CN114154393A - Method for predicting passenger throughput of target ground airport group based on abdominal theory - Google Patents
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Abstract
The invention discloses a method for predicting the passenger throughput of a target ground airport group based on an abdominal theory, which comprises the following steps: 1) firstly, dividing a target area into a plurality of areas, and mining the mobile signaling data of the target area in the current year to obtain the number of times of riding the airplane by each person in each area in the current situation; 2) reasonably predicting the number of times of riding the airplane by each person in each region in the future; 3) predicting the aerial throughput scale of each airport in the target airport group: according to the travel accessibility (travel time consumption) of the airport collection and distribution, the belly range of the airport is divided by using the belly theory, and the scale of taking the airplane in the region is shared to each airport; finally, factor adjustment is carried out by combining the specific functions of each airport to obtain the passenger throughput scale of each airport; the prediction method has the feasibility of implementation, and solves the problems that the traditional time sequence and regression analysis model are not comprehensive enough in consideration of the competitive 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 method for predicting the throughput of passengers of a target airport group based on an abdominal theory.
Background
Passenger throughput is an important basis for determining the function and scale of each airport of an airport group. At present, methods for predicting airport passenger throughput are more, qualitative prediction methods comprise survey prediction methods, similarity methods, collective opinion methods, Delphi methods and the like, and quantitative prediction methods comprise time series methods, trend extrapolation methods, regression analysis methods and the like [ Whitebackforest, civil airport passenger throughput prediction methods discuss [ J ] science forum, 2010(1): 142-. Chenyubao, which has just been used to overcome the defects of a single quantitative prediction method, based on a multivariate linear regression model and a time series trend extrapolation model, adopts a combined weighting method to carry out combined prediction on a prediction result to improve the prediction accuracy and reduce the prediction error [ Chenyubao, once and just. Li Minxi is based on the change characteristics of the throughput of the airport passenger, and an improved gray model obtained by carrying out residual error correction on a gray model by an artificial neural network is used for prediction of the throughput of the airport passenger [ Li Minxi. From the perspective of ground collection and distribution, such as plum, fang dong tao and the like, a four-stage method is proposed as a basis, and an airport collection and distribution characteristic optimization direction distribution model is combined [ plum, livingh, fang dong tao, large airport ground collection and distribution demand prediction method research [ J ] traffic transportation engineering and informatics report, 2013(4):87-93 ]; the road and yao utilizes a generalized regression neural network and a genetic BP neural network to provide an airport passenger spatial distribution prediction optimization model [ road and yao, urban spatial distribution prediction and traffic mode selection behavior research [ D ]. Beijing: Beijing industry university, 2019 ].
The existing airport group passenger throughput prediction method has the following defects:
the existing passenger throughput prediction model mostly takes GDP and historical passenger flow data as regression analysis, and neglects the influence of airport group competition factors, airport radiation circles and the influence of travelers on flight selection.
Disclosure of Invention
In view of the above, in order to overcome the defects of neglecting airport group competition factors, influence of airport radiation circles, influence of travelers on flight selection and the like in the traditional airport group passenger throughput prediction method, the invention takes big data as support, considers the competition relationship of each airport of an airport group, provides an airport passenger throughput prediction model based on the theory of the abdominal region, and reasonably predicts the airport passenger throughput from the regional airport competition perspective.
The technical scheme of the invention is as follows:
a method for predicting passenger throughput of a target ground airport group based on an abdominal theory mainly comprises the following processes:
1) firstly, dividing a target area into a plurality of areas, and mining the mobile signaling data of the target area in the current year to obtain the number of times of riding the airplane by each person in each area in the current situation:
identifying population of a target area and the number of passengers taking the airplane in the target area by using mobile phone signaling data, and obtaining the current person average number of passengers taking the airplane in each area by using a formula (1);
in formula (II), gamma'iFor area i, the number of times (times/person/year) that the person takes the airplane in average, yitFor the area i identified by the mobile phone signaling to the airport t, the air passenger flow scale (number of people), piThe number of population (ten thousand) of the area i, the piIdentifying by using the mobile phone signaling data;
2) calculating the number of times of taking the airplane by each regional person in the forecast year according to the social and economic development target of each region;
based on the fact that the growth rate of the number of people who are riding the aircraft is approximately equal to the growth rate relation of the total production value of the people-average region, the current number of people who are riding the aircraft gamma 'of the region i obtained through calculation in the step 1) is utilized'iAnd calculating the number gamma of the average number of riding the airplane in each area i of the forecast year by using a formula (2)i；
In formula (II), GDP'isGDP (universal unit/person) corresponding to city s current situation per person for region iisPredicting a mean-per-year-person GDP (ten thousand yuan/person) for the city s corresponding to the region i;
3) predicting the aerial throughput scale of each airport in the airport group of the target place;
the target airport group comprises m airports, and the aviation throughput scale of one airport t in the target airport group is calculated by the following formula (3):
in the formula (f)tFor airport t, air throughput (ten thousand persons/year), piNumber of people in area i (ten thousand), γiThe predicted annual average number of times of taking the airplane (times/person/year) for the region i, n is the number of regions in the belly region of the airport t, and sigmatIs a functional factor of airport t, the sigmatThe factors are influence factors of airport flight distribution and density.
