loss ratio, stopover passenger loss ratio, and transfer passenger loss ratio. The step of predicting the passenger loss information of the flight to be analyzed after the opening of the high-speed rail line to be opened according to the passenger loss information of the first historical benchmark flight after the opening of the first historical high-speed rail line specifically includes: Predict the first direct flight passenger loss ratio, the first stopover passenger loss ratio, and the first transfer passenger loss ratio of the flight to be analyzed after the opening of the high-speed rail line to be opened according to the direct flight passenger loss ratio, the stopover passenger loss ratio, and the transfer passenger loss ratio of the first historical benchmark flight after the opening of the first historical high-speed rail line.

3. The flight passenger diversion prediction method according to claim 1, characterized in that Before the step of determining the flight to be analyzed corresponding to the high-speed rail line to be opened, there is also a step: Determine the historically opened high-speed rail lines as candidate high-speed rail lines, and determine the historical benchmark flights corresponding to the historically opened high-speed rail lines; wherein, the first historical high-speed rail line is one of the candidate high-speed rail lines, and the first historical benchmark flight is one of the historical benchmark flights. Obtain the flight distance of the historical benchmark flight. Obtain the number of passengers of the historical benchmark flight corresponding to the candidate high-speed rail line, and the number of lost passengers of the historical benchmark flight after the opening of the candidate high-speed rail line.

4. The flight passenger diversion prediction method according to claim 1, characterized in that The flight passenger diversion prediction method further includes the steps of: Predicting the first passenger loss ratio of the flight to be analyzed after the opening of the high-speed rail line to be opened according to the passenger loss information of the first historical benchmark flight after the opening of the first historical high-speed rail line; Determining a second historical benchmark flight and a corresponding second historical high-speed rail line according to the flight to be analyzed; Wherein, the difference between the proportion of in-province passengers of the second historical benchmark flight before the opening of the second historical high-speed rail line and the proportion of in-province passengers of the flight to be analyzed is less than the first proportion threshold; the difference between the proportion of out-of-province passengers of the second historical benchmark flight before the opening of the second historical high-speed rail line and the proportion of out-of-province passengers of the flight to be analyzed is less than the second proportion threshold; The difference between the flight distance of the second historical benchmark flight and the flight distance of the flight to be analyzed is greater than the flight distance threshold; Predicting the second passenger loss ratio of the flight to be analyzed after the opening of the high-speed rail line to be opened according to the passenger loss ratio of the second historical benchmark flight after the opening of the second historical high-speed rail line; If the flight distance of the second historical benchmark flight is greater than that of the first historical benchmark flight, and the first passenger loss ratio is greater than the second passenger loss ratio, it is determined that the selection of the proportion threshold and / or the flight distance threshold is incorrect, and the first proportion threshold and / or the second proportion threshold and / or the flight distance threshold are adjusted accordingly; Or, if the flight distance of the second historical benchmark flight is less than that of the first historical benchmark flight, and the first passenger loss ratio is less than the second passenger loss ratio, it is determined that the selection of the proportion threshold and / or the flight distance threshold is incorrect, and the first proportion threshold and / or the second proportion threshold and / or the flight distance threshold are adjusted accordingly.

5. The flight passenger diversion prediction method according to claim 2, characterized in that The flight passenger diversion prediction method further includes the steps of: Correspondingly predicting at least one of the changes in passenger volume, seat occupancy rate, transport capacity, number of flight schedules, and price difference between high-speed rail fares and air tickets of the flight to be analyzed after the opening of the high-speed rail line to be opened according to at least one of the changes in passenger volume, seat occupancy rate, transport capacity, number of flight schedules, and price difference between high-speed rail fares and air tickets of the first historical benchmark flight after the opening of the first historical high-speed rail line.

6. The flight passenger diversion prediction method according to claim 5, wherein, The flight passenger diversion prediction method further includes the steps of: Providing corresponding flight change suggestions for the departure airport of the flight to be analyzed according to at least one of the trends of changes in passenger volume, seat occupancy rate, transport capacity, number of flight schedules, and price difference between high-speed rail fares and air tickets, as well as the travel search heat and flight search heat of passengers.

7. The flight passenger diversion prediction method according to claim 1, wherein The flight passenger diversion prediction method further includes the following steps: obtaining the passenger data of the flight to be analyzed; Wherein, the passenger data includes the permanent addresses of each passenger; If the permanent address of the passenger is not the province where the departure place of the flight to be analyzed is located, it is determined that the passenger is an out-of-province passenger; If the permanent address of the passenger is the province where the departure place of the flight to be analyzed is located, it is determined that the passenger is an in-province passenger.

8. A flight passenger diversion prediction system, characterized in that, The flight passenger diversion prediction system includes: a flight to be analyzed module, a passenger diversion basis module, and a passenger diversion prediction module; The flight module to be analyzed is used to determine the flight to be analyzed corresponding to the high-speed rail line to be opened; wherein, the departure and destination of the flight to be analyzed are the same as those of the high-speed rail line to be opened. The passenger flow diversion basis module is used to determine the first historical benchmark flight and the corresponding first historical high-speed rail line according to the flight to be analyzed; wherein, the departure and destination of the first historical benchmark flight are the same as those of the corresponding first historical high-speed rail line. And the difference between the proportion of in-province passengers of the first historical benchmark flight before the opening of the first historical high-speed rail line and the proportion of in-province passengers of the flight to be analyzed is less than the first proportion threshold; the difference between the proportion of out-of-province passengers of the first historical benchmark flight before the opening of the first historical high-speed rail line and the proportion of out-of-province passengers of the flight to be analyzed is less than the second proportion threshold. The difference between the flight distance of the first historical benchmark flight and the flight distance of the flight to be analyzed is less than the flight distance threshold. The passenger flow diversion prediction module is used to predict the first passenger loss ratio of the flight to be analyzed after the opening of the high-speed rail line to be opened according to the passenger loss ratio of the first historical benchmark flight after the opening of the first historical high-speed rail line. The passenger flow diversion prediction module is specifically used to obtain the number of passengers of the first historical benchmark flight when the first historical high-speed rail line is not opened and the number of lost passengers of the first historical benchmark flight after the opening of the first historical high-speed rail line; calculate the passenger loss ratio of the first historical benchmark flight after the opening of the first historical high-speed rail line according to the number of passengers and the number of lost passengers; set the passenger loss ratio as the passenger loss ratio of the flight to be analyzed after the opening of the high-speed rail line to be opened; calculate the number of lost passengers of the flight to be analyzed after the opening of the high-speed rail line to be opened according to the passenger loss ratio.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the flight passenger flow diversion prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the flight passenger flow diversion prediction method according to any one of claims 1 to 7.

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