CN113537554B - Passenger willingness to pay prediction method, device and storage medium - Google Patents
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Abstract
The invention discloses a passenger willingness to pay prediction method, a 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 personal attribute parameter and the vehicle attribute parameter; obtaining a second utility through a second analysis model for analyzing the personal behavior differences according to the personal attribute parameters and the vehicle attribute parameters; obtaining a passenger willingness to pay according to the first utility and the second utility; and the willingness to pay is considered from a plurality of angles, so that the accuracy of predicting the willingness to pay of the passengers is improved.
Description
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 China high-speed railway is gradually operated in a market, different vehicles have certain differences in departure time, running time, service level and the like, and the selection behaviors of passengers also have certain preference heterogeneity. Considering the selection behavior characteristics of different passengers and the difference of train service quality, a high-quality premium and flexible floating fare mechanism is implemented, and the economic benefit of railway transportation enterprises is improved. Therefore, the decision support can be provided for marketing strategy design of inter-city railway passenger transport products by researching the riding selection behavior heterogeneity of inter-city railway passengers.
Disclosure of Invention
The invention aims to at least solve one of the technical problems in the prior art and provides a passenger willingness to pay prediction method, a passenger willingness to pay prediction device and a storage medium.
The invention solves the problems by adopting the following technical scheme:
in a first aspect of the present invention, a method for predicting willingness-to-pay of a passenger 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 according to the personal attribute parameters of the passengers and the attribute parameters of the vehicles;
obtaining a second utility through a second analysis model for analyzing the personal behavior differences according to the personal attribute parameters of the passengers and the attribute parameters of the vehicles;
and obtaining the willingness to pay of the passengers according to the first utility and the second utility.
According to a first aspect of the present invention, the obtaining, according to the personal attribute parameter of the passenger and the vehicle attribute parameter, a first utility by a first analysis model for analyzing group behavior heterogeneity includes:
obtaining the passenger type corresponding to the passenger according to the personal attribute parameters of the passenger;
and obtaining a first utility according to the passenger type corresponding to the passenger and the attribute parameter of the vehicle and the first analysis model.
According to a first aspect of the invention, the personal attribute parameters include the age, school, income, travel frequency and cost source of the passenger.
According to a first aspect of the invention, the vehicle attribute parameters include fare and run time of the vehicle.
In a second aspect of the present invention, a passenger willingness-to-pay prediction apparatus includes:
a parameter acquisition unit for acquiring personal attribute parameters of passengers and attribute parameters of vehicles;
the first utility calculating unit is used for obtaining first utility through a first analysis model for analyzing group behavior heterogeneity according to the personal attribute parameters of the passengers and the attribute parameters of the vehicles;
a second utility calculating unit, configured to obtain a second utility through a second analysis model for analyzing a difference in personal behavior according to the personal attribute parameter of the passenger and the attribute parameter of the vehicle;
and the willingness-to-pay calculation unit is used for obtaining the willingness-to-pay of the passengers according to the first utility and the second utility.
According to a second aspect of the present invention, the first utility calculating unit includes:
the passenger type acquisition unit is used for acquiring the 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 the first utility according to the passenger type corresponding to the passenger and the attribute parameters of the vehicle and the first analysis model.
According to a second aspect of the invention, the personal attribute parameters include the age, school, income, travel frequency and cost source of the passenger.
According to a second aspect of the invention, the vehicle attribute parameters include fare and 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, the processor implementing the passenger willingness-to-pay prediction method according to the first aspect of the present invention when executing the computer program.
In a fourth aspect of the present invention, a storage medium has stored therein executable instructions which when executed by a processor implement the passenger willingness-to-pay prediction method according to the first aspect of the present invention.
The scheme has at least the following beneficial effects: the method comprises the steps that first utility of selecting a train on the premise that passengers belong to a certain passenger type is analyzed according to group behavior heterogeneity of a passenger group through a first analysis model, second utility of selecting the train by the passengers is analyzed according to personal behavior heterogeneity of passenger individuals through a second analysis model, and then the group behavior heterogeneity and the personal behavior heterogeneity are combined, and the willingness to pay of the passengers for the train is obtained according to the first utility and the second utility; and the willingness to pay is considered from a plurality of angles, so that the accuracy of predicting the willingness to pay of the 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.
Drawings
The invention is further described below with reference to the drawings and examples.