Preferably, in the step 3),
the number n of the t-belly area areas of the airport 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); comprehensive scale Z for representing airport by using airport flight taking-off and landing capacitytRepresenting distance by using travel time consumption of area i and airport t
Acquiring a time impedance matrix of travel time consumption based on a collecting and distributing network;
in the formula, FtiField strength, Z, for an airport t in the area itFor the comprehensive scale of the airport t,
the distance from the area i to the airport t, and alpha is a friction coefficient;
wherein the distance of the area i to the airport t
The determination is carried out according to the following method:
establishing a provincial traffic network model according to provincial track and highway planning conditions, connecting hubs to the network model according to the future planning distribution facility conditions of each hub, measuring and calculating time consumption matrixes of each region and each airport based on the model, and obtaining the time distance from the region i to the airport t
Wherein, the provincial track comprises high common railway, intercity and subway;
3.2) determining the number n of airport t-belly area regions: and comparing the field intensity values of m airports of the area i, if the maximum field intensity is the airport j, taking the area i into the belly of the airport j, and circularly calculating all the areas so as to divide the belly range area number n of each airport of the airport group.
Preferably, in the step 3),
functional factor sigma of airport ttThe determination steps are as follows:
according to the passenger throughput statistical data of each airport, the number of times that each person in each area takes the airplane and the calculation data of the abdominal population are calibrated, and the function factor sigmatIn the range of 0.82-1.14.
Compared with the prior art, the invention adopting the technical scheme has the following beneficial effects:
(1) the invention marks the number of times of the current average passenger ride by means of the latest big data technology means of mobile phone signaling, establishes a relation model between the number of times of the current average passenger ride and the GDP of the current average passenger ride, predicts the total passenger ride amount of the region in the future year and improves the accuracy of prediction.
(2) The method is based on the abdominal theory, the field intensity of the airport to each region is calculated by using travel time consumption, the abdominal range of each airport is determined, the airport passenger throughput prediction model is further provided, the abdominal theory is innovatively applied to the airport group passenger throughput prediction model, the airport group competition factor, the influence of an airport radiation circle and the influence of a traveler on flight selection on throughput prediction are considered, and prediction is more accurate.
(3) The invention also takes the Foshan airport in the airport group of Guangdong province as an example for application research, and the result shows that the airport passenger throughput prediction method based on the abdominal theory has the feasibility and solves the problems that the traditional time sequence and regression analysis model have incomplete competition relationship among the airport group and the airport function consideration.
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 present invention will be described in further detail with reference to the accompanying drawings and examples.
1 brief summary of the Abdominal theory
1.1 Abdominal theory
The hind land (hinderland) origin means the subordinate areas of the harbours, through which the harbours gather export supplies and distribute import supplies. Much more is now said of the spatial reach of interest in any colony (or commercial facility in a colony), being the area where the colony is a commodity exchange hub [ R.J.Johnston, etc. the Dictionary of Huamn Geography [ M ]. USA: Cambridge, Massachusetts,1996 ]. Christaller establishes a central theory in the central region of the southern Germany, and proposes a basic concept of the central region and the commodity service range [ ray. M. Northan. Urban Geography [ M ]. USA: New York, 1975 ], defines the central abdominal region as a spatial boundary where a certain central commodity can reach the hands of consumers, and marks the birth of the concept of the abdominal region. Thereafter, concepts such as the abdominal region of the city have been gradually proposed and expanded, and various theories have been developed, but the abdominal region is an influence and a radiation region of the central region, and the central region is closely connected with the abdominal region through traffic, information, and the like. With the development of decades, the abdominal theory is gradually applied to economic divisions and area planning, city planning, port planning, service facility layout planning, and company headquarter site selection.
