CN114154393B - Target airport group passenger throughput prediction method based on abdomen ground theory - Google Patents
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Abstract
The invention discloses a target airport group passenger throughput prediction method based on the abdomen ground theory, which comprises the following steps: 1) Firstly dividing a target area into a plurality of areas, and utilizing the current moving signaling data of the target area to mine so as to obtain the number of times of people's average riding in each area; 2) Reasonably predicting the number of times of people taking in each area in the future; 3) Predicting the aviation throughput scale of each airport in the target airport group: dividing the abdominal land range of the airport by utilizing the abdominal geographic theory according to the travel accessibility (travel time consumption) of airport gathering and distributing, and sharing the regional riding scale to each airport; finally, combining specific functions of each airport to perform factor adjustment to obtain the throughput scale of passengers in each airport; the prediction method has feasibility and solves the problem that the conventional time sequence and regression analysis model cannot fully consider the competition relationship among airport groups and airport functions.
Description
Technical Field
The invention belongs to the technical field of airport group passenger throughput prediction, and particularly relates to a target airport group passenger throughput prediction method based on an abdomen ground theory.
Background
Passenger throughput is an important basis for determining functions and scales of each airport of airport groups. At present, a plurality of methods for predicting airport passenger throughput are available, qualitative prediction methods comprise investigation prediction methods, analogy methods, collection opinion methods, delphi methods and the like, quantitative prediction methods comprise time sequence methods, trend extrapolation methods, regression analysis methods and the like [ Huishan forest. Civil airport passenger throughput prediction methods are discussed in [ J ]. Scientific forum, 2010 (1): 142-143 ]. Chen Yubao it was just to overcome the defect of single quantitative prediction method, based on multiple linear regression model and time series trend extrapolation model, adopting combined weighting method to make combined prediction of prediction result to raise prediction accuracy and reduce prediction error [ Chen Yubao, and just based on combined prediction method, civil aviation passenger throughput prediction research-take capital airport as example [ J ]. University of civil aviation, university of China, 2014,32 (2): 59-64 ]. Li Ming an improved gray model with residual correction of the gray model by an artificial neural network is used in the prediction of airport passenger throughput based on airport passenger throughput variation characteristics [ Li Ming. Airport passenger throughput prediction based on the improved gray model [ D ]. Kunming, university of Yunnan, 2018 ]. Li Dongmei, li Wenquan, fan Dongtao and the like are based on a four-stage method and combined with airport collecting and transporting characteristic optimization direction distribution models [ Li Dongmei, li Wenquan, fan Dongtao ] a large airport ground collecting and transporting demand prediction method is researched [ J ]. Traffic engineering and information report, 2013 (4): 87-93 ]; lu Yao an airport passenger space distribution prediction optimization model is provided by using a generalized regression neural network and a genetic BP neural network [ Lu Yao ] a large airport passenger city space distribution prediction and traffic mode selection behavior research [ D ] Beijing: beijing university of industry, 2019 ].
The existing airport group passenger throughput prediction method has the following defects:
the existing passenger throughput prediction model takes GDP and historical passenger flow data as regression analysis, and ignores the influence of airport group competition factors and airport radiation rings and the influence of travelers on flight selection.
Disclosure of Invention
In view of the above, in order to solve the defects of the traditional airport group passenger throughput prediction method, such as neglecting the competition factors of the airport group, the influence of airport radiation rings, the influence of travelers on the selection of flights, and the like, the invention takes big data as support, considers the competition relationship of each airport of the airport group, and provides an airport passenger throughput prediction model based on the geographic theory of abdomen, which reasonably predicts the airport passenger throughput from the view angle of regional airport competition.
