CN107506869A - A kind of air ticket day booking number Forecasting Methodology based on Recognition with Recurrent Neural Network - Google Patents
A kind of air ticket day booking number Forecasting Methodology based on Recognition with Recurrent Neural Network Download PDFInfo
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Abstract
The present invention relates to number Forecasting Methodology of making a reservation a kind of air ticket day based on Recognition with Recurrent Neural Network.Air ticket day booking number Forecasting Methodology of the present invention based on Recognition with Recurrent Neural Network, using Recognition with Recurrent Neural Network modeling aviation booking data sequence information, applied to aviation booking data prediction；Compared with prior art, total ticket booking number when all flights in certain course line finally take off can not only be predicted, the air ticket quantity that each advance ticket date sells before flight takeoff can also be predicted, and then the ticket price on each presell date is instructed to formulate, to reach the purpose for making airline's maximum revenue, there is important practical application meaning.
Description
Technical field
The present invention relates to number Forecasting Methodology of making a reservation a kind of air ticket day based on Recognition with Recurrent Neural Network, belong to Civil Aviation IT
Technical field.
Background technology
The target of yield management be by commodity in the suitable time with suitable price sales to suitable customer, to reach
The final goal of maximum revenue.As the key technology of airline revenue mangement, course line passenger flow forecast is that airline implements
The basis of the operations such as Dynamic Pricing, the control of seat storage, is one of important process of airline's development plan.Accurate passenger flow
Amount prediction can provide irreplaceable decision support for the development of airline.
The current existing research on course line passenger flow forecast, it is to utilize mathematics according to flight history booking data mostly
Final volume of the flow of passengers when model is to predict the flight takeoff.In the actual mechanical process of airline, it is final to predict flight
Although total ticketing number for instruct vehicle supervision department ahead of time planned and managed and optimize aeronautical resources configure have
Important function, but for instructing having little significance for the air ticket Dynamic Pricing of the flight presell interim every day.And if can be accurate
The air ticket quantity that the flight in the course line is sold interim every day in presell is really predicted, then to instructing every day flight presell phase
Air ticket pricing it is significant.Airline formulates air ticket Dynamic Pricing plan based on the daily booking number predicted
Slightly, to reach the purpose of maximum gain.
Artificial neural network is the operational model being interconnected to constitute by a large amount of neurons, in pattern-recognition, intelligent machine
Device people, the field such as automatically control and successfully solve the insoluble practical problem of many modern computers.Recognition with Recurrent Neural Network
(RNN) it is a kind of artificial neural network of particular design, the difference of itself and conventional feed forward neutral net is, its hidden layer
Directed circulation is introduced, the information transmission of last moment to current time can be handled front and rear pass between those inputs
The problem of connection.
The content of the invention
In view of the shortcomings of the prior art, the present invention provides a kind of air ticket day booking number prediction side based on Recognition with Recurrent Neural Network
Method.
The technical scheme is that：
A kind of air ticket day booking number Forecasting Methodology based on Recognition with Recurrent Neural Network, including step are as follows：
1) initial data is pre-processed；Statistics belongs to the flight in same course line, total freight space number in every course line and every
Total booking number in bar course line；
Wherein, every course line has n+1 bars to make a reservation data, and n+1 bars booking data correspond to before flight takeoff n days to flight
Take off the same day every day course line booking number；Flight therein is all flights that a certain course line includes.
The presell phase of usual flight be from n days before the flight takeoff to the flight takeoff on the day of, therefore for each boat
Class, we have n+1 bars booking data；What we to be predicted is the booking data in certain course line, rather than some specific flight
Booking data, it is therefore desirable to data are pre-processed, the data message of all flights in the course line in every day is summed,
Obtain the total data in the course line.
2) it is first, n+1 groups according to the date difference between advance ticket date and date by total data, numbering i=0
~n；N+1 groups data correspond in the different ticketing dates total freight space number in every course line and total booking number in every course line respectively；
To each group of data, the data of continuous seven dates are taken, wherein the data of the first six date are as god
Input through network, the output of the data of the 7th date as neutral net；The form of the input of neutral net is 6 rows
The matrix of 4 row, every data line of matrix represent the historical data of date；4 column datas of matrix refer respectively to the group
Whether the numbering i of data, the date are the ticketing sum of i days, the cabin of the 7th day to be predicted before weekend, the date
Position sum；Wherein, group # i and the freight space sum of the 7th day to be predicted are that 6 rows share；The form of the output of neutral net
For for a scalar value；The scalar value of neutral net output represents the course line of the 7th day to be predicted and worked as when taking off i days a few days ago
It ticketing number.It is identical that so-called 6 row shares i.e. 6 row data.
3) above operation is carried out successively to whole n+1 groups data, obtains whole training data samples；
4) test sample is chosen, inputs in the model trained, obtains prediction result.
According to currently preferred, the n=17.