1.2 Abdominal Range determination
The central city is the core of a certain area, the influence is called as "field intensity", therefore, the field intensity model is one of the main methods for determining the range of the abdominal region, and the calculation formula is shown as (1):
in the formula, FikField strength at point k for city i, ZiFor the comprehensive scale of the city of i,
i city to k point distance, and α is the coefficient of friction. After the field intensity of each point is determined, the urban abdominal area range is determined according to the field intensity.
2 airport passenger throughput prediction process
Referring to fig. 1, the process of predicting passenger throughput in each airport in the target airport group is as follows:
1) firstly, dividing a target area into a plurality of areas, and mining the mobile signaling data of the target area in the current year to obtain the number of times of riding the airplane by each person in each area in the current situation;
2) reasonably predicting the number of times of taking the airplane by each person in each region in the future: calculating the number of times of taking the airplane by each regional person in the forecast year according to the social and economic development target of each region;
3) predicting the aerial throughput scale of each airport in the target airport group: according to the travel accessibility (travel time consumption) of the airport collection and distribution, the belly range of the airport is divided by using the belly theory, and the scale of taking the airplane in the region is shared to each airport; and finally, factor adjustment is carried out by combining the specific functions of each airport to obtain the passenger throughput scale of each airport.
3 passenger throughput prediction model construction
3.1 throughput prediction model
Research of scholars at home and abroad shows that a certain relationship exists between the population of a region and the throughput of passengers at an airport, and the population is an important factor for measuring the aviation market; the number of the passengers taking the airplane per capita is reflected by the willingness of local population to take the airplane, and is the representation of the sharing of the aviation market after the local economic development level and the comprehensive transportation network are fully competitive; the airport passenger throughput also depends on the competitiveness of the airport to a certain extent, and specifically comprises factors such as passenger flow abdominal areas, airport self functions (airline density and distribution) and the like, so that a prediction model of the airport passenger throughput, the abdominal area range, the population scale, the number of passengers taking the airplane per se and the airport function factors shown in the following formula (2) is established.
In the formula (f)tFor airport t, air throughput (ten thousand persons/year), piNumber of people in area i (ten thousand), γiThe predicted annual average number of times of taking the airplane (times/person/year) for the region i, n is the number of regions in the belly region of the airport t, and sigmatIs a functional factor of the airport t (mainly an airport flight distribution and density influence factor).
3.2 prediction of number of man-averaged riding times based on mobile phone signaling data
The number of man-shared rides can be obtained by questionnaire survey, but for airport groups, the questionnaire survey is difficult to obtain satisfactory results due to administrative restrictions and sample restrictions. With the popularization and the mature technology of the mobile phone signaling data, the area population and the number of the local passengers are identified by using the mobile phone signaling data (a specific algorithm is shown in fig. 2), and the index of the number of the current average passengers is obtained.
In formula (II), gamma'iFor area i, the number of times (times/person/year) that the person takes the airplane in average, yitFor the area i identified by the mobile phone signaling to the airport t, the air passenger flow scale (number of people), piThe population of the area i (ten thousand people, identified by the cell phone signaling data).
Because historical mobile phone signaling data is difficult to obtain, the number of times of taking the airplane by each city grade in each year is calculated by adopting the actual passenger throughput of the airport, and the number of times of taking the airplane by each city grade is associated and analyzed with the production total value (according to urban statistics), and the result shows that the increase rate of the number of times of taking the airplane by each person is approximate to the increase rate of the production total value of each area by each person (see table 1). Based on the relationship that the growth rate of the average number of passengers is approximately equal to the total production value growth rate of the average area, the average number of passengers of each area i in 2019 is calculated by using the mobile phone signaling data, and a model of the average number of passengers of each area i in the next year is predicted by using a formula (4).
In formula (II), GDP'isGDP (universal unit/person) corresponding to city s current situation per person for region iisThe annual average GDP (ten thousand yuan/person) is predicted for the city s corresponding to the area i.
TABLE 1 Presence GDP and number of man-riding relation list
Note:
3.3 airport passenger flow abdominal analysis based on the Abdominal theory
Similar to the field intensity model, the amplitude of each airport in the airport group is related to the collecting and distributing network and the airport scale, so the abdominal range of the airport t is determined according to the following method:
STEP1 calculation of the field intensity F of the airport t of the area i according to the equation (1)ti. Representation of airport scale Z by airport flight taking-off and landing capacitytRepresenting distance by using travel time consumption of area i and airport t
And the travel time consumption is obtained based on a time impedance matrix of the collection and distribution network.