The technical scheme of the invention is as follows:
a target airport group passenger throughput prediction method based on the abdomen ground theory mainly comprises the following steps:
1) Firstly dividing a target area into a plurality of areas, and utilizing the current year to carry out moving signaling data mining on the target area to obtain the number of times of people and average passengers in each area:
identifying population of a target area and number of passengers of the target area by using mobile phone signaling data, and obtaining current situation average passenger number index of each area by using a formula (1);
wherein, gamma' i Number of times of people take advantage of each machine (times/people/year) for current situation of area i, y it For the aviation passenger flow scale (number of persons) from the region i to the airport t identified by using mobile phone signaling, p i Population (ten thousands) of people in region i, p i Identifying by using mobile phone signaling data;
2) According to the social economic development targets of each region, calculating and predicting the number of times of people taking in each region in the year;
based on the relationship that the increase rate of the number of times of the people and average aircrafts is approximately equal to the increase rate of the total production value in the people and average region, the current situation of the area i calculated in the step 1) is utilized to calculate the number of times gamma 'of the people and average aircrafts' i And calculating the number of times gamma of people's average passenger in each area i of the predicted year by using the formula (2) i ；
In the formula, GDP' is GDP (ten thousand yuan per person) for current status of city s for region i, GDP is Predicting annual average person GDP (ten thousand yuan/person) for the city s corresponding to the region i;
3) Predicting the aviation throughput scale of each airport in the target airport group;
the target airport group comprises m airports, and the aviation throughput scale of one airport t in the target airport group is calculated by adopting the following formula (3):
wherein f t For airport t aviation throughput (ten thousand people per year), p i Population (ten thousands) of people in region i, γ i Number of people per year (times/people/year) for prediction of region i, n is the number of regions in the t-web region of airport, sigma t As a functional factor of airport t, the sigma t Is an airport flight distribution and density influencing factor.
Preferably, in the step 3),
the number n of the airport t-abdomen area is determined according to the following method:
3.1 Calculating the field strength of the airport t of the area i according to the following formula (4); characterization of the comprehensive scale Z of an airport by means of the taking-off and landing capabilities of the airport flight t Characterizing distance by travel time consumption of region i and airport tThe time consumption of the outgoing line is acquired based on a time impedance matrix of the aggregation and dispersion network;
wherein F is ti Is an airportt field strength in region i, Z t For the integrated scale of the airport t,for the distance from region i to airport t, α is the coefficient of friction;
wherein the distance of the region i to the airport tThe method is characterized by comprising the following steps:
establishing a full-province traffic network model according to province track and expressway planning conditions, connecting the hubs to the network model according to future planning and distributing facility conditions of each hub, and calculating time consumption matrixes of each area and each airport based on the model to obtain the time distance from the area i to the airport tWherein the provincial track comprises Gao Putie, inter-city and subway;
3.2 Determining airport t-land range area number n: comparing the field intensity values of m airports for the region i, if the maximum field intensity is airport j, circularly calculating the region i into the abdomen of airport j to all the regions, and dividing the region number n of the abdomen of each airport of the airport group.
Preferably, in the step 3),
function factor sigma of airport t t The determination steps of (a) are as follows:
calibrating the current number of times of passenger per unit passenger in each area and the abdominal region population calculation data according to the passenger throughput statistical data of each airport, and performing a function factor sigma t 0.82-1.14.
Compared with the prior art, the technical scheme has the following beneficial effects:
(1) The invention calibrates the current number of times of the people's average aircrafts by means of the latest mobile phone signaling big data technical means, establishes a relation model of the number of times of the people's average aircrafts and the people's average GDP, predicts the total amount of aircrafts in the future year region, and improves the prediction accuracy.
(2) Based on the theory of the land, the method utilizes the field intensity of the outgoing time consumption computer airport to each region to determine the land range of each airport, further provides an airport passenger throughput prediction model, creatively applies the geographic theory of the land to the airport group passenger throughput prediction model, and takes account of the influence of airport group competition factors, airport radiation rings and the influence of travelers on the flight selection on the throughput prediction, so that the prediction is more accurate.
(3) The invention also takes the Buddha new airport in the Guangdong province airport group as an example for application research, and the result shows that the airport passenger throughput prediction method based on the abdomen ground theory has practicability, and solves the problems that the traditional time sequence and regression analysis model has incomplete consideration of the competition relationship and airport functions among the airport groups.
Drawings
Fig. 1 is a flow chart of airport group passenger throughput prediction.
Fig. 2 is a flow chart of a hub passenger flow identification algorithm based on mobile phone signaling.
Detailed Description
The invention will be described in further detail with reference to the drawings and examples.
Brief description of the geography of the abdomen 1
1.1 abdomen geography concept
The land (Hinterland) is originally meant to be the subordinate area of ports through which ports accumulate export material and distribute import material. Now more abdominal refers to the space area of influence in any colony (or commercial establishment in a colony) that is the area of the commercial exchange hub with the colony [ R.J.Johnston, etc. the Dictionary of Huamn Geography [ M ]. USA: cambridge, massachusetts,1996 ]. Christaller establishes a central ground theory in the central ground of south Germany, and proposes a basic concept of the central ground and a commodity service range thereof [ ray.M. Northan. Urban geograph [ M ]. USA: new York,1975 ]. Thereafter, the concept of urban land and the like is gradually proposed, expanded and evolved into various theories, but the land is an influence and radiation area of the central land, and the central land and the land are closely related through traffic, information and the like. Through decades of development, the web geography is gradually applied to the aspects of economic division and area planning, city planning, port planning, service facility layout planning, corporate headquarters site selection and the like.