FIG. 1 is a flow chart of a passenger willingness-to-pay prediction method according to an embodiment of the present invention;
fig. 2 is a specific 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 embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein the accompanying drawings are used to supplement the description of the written description so that one can intuitively and intuitively understand each technical feature and overall technical scheme of the present invention, but not to limit the scope of the present invention.
In the description of the present invention, it should be understood that references to orientation descriptions such as upper, lower, front, rear, left, right, etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of description of the present invention and to simplify the description, and do not indicate or imply that the apparatus or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present invention.
In the description of the present invention, a number means one or more, a number means two or more, and greater than, less than, exceeding, etc. are understood to not include the present number, and above, below, within, etc. are understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless explicitly defined otherwise, terms such as arrangement, installation, connection, etc. should be construed broadly and the specific meaning of the terms in the present invention can be reasonably determined by a person skilled in the art in combination with the specific contents of the technical scheme.
Referring to fig. 1, an embodiment of the first aspect of the present invention provides a passenger willingness-to-pay prediction method.
The passenger willingness to pay prediction method comprises the following steps:
step S100, acquiring personal attribute parameters and vehicle attribute parameters of passengers;
step 200, obtaining a first utility through a first analysis model for analyzing group behavior heterogeneity according to personal attribute parameters of passengers and attribute parameters of vehicles;
step S300, obtaining a second utility through a second analysis model for analyzing the personal behavior difference according to the personal attribute parameters of the passengers and the attribute parameters of the vehicles;
and step 400, obtaining the willingness to pay of the passengers according to the first utility and the second utility.
For step S100, personal attribute parameters and vehicle attribute parameters of the passenger are acquired, wherein. Personal attribute parameters include the age, school, income, travel frequency, and cost source of the passenger. The vehicle attribute parameters include fare and runtime of the vehicle. Wherein, age i Indicates the age of the ith passenger, edu i The academy, inc representing the ith passenger i Indicating the income, fre, of the ith passenger i Represents the travel frequency of the ith passenger rei i Representing the cost source of the ith passenger, c j Representing ticket price of jth train, t j Indicating the run time of the jth train.
It should be noted that the personal attribute parameters may also include other parameters, such as gender, physical health condition, and the like. The vehicle attribute parameters may also include others, such as quality of service ratings, etc.
It should be noted that, collection of personal attribute parameters of passengers needs to obtain personal consent, and a secret form is adopted to avoid collection and commercial utilization of public personal information without knowledge.
Referring to fig. 2, certain embodiments of the first aspect of the present invention, for step S200, deriving a first utility from a first analysis model for analyzing group behavior heterogeneity based on personal attribute parameters of passengers and vehicle attribute parameters, comprising:
step S210, obtaining a passenger type corresponding to the passenger according to the personal attribute parameters of the passenger;
step 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 explain group behavior heterogeneity, and the objective is to divide travelers into several potential categories, and estimate parameters corresponding to different categories respectively, so as to analyze the heterogeneity between the categories. The individual passengers are denoted by i, j denotes the alternative trains, j e {1,2, L, J }. s denotes the passenger type, s.epsilon.1, 2, L, S.
The utility function of passenger i belonging to the s-th type is expressed as follows:
wherein V is i (s) is the determined first utility, ε i (s) random term for the effect, < ->Are weight parameters related to the age, school, income, travel frequency and cost source of the passenger, respectively.
Then the probability that passenger i belongs to the s-th category is:
the first utility is represented by a utility function of passenger i selecting the jth train if it belongs to the jth type, and the utility function of passenger i selecting the jth train if it belongs to the jth type is as follows: u (U) i (j/s)＝V i (j/s)+ε i (j/s)＝β 1/s c j +β 2/s t j +ε i (j/s); wherein beta is 1/s And beta 2/s Epsilon is the weight parameter i (j/s) is a random term of the utility.
In practice, it can be obtained that the probability of passenger i selecting the jth train on the premise of belonging to the jth type is:
in practice, passengers are classified into two types, an economical type passenger and a business type passenger, respectively. Wherein for an economical passenger the fare sensitivity is greater than the run time sensitivity. For business type passengers, fare sensitivity is less than run-time sensitivity.
For step S300, a second utility is obtained from the personal attribute parameters of the passenger and the vehicle attribute parameters by a second analysis model for analyzing the personal behavior variability. In this embodiment, for the second analysis model, there is a change in the fare and running time of the train as the train j changes, and the age, academic, monthly income, trip frequency and cost sources of the passengers are relatively fixed.
