CN113537554A - Passenger willingness-to-pay prediction method, device and storage medium - Google Patents

Passenger willingness-to-pay prediction method, device and storage medium Download PDF

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CN113537554A
CN113537554A CN202110613299.8A CN202110613299A CN113537554A CN 113537554 A CN113537554 A CN 113537554A CN 202110613299 A CN202110613299 A CN 202110613299A CN 113537554 A CN113537554 A CN 113537554A
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苏焕银
陶文聪
彭舒婷
代慧子
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Abstract

The invention discloses a passenger willingness-to-pay prediction method, a passenger willingness-to-pay prediction device and a storage medium, wherein the method comprises the steps of obtaining personal attribute parameters and vehicle attribute parameters of passengers; obtaining a first utility through a first analysis model for analyzing group behavior heterogeneity according to the individual attribute parameters and the vehicle attribute parameters; obtaining a second effect through a second analysis model for analyzing personal behavior difference according to the personal attribute parameters and the vehicle attribute parameters; obtaining the payment willingness of the passenger according to the first utility and the second utility; and the willingness-to-pay is considered from multiple angles, so that the accuracy of predicting the willingness-to-pay of passengers is improved.

Description

Passenger willingness-to-pay prediction method, device and storage medium
Technical Field
The invention relates to the technical field of information, in particular to a passenger willingness-to-pay prediction method, a passenger willingness-to-pay prediction device and a storage medium.
Background
The Chinese high-speed railway is in the gradual market operation, different vehicles have certain differences in the aspects of departure time, running time, service level and the like, and the selection behaviors of passengers also have certain preference heterogeneity. The method considers the selection behavior characteristics of different passengers and the quality difference of train service, implements a high-quality premium and flexible floating fare mechanism, and is favorable for improving the economic benefit of railway transportation enterprises. Therefore, the heterogeneity of the riding selection behaviors of intercity railway passengers is researched, and decision support can be provided for the marketing strategy design of intercity railway passenger transport products.
Disclosure of Invention
The present invention at least solves one of the technical problems in the prior art, and provides a method, an apparatus and a storage medium for predicting a passenger's willingness-to-pay.
The technical scheme adopted by the invention for solving the problems is as follows:
in a first aspect of the present invention, a method for predicting a passenger's willingness-to-pay includes:
acquiring personal attribute parameters and vehicle attribute parameters of passengers;
obtaining a first utility through a first analysis model for analyzing group behavior heterogeneity based on the personal attribute parameters of the passengers and the vehicle attribute parameters;
obtaining a second effect through a second analysis model for analyzing personal behavior difference according to the personal attribute parameters of the passengers and the vehicle attribute parameters;
and obtaining the payment willingness of the passenger according to the first utility and the second utility.
According to a first aspect of the invention, said deriving a first utility from a first analytical model for analyzing group behavior heterogeneity based on said personal attribute parameters of said passengers and said vehicle attribute parameters comprises:
obtaining a passenger type corresponding to the passenger according to the personal attribute parameters of the passenger;
and obtaining a first utility according to the first analysis model according to the passenger type corresponding to the passenger and the vehicle attribute parameters.
According to a first aspect of the invention, the personal attribute parameters include the age, school calendar, income, frequency of travel and cost source of the passenger.
According to a first aspect of the invention, the vehicle attribute parameters include a fare and a run time of the vehicle.
In a second aspect of the present invention, a passenger willingness-to-pay prediction apparatus includes:
a parameter acquiring unit for acquiring personal attribute parameters of passengers and vehicle attribute parameters;
a first utility calculating unit, configured to obtain a first utility through a first analysis model for analyzing heterogeneity of group behaviors according to the personal attribute parameters of the passenger and the vehicle attribute parameters;
the second effectiveness calculation unit is used for obtaining second effectiveness through a second analysis model for analyzing personal behavior difference according to the personal attribute parameters of the passengers and the vehicle attribute parameters;
and the payment intention calculation unit is used for obtaining the payment intention of the passenger according to the first utility and the second utility.
According to a second aspect of the present invention, the first utility calculation unit includes:
the passenger type obtaining unit is used for obtaining a passenger type corresponding to a passenger according to the personal attribute parameters of the passenger;
and the first utility calculating subunit is used for obtaining a first utility according to the passenger type corresponding to the passenger and the vehicle attribute parameters and the first analysis model.
According to a second aspect of the invention, the personal attribute parameters include the age, school calendar, income, frequency of travel and cost source of the passenger.
According to a second aspect of the invention, the vehicle attribute parameters include a fare and a run time of the vehicle.
In a third aspect of the present invention, a passenger willingness-to-pay prediction apparatus comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the passenger willingness-to-pay prediction method according to the first aspect of the present invention when executing the computer program.
A fourth aspect of the present invention is a storage medium having stored therein executable instructions that, when executed by a processor, implement a passenger willingness-to-pay prediction method according to the first aspect of the present invention.