1.2 abdominal region determination
The central city is used as the core of a certain area, the influence is called as field intensity, and therefore, a field intensity model is one of main methods for determining the abdominal region range, and a calculation formula is shown as (1):
wherein F is ik For i city field strength at k point, Z i For the integrated scale of the city i,for the distance from i city to k point, α is the coefficient of friction. After the field intensity of each point is determined, the abdomen-ground range of the city is determined according to the field intensity.
2 airport passenger throughput prediction process
Referring to fig. 1, the flow of prediction of airport passenger throughput in the destination airport group is as follows:
1) Firstly dividing a target area into a plurality of areas, and utilizing the current moving signaling data of the target area to mine so as to obtain the number of times of people's average riding in each area;
2) Reasonably predicting the number of times of people taking in each area in the future: according to the social economic development targets of each region, calculating and predicting the number of times of people taking in each region in the year;
3) Predicting the aviation throughput scale of each airport in the target airport group: dividing the abdominal land range of the airport by utilizing the abdominal geographic theory according to the travel accessibility (travel time consumption) of airport gathering and distributing, and sharing the regional riding scale to each airport; and finally, carrying out factor adjustment by combining specific functions of each airport to obtain the throughput scale of passengers in each airport.
3 construction of passenger throughput prediction model
3.1 throughput prediction model
The research of domestic and foreign scholars shows that a certain relationship exists between the population of the region and the throughput of airport passengers, and the population is an important factor for measuring the aviation market; the number of times of passenger per unit is the expression of local population passenger willingness, and is the representation of aviation market sharing after the comprehensive transportation network fully competes with the local economic development level; airport passenger throughput also depends on the competitiveness of the airport to a certain extent, and specifically comprises factors such as passenger flow land, airport self functions (line density and distribution), and the like, so that a prediction model of the airport passenger throughput and land range, population scale, number of people and passengers and airport functional factors shown in the following formula (2) is established.
Wherein f t For airport t aviation throughput (ten thousand people per year), p i Population (ten thousands) of people in region i, γ i Number of people per year (times/people/year) for prediction of region i, n is the number of regions in the t-web region of airport, sigma t Is a functional factor of airport t (mainly an airport flight distribution and density influence factor).
3.2 people-average taking times prediction based on mobile phone signaling data
The number of times of riding per person can be obtained by questionnaire, but for airport groups, the questionnaire is difficult to obtain satisfactory results due to administrative restrictions and sample restrictions. As the mobile phone signaling data is popular and the technology is mature, the population of the area and the number of the regional passengers are identified by using the mobile phone signaling data (a specific algorithm is shown in fig. 2), and the current number of times of passenger passengers is obtained.
Wherein, gamma' i Number of times of people take advantage of each machine (times/people/year) for current situation of area i, y it For the aviation passenger flow scale (number of persons) from the region i to the airport t identified by using mobile phone signaling, p i Population for region i(Ten thousand people, identified with the handset signaling data).
Because the historical mobile phone signaling data are difficult to acquire, the actual passenger throughput of the airport is adopted to calculate the times of urban average passenger in each year, and the times of average passenger are related to the total production value (according to urban statistics) in the average region, and the result shows that the increase rate of the times of average passenger is similar to the increase rate of the total production value in the average region (see table 1). Based on the relationship that the increase rate of the number of times of the people and the average of the machines is approximately equal to the increase rate of the total production value in the people and average of the machines in each area i in 2019 is calculated by using the mobile phone signaling data, and a number of times model of the people and the average of the machines in each area i in the coming year is predicted by using a formula (4).
In the formula, GDP' is GDP (ten thousand yuan per person) for current status of city s for region i, GDP is Annual average GDP (ten thousand yuan/man) is predicted for region i corresponding to city s.
Table 1 present summary of the relationship between the number of times of people and people's average GDP
Note that:
3.3 airport passenger flow abdominal analysis based on abdominal theory
Similar to the field strength model, each airport amplitude in an airport group is related to its concentration and distribution network and airport scale, so the abdominal region of airport t is determined as follows:
STEP1 the field strength F of the airport t of the region i is calculated according to formula (1) ti . Characterization of airport Scale Z Using airport flight landing Capacity t Characterizing distance by travel time consumption of region i and airport tThe outgoing time consumption is obtained based on a time impedance matrix of the aggregation and dispersion network.