The fare and running time parameters of the train in the trip selection utility function are random parameters, and the utility function of the j-th train selected by the passenger i is expressed as follows: v (V) i (j)＝(β 1 +α 1 v c )c j +(β 2 +α 2 v t )t j The method comprises the steps of carrying out a first treatment on the surface of the Wherein beta is 1 +α 1 v c Is a random parameter of fare beta 2 +α 2 v t Random parameters of running time are in accordance with normal distribution; beta 1 、β 2 Is the mean value of the random parameter,α 1 、α 2 standard deviation of the random parameters; v c 、v t Is a random variable, subject to a standard normal distribution.
The probability of passenger i selecting the jth train is:
to analyze the influence of the passenger personal attribute on the random parameter, the passenger personal attribute is considered in the random parameter setting. Then, considering the personal attributes of the passengers, the observable portion of the utility function for passenger i to select the jth train is represented as follows: v (V) i (j)＝(β 1 +δ 1 z i +α 1 v c )c j +(β 2 +δ 2 z i +α 2 v t )t j ；In delta k Vector, z representing parameter composition corresponding to personal attribute i Vector representing the composition of personal attribute variables, +.> Is a parameter.
For step S400, a passenger' S willingness to pay is obtained from the first utility and the second utility. The willingness to pay of the passenger is expressed as follows:wherein t is train running time, c is train fare, V is travel selection utility of passengers, and V is the sum of the first utility and the second utility.
For 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 for group behavior heterogeneity of a passenger group through a first analysis model, a second utility of selecting the train for the passenger is analyzed for personal behavior heterogeneity of a passenger person through a second analysis model, and then the group behavior heterogeneity and the personal behavior heterogeneity are combined, and the willingness-to-pay of the passenger for the train is obtained according to the first utility and the second utility; and the willingness to pay is considered from a plurality of angles, so that the accuracy of predicting the willingness to pay of the passengers is improved.
Referring to fig. 3, an embodiment of a second aspect of the present invention provides a passenger willingness-to-pay prediction apparatus.
The passenger willingness-to-pay prediction apparatus includes:
a parameter acquisition unit 10 for acquiring personal attribute parameters of passengers and vehicle attribute parameters;
a first utility calculating unit 20 for obtaining a first utility through a first analysis model for analyzing the heterogeneity of group behaviors according to the personal attribute parameter and the vehicle attribute parameter of the passenger;
a second utility calculating unit 30 for obtaining a second utility from the personal attribute parameter of the passenger and the vehicle attribute parameter by a second analysis model for analyzing the personal behavior variability;
and a willingness-to-pay calculation unit 40 for obtaining the willingness-to-pay of the passenger according to the first utility and the second utility.
In certain embodiments of the second aspect of the present invention, the first utility calculating unit 20 includes:
the passenger type acquisition unit is used for acquiring the 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 the first utility according to the passenger type and the vehicle attribute parameters corresponding to the passenger and the first analysis model.
In particular, personal attribute parameters include the age, school, income, travel frequency, and cost source of the passenger. The vehicle attribute parameters include fare and runtime of the vehicle.
In this embodiment, a 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, a second utility of selecting a train by a passenger is analyzed for personal behavior heterogeneity of a passenger individual by the second utility calculating unit 30 using a second analysis model, and then a willingness-to-pay of the passenger to the train is obtained by the willingness-to-pay calculating unit 40 according to the first utility and the second utility in combination with the group behavior heterogeneity and the personal behavior heterogeneity; and the willingness to pay is considered from a plurality of angles, so that the accuracy of predicting the willingness to pay of the passengers is improved.
It should be noted that, the willingness-to-pay prediction device provided in the second aspect of the present invention adopts the willingness-to-pay prediction device method provided in the first aspect of the present invention, which has the same technical scheme, solves the same technical problems, and has the same technical effects, and is not described in detail herein.
An embodiment of a third aspect of the present invention provides a passenger willingness-to-pay prediction apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing 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, processor, and communication interface are implemented independently, the memory, processor, and communication interface may be interconnected and communicate with each other via a bus. The bus may be an industry standard architecture bus, an external device interconnect bus, or an extended industry standard architecture bus, among others. The buses may be divided into address buses, data buses, control buses, etc.
Alternatively, 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 communicate with each other through the internal interface.
An embodiment of the fourth aspect of the present invention is a storage medium having stored therein executable instructions which when executed by a processor implement the passenger willingness-to-pay prediction method according to the first aspect of the present invention.