The scheme at least has the following beneficial effects: the first utility of selecting the train on the premise that the passenger belongs to a certain passenger type is analyzed through the first analysis model aiming at the group behavior heterogeneity of the passenger group, the second utility of selecting the train by the passenger is analyzed through the second analysis model aiming at the individual behavior heterogeneity of the passenger, and then the payment intention of the passenger for the train is obtained according to the first utility and the second utility by combining the group behavior heterogeneity and the individual behavior heterogeneity; and the willingness-to-pay is considered from multiple angles, so that the accuracy of predicting the willingness-to-pay of passengers is improved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The invention is further illustrated with reference to the following figures and examples.
Fig. 1 is a flowchart of a passenger willingness-to-pay prediction method according to an embodiment of the invention;
fig. 2 is a detailed flowchart of step S200;
fig. 3 is a flowchart of a passenger willingness-to-pay prediction apparatus according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the present preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like elements throughout.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the directions of up, down, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality of means is one or more, the meaning of a plurality of means is two or more, and larger, smaller, larger, etc. are understood as excluding the present number, and the meaning of larger, smaller, etc. are understood as including the present number. If there is a description of first and second for the purpose of distinguishing technical features, it is not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of technical features indicated.
In the description of the present invention, unless otherwise expressly limited, the terms set, mounted, connected, etc. should be construed broadly, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in light of the detailed contents of the technical solutions.
Referring to fig. 1, an embodiment of a first aspect of the present invention provides a passenger payment intention prediction method.
The passenger willingness-to-pay prediction method comprises the following steps:
s100, acquiring personal attribute parameters and vehicle attribute parameters of passengers;
s200, obtaining a first utility through a first analysis model for analyzing the heterogeneity of group behaviors according to the personal attribute parameters of passengers and the attribute parameters of vehicles;
step S300, obtaining a second effect through a second analysis model for analyzing personal behavior difference according to the personal attribute parameters of the passengers and the attribute parameters of the vehicles;
and S400, obtaining the passenger willingness to pay according to the first utility and the second utility.
For step S100, personal attribute parameters and vehicle attribute parameters of the passenger are acquired, among others. Personal attribute parameters include the age, academic calendar, income, frequency of travel and cost source of the passenger. Vehicle attribute parametersIncluding the fare and run time of the vehicle. Wherein, ageiIndicates the age, edu, of the ith passengeriIndicating the academic calendar, inc, of the ith passengeriIndicates the income of the ith passenger, freiRepresenting the trip frequency of the ith passenger, reiiIndicating the source of the fare of the ith passenger, cjShows the fare of the jth train, tjThe operation time of the jth train is shown.
It should be noted that the personal attribute parameters may also include other parameters, such as gender, physical health, and the like. Vehicle attribute parameters may also include other such as quality of service ratings, etc.
Referring to fig. 2, certain embodiments of the first aspect of the present invention, for step S200, derive a first utility from a first analysis model for analyzing group behavior heterogeneity as a function of personal attribute parameters and vehicle attribute parameters of passengers, comprising:
step S210, obtaining a passenger type corresponding to the passenger according to the personal attribute parameters of the passenger;
and S220, obtaining a first utility according to the passenger type and the vehicle attribute parameters corresponding to the passenger and the first analysis model.
In this embodiment, the first analysis model can account for the heterogeneity of group behaviors, and its purpose is to divide the traveler into several potential categories, and estimate the parameters corresponding to different categories, respectively, so as to analyze the differences between the categories. Let i denote the passenger individual, J denote the alternative train, J ∈ {1,2, …, J }. S represents the passenger type, S ∈ {1,2, …, S }.
The utility function for passenger i belonging to the s-th class is expressed as follows:
Figure BDA0003096482060000061
in the formula, Vi(s) is a determined first effect, εi(s) is a random term of the utility,
Figure BDA0003096482060000062
respectively corresponding to the age, school calendar, income, frequency and cost of passengersSource-related weight parameters.
Then, the probability that passenger i belongs to the s-th category is:
Figure BDA0003096482060000063
the first utility is represented by a utility function of selecting the j train by the passenger i on the premise of belonging to the s type, and the utility function of selecting the j train by the passenger i on the premise of belonging to the s type is as follows: u shapei(j/s)＝Vi(j/s)+εi(j/s)＝β1/scj+β2/stj+εi(j/s); in the formula, beta1/sAnd beta2/sAs a weight parameter, epsiloni(j/s) is a random term for this utility.
In practice, it can be found that the probability that the passenger i selects the jth train if the passenger i belongs to the s-th type is:
Figure BDA0003096482060000071
in fact, passengers are classified into two types, economy type passengers and business type passengers. Wherein the fare sensitivity is greater than the run time sensitivity for economy passengers. For business type passengers, fare sensitivity is less than run-time sensitivity.
For step S300, a second utility is obtained through a second analysis model for analyzing personal behavior variability according to the personal attribute parameters of the passengers and the vehicle attribute parameters. In this embodiment, for the second analysis model, as the train fare and the operation time vary with the variation of the train j, the age, the academic history, the monthly income, the trip frequency and the cost source of the passengers are relatively fixed.
The fare of the train and the parameters of the running time in the trip selection utility function are random parameters, and the utility function of the passenger i for selecting the j-th train is represented as follows: vi(j)＝(β1+α1vc)cj+(β2+α2vt)tj(ii) a In the formula, beta1+α1vcAs a random parameter of the fare, beta2+α2vtRandom parameters of the running time are all in accordance with normal distribution; beta is a1、β2Is the mean value of a random parameter, alpha1、α2Is the standard deviation of the random parameter; v. ofc、vtIs a random variable, obeying a standard normal distribution.