STEP2, determining the number of the airport t-land range areas n: comparing the field intensity values of m airports for the region i, if the maximum field intensity is airport j, circularly calculating the region i into the abdomen of airport j to all the regions, and dividing the region number n of the abdomen of each airport of the airport group.
3.4 determination of airport Functions
The airport passenger throughput includes transit passenger flow in addition to passenger flow in the abdomen area, and meanwhile, the passenger flow in the abdomen area can also consider the influences of flight classification, shift and the like in airport selection, namely the passenger throughput is also influenced by airport functions, so that airport function factors are needed to correct the calculated passenger flow in the abdomen area. According to passenger throughput statistical data of Guangzhou white cloud airport, shenzhen Baoan airport, huizhou airport, zhanhai airport, zhanjiang airport, ganshan airport, meizhou airport, current number of times of people's average take advantage of and abdominal region population calculation data, calibrating, and function factor sigma t 0.82-1.14.
Table 2 airport functional factor calibration results list
| Airport name | Abdominal earth passenger flow (thousands people) | Actual throughput (thousands of people) | Function factor sigma t |
| Guangzhou white cloud airport | 6418.9 | 7337.8 | 1.14 |
| Baoan+Huizhou airport | 5234.2 | 5548.5 | 1.06 |
| Pearl sea airport | 1370.3 | 1228.2 | 0.90 |
| Zhanjiang airport | 365.9 | 298.4 | 0.82 |
| Chaoshan and Meizhou airport | 924.9 | 802.6 | 0.87 |
4 practical application-prediction of passenger flow at each airport of Guangdong province airport group
4.1 times of people-average riding in Guangdong province prediction
Dividing the Guangdong province area into 1678 traffic subareas, utilizing 2019 Guangdong province mobile signaling data to mine, obtaining current situation number of people taking advantage of each subarea, wherein the current situation average number of people taking advantage of the whole province is about 1.24 times per person/year, wherein Guangzhou, shenzhen reach about 2.9 times per person/year, 2.64 times per person/year, buddha mountain reach about 1.53 times per person/year, and the number of people taking advantage of areas such as the name of the Max, the cause of the death, the river source, the uncovering of the sun and the like is still lower.
According to the formula (4), according to social and economic development targets of each market, the number of times of people taking the aircraft in each area in the prediction year is calculated. The calculation result shows that the number of times of people's average riding in 2035 years of Guangdong is 2.53 times per person/year, the number of times of people's average riding in 2050 year is 3.1 times per person/year, and the number of times of people's average riding in the United states is equal to half (6.2 times per person/year) of Singapore.
4.2 division of the region of the passenger flow abdomen in the airport group in Guangdong province
According to the full-province track (comprising Gao Putie, inter-city and subway) and expressway planning conditions, a full-province traffic network model is established, the hub is connected to the network model according to future planning and distributing facility conditions of each hub, the time consumption matrix of each area and each airport is calculated based on the model, and the abdomen area range of each airport is further divided.
4.3 prediction of the Scale of passenger flow at each airport of the Guangdong province airport group
The abdominal passenger flow scale of each airport is calculated according to the planned population of each area and the number of times of people and average passenger machines and the abdominal region range division of the airport, and is shown in table 3. The new airport of the Buddha is regional hub airport, combine its present situation of planning in the collection and distribution system can judge the new airport of the Buddha is in the most unfavorable condition in the area and will not be lower than the present pearl sea airport in its peripheral area too, in addition, because the new airport of the Buddha assists the white cloud airport to become the important composition of the international aviation hub of Guangzhou together in the future, can judge its functional position will not exceed the functional position in the area of the present state of white cloud airport under the most favorable development condition too, so its functional factor is about 0.9-1.14 (the functional factor of pearl sea airport and white cloud international airport separately), can predict the actual throughput level 2035 years of new airport of the Buddha fluctuates between about 2900-3700 ten thousand people/year; 2050 fluctuates between about 5300-6700 people/year.
TABLE 3 passenger flow Scale for abdominal region of airports
Compared with the prior art, the technical scheme has the following beneficial effects:
(1) The invention calibrates the current number of times of the people's average aircrafts by means of the latest mobile phone signaling big data technical means, establishes a relation model of the number of times of the people's average aircrafts and the people's average GDP, predicts the total amount of aircrafts in the future year region, and improves the prediction accuracy.