STEP2, determining the number n of airport t-belly area areas: and comparing the field intensity values of m airports of the area i, if the maximum field intensity is the airport j, taking the area i into the belly of the airport j, and circularly calculating all the areas so as to divide the belly range area number n of each airport of the airport group.
3.4 determination of airport functional factors
Besides the passenger flow in the abdominal region, the airport passenger throughput also includes the passenger flow transit, and meanwhile, the influence of the flight number, the shift and the like is also considered in the selection of the airport for the passenger flow in the abdominal region, namely, the passenger throughput is also influenced by the airport function, so that the passenger flow calculated in the abdominal region needs to be corrected by using airport function factors. Calibrating the current number of man-riding times and the calculation data of the population in the belly according to the passenger throughput statistical data of Guangzhou Baiyun airport, Shenzhen Baoan airport, Huizhou airport, Zhuhai airport, Zhanjiang airport, Duohanshan airport and Meizhou airport, and using a functional factor sigmatIn the range of 0.82-1.14.
TABLE 2 airport function factor calibration results List
| Name of airport | Abdomen floor passenger flow (thousands of people) | Actual throughput (thousands of people) | Function factor sigmat |
| Guangzhou white cloud airport | 6418.9 | 7337.8 | 1.14 |
| Baoan + Huizhou airport | 5234.2 | 5548.5 | 1.06 |
| Zhuhai airport | 1370.3 | 1228.2 | 0.90 |
| Zhanjiang airport | 365.9 | 298.4 | 0.82 |
| Shaoyang Chaoshan + Meizhou airport | 924.9 | 802.6 | 0.87 |
4 practical application-prediction of passenger flow of airport group in Guangdong province
4.1 prediction of average riding times of Guangdong province
The Guangdong province is divided into 1678 traffic zones, and the mobile signaling data of the Guangdong province in 2019 is mined to obtain the number of times of taking the average person in each zone in the current situation, wherein the number of times of taking the average person in the current situation of the whole province is about 1.24 times/person/year, the number of times of taking the average person in Guangzhou and Shenzhen reaches about 2.9 times/person/year and 2.64 times/person/year, the number of times of taking the average person in the Fushan reaches about 1.53 times/person/year, and the number of times of taking the average person in the areas such as Cannon, Zhaoqing, Heyuan and Yang revenue is still lower.
And (4) according to a formula (4), calculating and predicting the number of times of riding the airplane of each regional person in each year according to the social and economic development targets of each city. The calculation result shows that the number of passengers taking the airplane by the average person in 2035 years in Guangdong province is 2.53 times/person/year, the number of passengers taking the airplane by the average person in 2050 years is 3.1 times/person/year, and the number of passengers taking the airplane by the average person in the state of America is equal to that of the passengers taking the airplane by the average person in the state of America (3 times/person/year), and is half of that of Singapore (6.2 times/person/year).
4.2 airport group passenger flow abdominal area range division in Guangdong province
According to the planning conditions of the global province track (including high-speed rail, intercity and subway) and the highway, a global province traffic network model is established, hubs are connected to the network model according to the future planning and distribution facility conditions of each hub, and the time consumption matrix of each region and each airport is calculated based on the model, so that the belly region range of each airport is divided.
4.3 prediction of passenger flow size at each airport in airport group in Guangdong province
The abdominal region passenger flow scale of each airport obtained by calculation according to the planned population of each region, the number of times of taking the airplane by the average person and the range division of the abdominal region of the airport is shown in table 3. The Fushan New airport is a regional hub airport, and can judge that the functional position of the Fushan New airport in the region is not lower than that of the current Zhuhai airport in the peripheral region thereof under the most unfavorable condition by combining the planning condition of the current gathering and distributing system, and in addition, as the Fushan New airport is an important component of the Guangzhou international aviation hub together with the auxiliary Baiyun airport in the future, the functional position of the Fushan New airport can be judged to be under the most favorable development condition and not exceed that of the Baiyun airport in the region, so that the functional factors are about 0.9-1.14 (respectively the functional factors of the Zhuhai airport and the Baiyun international airport), the fluctuation of the actual throughput level of the Fushan New airport in 2035 years and about 2900 and 3700 thousands of people per year can be predicted; in 2050, the fluctuation was about 5300-6700 persons/year.
TABLE 3 passenger flow Scale List for airport open area
Compared with the prior art, the invention adopting the technical scheme has the following beneficial effects:
(1) the invention marks the number of times of the current average passenger ride by means of the latest big data technology means of mobile phone signaling, establishes a relation model between the number of times of the current average passenger ride and the GDP of the current average passenger ride, predicts the total passenger ride amount of the region in the future year and improves the accuracy of prediction.