The probability that passenger i selects the jth train is:
Figure BDA0003096482060000081
to analyze the influence of the passenger personal attributes on the random parameters, the passenger personal attributes are taken into account in the random parameter setting. Then, the observable portion of the utility function for passenger i selecting the jth train, considering the passenger's personal attributes, is represented as follows: vi(j)＝(β1+δ1zi+α1vc)cj+(β2+δ2zi+α2vt)tj；
Figure BDA0003096482060000082
In the formula, deltakVector of parameters corresponding to attributes of the person, ziA vector representing the composition of the personal attribute variables,
Figure BDA0003096482060000083
Figure BDA0003096482060000084
are parameters.
For step S400, the passenger' S willingness-to-pay is obtained according to the first utility and the second utility. The passenger's willingness-to-pay is expressed as follows:
Figure BDA0003096482060000085
in the formula, t is train running time, c is train fare, V is trip selection utility of passengers, and V is the sum of the first utility and the second utility.
According to the passenger willingness-to-pay prediction method, a first utility of selecting a train on the premise that a passenger belongs to a certain passenger type is analyzed according to group behavior heterogeneity of a passenger group through a first analysis model, a second utility of selecting the train by the passenger is analyzed according to personal behavior heterogeneity of the passenger through a second analysis model, and then the willingness-to-pay of the passenger on the train is obtained according to the first utility and the second utility by combining the group behavior heterogeneity and the personal behavior heterogeneity; and the willingness-to-pay is considered from multiple angles, so that the accuracy of predicting the willingness-to-pay of passengers is improved.
Referring to fig. 3, an embodiment of a second aspect of the present invention provides a passenger payment intention prediction apparatus.
The passenger willingness-to-pay prediction device includes:
a parameter acquisition unit 10 for acquiring personal attribute parameters of passengers and vehicle attribute parameters;
a first utility calculation unit 20 for obtaining a first utility through a first analysis model for analyzing heterogeneity of group behaviors, based on personal attribute parameters of passengers and transportation tool attribute parameters;
the second utility calculation unit 30 is used for obtaining a second utility through a second analysis model for analyzing personal behavior differences according to the personal attribute parameters of the passengers and the attribute parameters of the transportation tools;
and the willingness-to-pay calculation unit 40 is used for obtaining the willingness-to-pay of the passenger according to the first utility and the second utility.
In some embodiments of the second aspect of the present invention, the first utility calculation unit 20 comprises:
the passenger type obtaining unit is used for obtaining a passenger type corresponding to the passenger according to the personal attribute parameters of the passenger;
and the first utility calculating subunit is used for obtaining a first utility according to the passenger type corresponding to the passenger and the vehicle attribute parameters and the first analysis model.
Specifically, the personal attribute parameters include the age, academic calendar, income, travel frequency, and cost source of the passenger. The vehicle attribute parameters include the fare and run time of the vehicle.
In this embodiment, the first utility of selecting a train on the premise that a passenger belongs to a certain passenger type is analyzed for group behavior heterogeneity of a passenger group by the first utility calculating unit 20 using a first analysis model, the second utility of selecting a train is analyzed for individual behavior heterogeneity of the passenger by the second utility calculating unit 30 using a second analysis model, and then the willingness-to-pay of the passenger for the train is obtained according to the first utility and the second utility by the willingness-to-pay calculating unit 40 combining the group behavior heterogeneity and the individual behavior heterogeneity; and the willingness-to-pay is considered from multiple angles, so that the accuracy of predicting the willingness-to-pay of passengers is improved.
It should be noted that, the willingness-to-pay prediction device provided by the second aspect of the present invention adopts the method of the willingness-to-pay prediction device provided by the first aspect of the present invention, has the same technical solution, solves the same technical problems, and has the same technical effects, and will not be described in detail herein.
In an embodiment of the third aspect of the present invention, a passenger willingness-to-pay prediction apparatus is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the passenger willingness-to-pay prediction method according to the embodiment of the first aspect of the present invention when executing the computer program.
If the memory, the processor and the communication interface are implemented independently, the memory, the processor and the communication interface may be connected to each other through a bus and perform communication with each other. The bus may be an industry standard architecture bus, a peripheral device interconnect bus, or an extended industry standard architecture bus, among others. The bus may be divided into an address bus, a data bus, a control bus, etc.
Optionally, in a specific implementation, if the memory, the processor, and the communication interface are integrated on a chip, the memory, the processor, and the communication interface may complete mutual communication through the internal interface.
An embodiment of the fourth aspect of the invention, a storage medium having stored therein executable instructions that when executed by a processor implement a passenger willingness-to-pay prediction method as described in the first aspect of the invention.
The computer readable media of embodiments of the present invention may be computer readable signal media or computer readable storage media or any combination of the two. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable read-only memory (CDROM). Additionally, the computer-readable storage medium may even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
In an embodiment of the present invention, the storage medium may comprise a propagated data signal with the computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, input method, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, radio frequency, or any suitable combination of the foregoing.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a dedicated integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and the present invention shall fall within the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means.