The computer readable medium of the embodiments of the present invention may be a computer readable signal medium or a computer readable storage medium or any combination of the two. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include at least 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). In addition, the computer-readable storage medium may even be paper or other suitable medium upon which the program is printed, as the program may 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 include a data signal propagated in baseband or as part of a carrier wave, with computer readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also 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 is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
The present invention is not limited to the above embodiments, but is merely preferred embodiments of the present invention, and the present invention should be construed as being limited to the above embodiments as long as the technical effects of the present invention are achieved by the same means.
Claims (9)
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 according to the personal attribute parameters of the passengers and the attribute parameters of the vehicles;
obtaining a second utility through a second analysis model for analyzing the personal behavior differences according to the personal attribute parameters of the passengers and the attribute parameters of the vehicles;
and obtaining a passenger willingness to pay according to the first utility and the second utility, wherein the passenger willingness to pay is expressed as follows:wherein t is train running time, c is train fare, V is travel selection utility of passengers, and is the sum of the first utility and the second utility;
wherein the obtaining, according to the personal attribute parameter of the passenger and the attribute parameter of the vehicle, the first utility through a first analysis model for analyzing the heterogeneity of group behaviors includes:
obtaining the passenger type corresponding to the passenger according to the personal attribute parameters of the passenger;
obtaining the first utility according to the passenger type corresponding to the passenger and the attribute parameters of the vehicle and the first analysis model;
passenger types include economy type passengers and business type passengers; the fare sensitivity of the economical passenger is greater than the run time sensitivity; the fare sensitivity of the business type passenger is less than the run-time sensitivity.
2. The method of claim 1, wherein the personal attribute parameters include the passenger's age, academy, income, travel frequency, and cost source.
3. A method of predicting willingness-to-pay for a passenger as defined in claim 1, wherein the vehicle attribute parameters include a fare and a run time of the vehicle.
4. A passenger willingness-to-pay prediction apparatus, comprising:
a parameter acquisition unit for acquiring personal attribute parameters of passengers and attribute parameters of vehicles;
the first utility calculating unit is used for obtaining first utility through a first analysis model for analyzing group behavior heterogeneity according to the personal attribute parameters of the passengers and the attribute parameters of the vehicles;
a second utility calculating unit, configured to obtain a second utility through a second analysis model for analyzing a difference in personal behavior according to the personal attribute parameter of the passenger and the attribute parameter of the vehicle;
and the willingness-to-pay calculation unit is used for obtaining the willingness-to-pay of the passengers according to the first utility and the second utility, wherein the willingness-to-pay of the passengers is expressed as follows:wherein t is train running time, c is train fare, V is travel selection utility of passengers, and V is the sum of the first utility and the second utility;
wherein the obtaining, according to the personal attribute parameter of the passenger and the attribute parameter of the vehicle, the first utility through a first analysis model for analyzing the heterogeneity of group behaviors includes:
obtaining the passenger type corresponding to the passenger according to the personal attribute parameters of the passenger;
obtaining the first utility according to the passenger type corresponding to the passenger and the attribute parameters of the vehicle and the first analysis model;
passenger types include economy type passengers and business type passengers; the fare sensitivity of the economical passenger is greater than the run time sensitivity; the fare sensitivity of the business type passenger is less than the run-time sensitivity.
5. The passenger willingness-to-pay prediction apparatus of claim 4, wherein the first utility calculation unit comprises:
the passenger type acquisition unit is used for acquiring the 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 the first utility according to the passenger type corresponding to the passenger and the attribute parameters of the vehicle and the first analysis model.
6. The passenger willingness-to-pay prediction apparatus of claim 4, wherein the personal attribute parameters comprise age, academy, income, travel frequency, and cost source of the passenger.
7. The passenger willingness-to-pay prediction apparatus of claim 4, wherein the vehicle attribute parameters comprise a fare and a run time of the vehicle.
8. A passenger willingness-to-pay prediction apparatus comprising 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 of any one of claims 1 to 3 when executing the computer program.
9. A storage medium having stored therein executable instructions which when executed by a processor implement the passenger willingness-to-pay prediction method of any one of claims 1 to 3.
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| US20150142482A1 (en) * | 2013-11-18 | 2015-05-21 | Amadeus S.A.S. | Search engine for identifying business travel proposals |
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| EP2874109A1 (en) * | 2013-11-18 | 2015-05-20 | Amadeus S.A.S. | Search engine for identifying business travel proposals |
| CN107368915A (en) * | 2017-06-16 | 2017-11-21 | 北京交通大学 | A kind of Metro Passenger travel time housing choice behavior analysis method |
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