(2) Based on the theory of the land, the method utilizes the field intensity of the outgoing time consumption computer airport to each region to determine the land range of each airport, further provides an airport passenger throughput prediction model, creatively applies the geographic theory of the land to the airport group passenger throughput prediction model, and takes account of the influence of airport group competition factors, airport radiation rings and the influence of travelers on the flight selection on the throughput prediction, so that the prediction is more accurate.
(3) The invention also takes the Buddha new airport in the Guangdong province airport group as an example for application research, and the result shows that the airport passenger throughput prediction method based on the abdomen ground theory has practicability, and solves the problems that the traditional time sequence and regression analysis model has incomplete consideration of the competition relationship and airport functions among the airport groups.
It will be apparent to those skilled in the art that several modifications and improvements can be made to the embodiments of the present invention without departing from the inventive concept of the present application.
Claims (2)
1. A target airport group passenger throughput prediction method based on the abdominal theory is characterized in that the main flow is as follows:
1) Firstly dividing a target area into a plurality of areas, and utilizing the current year to carry out moving signaling data mining on the target area to obtain the number of times of people and average passengers in each area:
identifying population of a target area and number of passengers of the target area by using mobile phone signaling data, and obtaining current situation average passenger number index of each area by using a formula (1);
wherein, gamma' i Number of times of people take advantage of each machine (times/people/year) for current situation of area i, y it Aviation passenger flow gauge for area i to airport t identified by mobile phone signalingMode (number of people), p i Population (ten thousands) of people in region i, p i Identifying by using mobile phone signaling data;
2) According to the social economic development targets of each region, calculating and predicting the number of times of people taking in each region in the year;
based on the relationship that the increase rate of the number of times of the people and average aircrafts is approximately equal to the increase rate of the total production value in the people and average region, the current situation of the area i calculated in the step 1) is utilized to calculate the number of times gamma 'of the people and average aircrafts' i And calculating the number of times gamma of people's average passenger in each area i of the predicted year by using the formula (2) i ；
In the formula, GDP' is GDP (ten thousand yuan per person) for current status of city s for region i, GDP is Predicting annual average person GDP (ten thousand yuan/person) for the city s corresponding to the region i;
3) Predicting the aviation throughput scale of each airport in the target airport group;
the target airport group comprises m airports, and the aviation throughput scale of one airport t in the target airport group is calculated by adopting the following formula (3):
wherein f t For airport t aviation throughput (ten thousand people per year), p i Population (ten thousands) of people in region i, γ i Number of people per year (times/people/year) for prediction of region i, n is the number of regions in the t-web region of airport, sigma t As a functional factor of airport t, the sigma t The airport flight distribution and density influence factors;
the number n of the airport t-abdomen area is determined according to the following method:
3.1 Calculating the field strength of the airport t of the region i according to the following formula (4); characterization of the comprehensive scale Z of an airport by means of the taking-off and landing capabilities of the airport flight t Travel time consumption of using region i and airport tCharacterizing distanceThe time consumption of the outgoing line is acquired based on a time impedance matrix of the aggregation and dispersion network;
wherein F is ti For field strength of airport t in region i, Z t For the integrated scale of the airport t,for the distance from region i to airport t, α is the coefficient of friction;
wherein the distance of the region i to the airport tThe method is characterized by comprising the following steps:
establishing a full-province traffic network model according to province track and expressway planning conditions, connecting the hubs to the network model according to future planning and distributing facility conditions of each hub, and calculating time consumption matrixes of each area and each airport based on the model to obtain the time distance from the area i to the airport tWherein the provincial track comprises Gao Putie, inter-city and subway;
3.2 Determining airport t-land range area number n: comparing the field intensity values of m airports for the region i, if the maximum field intensity is airport j, circularly calculating the region i into the abdomen of airport j to all the regions, and dividing the region number n of the abdomen of each airport of the airport group.
2. The method for predicting the throughput of passengers in an airport group at a destination location based on the theory of the abdomen according to claim 1, wherein in said step 3), the function factor σ of airport t is t Is determined by (a)The method comprises the following steps:
calibrating the current number of times of passenger per unit passenger in each area and the abdominal region population calculation data according to the passenger throughput statistical data of each airport, and performing a function factor sigma t 0.82-1.14.
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