Claims (10)

1. A passenger willingness-to-pay prediction method, comprising:
acquiring personal attribute parameters and vehicle attribute parameters of passengers;
obtaining a first utility through a first analysis model for analyzing group behavior heterogeneity based on the personal attribute parameters of the passengers and the vehicle attribute parameters;
obtaining a second effect through a second analysis model for analyzing personal behavior difference according to the personal attribute parameters of the passengers and the vehicle attribute parameters;
and obtaining the payment willingness of the passenger according to the first utility and the second utility.
2. The passenger willingness-to-pay prediction method according to claim 1, wherein the obtaining of the first utility through the first analysis model for analyzing the heterogeneity of group behaviors according to the personal attribute parameters and the vehicle attribute parameters of the passenger comprises:
obtaining a passenger type corresponding to the passenger according to the personal attribute parameters of the passenger;
and obtaining the first utility according to the first analysis model according to the passenger type corresponding to the passenger and the vehicle attribute parameters.
3. The method of claim 1, wherein the personal attribute parameters include age, academic calendar, income, travel frequency and expense source of the passenger.
4. A passenger willingness-to-pay prediction method according to claim 1, wherein the vehicle attribute parameters comprise a fare and a running time of the vehicle.
5. A passenger willingness-to-pay prediction apparatus, comprising:
the parameter acquisition unit is used for acquiring personal attribute parameters and vehicle attribute parameters of passengers;
a first utility calculation unit for obtaining a first utility through a first analysis model for analyzing heterogeneity of group behaviors according to the personal attribute parameters of the passengers and the vehicle attribute parameters;
the second effectiveness calculation unit is used for obtaining second effectiveness through a second analysis model for analyzing personal behavior difference according to the personal attribute parameters of the passengers and the vehicle attribute parameters;
and the payment intention calculation unit is used for obtaining the payment intention of the passenger according to the first utility and the second utility.
6. The passenger willingness-to-pay prediction device according to claim 5, wherein the first utility calculation unit comprises:
the passenger type obtaining unit is used for obtaining a passenger type corresponding to the passenger according to the personal attribute parameter of the passenger;
and the first utility calculating subunit is used for obtaining a first utility according to the passenger type corresponding to the passenger and the vehicle attribute parameters and the first analysis model.
7. The passenger willingness-to-pay prediction device according to claim 5, wherein the personal attribute parameters comprise the age, academic history, income, trip frequency and expense source of the passenger.
8. A passenger willingness-to-pay prediction device according to claim 5, wherein the vehicle attribute parameters include a fare and a running time of the vehicle.
9. Passenger willingness-to-pay prediction device, characterized by comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements a passenger willingness-to-pay prediction method according to any one of claims 1 to 4.
10. Storage medium, characterized in that it stores executable instructions that, when executed by a processor, implement the passenger willingness-to-pay prediction method of any one of claims 1 to 4.
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