(2) The method is based on the abdominal theory, the field intensity of the airport to each region is calculated by using travel time consumption, the abdominal range of each airport is determined, the airport passenger throughput prediction model is further provided, the abdominal theory is innovatively applied to the airport group passenger throughput prediction model, the airport group competition factor, the influence of an airport radiation circle and the influence of a traveler on flight selection on throughput prediction are considered, and prediction is more accurate.
(3) The invention also takes the Foshan airport in the airport group of Guangdong province as an example for application research, and the result shows that the airport passenger throughput prediction method based on the abdominal theory has the feasibility and solves the problems that the traditional time sequence and regression analysis model have incomplete competition relationship among the airport group and the airport function consideration.
It will be apparent to those skilled in the art that various modifications and improvements can be made to the embodiments of the present invention without departing from the inventive concept of the present application, which falls within the scope of the present application.
Claims (3)
1. A method for predicting passenger throughput of a target ground airport group based on an abdominal theory is characterized by comprising the following main processes:
1) firstly, dividing a target area into a plurality of areas, and mining the mobile signaling data of the target area in the current year to obtain the number of times of riding the airplane by each person in each area in the current situation:
identifying population of a target area and the number of passengers taking the airplane in the target area by using mobile phone signaling data, and obtaining the current person average number of passengers taking the airplane in each area by using a formula (1);
in formula (II), gamma'iFor area i, the number of times (times/person/year) that the person takes the airplane in average, yitFor the area i identified by the mobile phone signaling to the airport t, the air passenger flow scale (number of people), piThe number of population (ten thousand) of the area i, the piIdentifying by using the mobile phone signaling data;
2) calculating the number of times of taking the airplane by each regional person in the forecast year according to the social and economic development target of each region;
based on the fact that the growth rate of the number of people who are riding the aircraft is approximately equal to the growth rate relation of the total production value of the people-average region, the current number of people who are riding the aircraft gamma 'of the region i obtained through calculation in the step 1) is utilized'iAnd calculating the number gamma of the average number of riding the airplane in each area i of the forecast year by using a formula (2)i；
In formula (II), GDP'isGDP (universal unit/person) corresponding to city s current situation per person for region iisPredicting a mean-per-year-person GDP (ten thousand yuan/person) for the city s corresponding to the region i;
3) predicting the aerial throughput scale of each airport in the airport group of the target place;
the target airport group comprises m airports, and the aviation throughput scale of one airport t in the target airport group is calculated by the following formula (3):
in the formula (f)tFor airport t, air throughput (ten thousand persons/year), piNumber of people in area i (ten thousand), γiThe predicted annual average number of times of taking the airplane (times/person/year) for the region i, n is the number of regions in the belly region of the airport t, and sigmatIs a functional factor of airport t, the sigmatThe factors are influence factors of airport flight distribution and density.
2. The method for predicting passenger throughput of target ground fleet according to claim 1, wherein in step 3),
the number n of the t-belly area areas of the airport 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); comprehensive scale Z for representing airport by using airport flight taking-off and landing capacitytRepresenting distance by using travel time consumption of area i and airport t
Acquiring a time impedance matrix of travel time consumption based on a collecting and distributing network;
in the formula, FtiField strength, Z, for an airport t in the area itFor the comprehensive scale of the airport t,
the distance from the area i to the airport t, and alpha is a friction coefficient;
wherein the distance of the area i to the airport t
The determination is carried out according to the following method:
according to provincial region railEstablishing a provincial traffic network model according to the planning conditions of roads and highways, connecting hubs to the network model according to the future planning and distribution facility conditions of each hub, measuring and calculating time consumption matrixes of each area and each airport based on the model, and obtaining the time distance from the area i to the airport t
Wherein, the provincial track comprises high common railway, intercity and subway;
3.2) determining the number n of airport t-belly area regions: and comparing the field intensity values of m airports of the area i, if the maximum field intensity is the airport j, taking the area i into the belly of the airport j, and circularly calculating all the areas so as to divide the belly range area number n of each airport of the airport group.
3. The method for predicting passenger throughput of target ground fleet according to any one of claims 1 to 2, wherein in the step 3),
functional factor sigma of airport ttThe determination steps are as follows:
according to the passenger throughput statistical data of each airport, the number of times that each person in each area takes the airplane and the calculation data of the abdominal population are calibrated, and the function factor sigmatIn the range of 0.82-1.14.
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