Beneficial effects of the present invention are：
1. the air ticket day booking number Forecasting Methodology of the present invention based on Recognition with Recurrent Neural Network, is built using Recognition with Recurrent Neural Network
Mould aviation booking data sequence information, applied to aviation booking data prediction；Compared with prior art, certain boat can not only be predicted
Total ticket booking number when all flights of line finally take off, moreover it is possible to the air ticket quantity that each advance ticket date sells before flight takeoff
It is predicted, and then instructs the ticket price on each presell date to formulate, to reach the purpose for making airline's maximum revenue,
With important practical application meaning；
2.RNN introduces directed circulation in hidden layer, can locate the information transmission of last moment to current time
Manage those input between forward-backward correlation the problem of this property, make its be particularly suitable for handle sequence information；And certain course line is gone through
The certain sequential relationship of history ticket booking data fit, we can learn the sequential of course line ticket booking data using Recognition with Recurrent Neural Network
Rule, the purpose that current ticket booking number is predicted according to historical data is realized well.
Brief description of the drawings
Fig. 1 is number Forecasting Methodology flow chart of making a reservation the air ticket day of the present invention based on Recognition with Recurrent Neural Network；
Fig. 2 is the illustraton of model of neutral net of the present invention.
Embodiment
With reference to embodiment and Figure of description, the present invention will be further described, but not limited to this.
Embodiment 1
As shown in Figure 1-2.
A kind of air ticket day booking number Forecasting Methodology based on Recognition with Recurrent Neural Network, including step are as follows：
1) initial data is pre-processed；Statistics belongs to the flight in same course line, total freight space number in every course line and every
Total booking number in bar course line；
Wherein, every course line has n+1 bars to make a reservation data, and n+1 bars booking data correspond to before flight takeoff n days to flight
Take off the same day every day course line booking number；Flight therein is all flights that a certain course line includes.
The presell phase of usual flight be from n days before the flight takeoff to the flight takeoff on the day of, therefore for each boat
Class, we have n+1 bars booking data；What we to be predicted is the booking data in certain course line, rather than some specific flight
Booking data, it is therefore desirable to data are pre-processed, the data message of all flights in the course line in every day is summed,
Obtain the total data in the course line.
2) it is first, n+1 groups according to the date difference between advance ticket date and date by total data, numbering i=0
~n；N+1 groups data correspond in the different ticketing dates total freight space number in every course line and total booking number in every course line respectively；
To each group of data, the data of continuous seven dates are taken, wherein the data of the first six date are as god
Input through network, the output of the data of the 7th date as neutral net；The form of the input of neutral net is 6 rows
The matrix of 4 row, every data line of matrix represent the historical data of date；4 column datas of matrix refer respectively to the group
Whether the numbering i of data, the date are the ticketing sum of i days, the cabin of the 7th day to be predicted before weekend, the date
Position sum；Wherein, group # i and the freight space sum of the 7th day to be predicted are that 6 rows share；The form of the output of neutral net
For for a scalar value；The scalar value of neutral net output represents the course line of the 7th day to be predicted and worked as when taking off i days a few days ago
It ticketing number.It is identical that so-called 6 row shares i.e. 6 row data.
3) above operation is carried out successively to whole n+1 groups data, obtains whole training data samples；
4) test sample is chosen, inputs in the model trained, obtains prediction result.
Wherein, n=17.
Claims (2)
1. a kind of air ticket day booking number Forecasting Methodology based on Recognition with Recurrent Neural Network, it is characterised in that as follows including step：
1) initial data is pre-processed；Statistics belongs to the flight in same course line, total freight space number in every course line and every boat
Total booking number of line；
Wherein, every course line has n+1 bars to make a reservation data, and n+1 bars booking data correspond to before flight takeoff n days to flight takeoff
Every day on the same day course line booking number；
2) it is first, n+1 groups according to the date difference between advance ticket date and date by total data, numbering i=0~n；
N+1 groups data correspond in the different ticketing dates total freight space number in every course line and total booking number in every course line respectively；
To each group of data, the data of continuous seven dates are taken, wherein the data of the first six date are as nerve net
The input of network, the output of the data of the 7th date as neutral net；The form of the input of neutral net is that 6 rows 4 arrange
Matrix, every data line of matrix represents the historical data of date；4 column datas of matrix refer respectively to this group of data
Numbering i, the ticketing the sum whether date is i days before weekend, the date, the freight space of the 7th day to be predicted it is total
Number；Wherein, group # i and the freight space sum of the 7th day to be predicted are that 6 rows share；The form of the output of neutral net be for
One scalar value；
3) above operation is carried out successively to whole n+1 groups data, obtains whole training data samples；
4) test sample is chosen, inputs in the model trained, obtains prediction result.
2. the air ticket day booking number Forecasting Methodology according to claim 1 based on Recognition with Recurrent Neural Network, it is characterised in that institute
State n=17.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109858671A (en) * | 2018-12-26 | 2019-06-07 | 携程旅游网络技术（上海）有限公司 | Predict the method and system of the visiting rate of target pattern |
| CN110516873A (en) * | 2019-08-28 | 2019-11-29 | 贵州优策网络科技有限公司 | A kind of airline's Slot Allocation optimization method |
| CN111192090A (en) * | 2019-12-31 | 2020-05-22 | 广州优策科技有限公司 | Seat allocation method and device for flight, storage medium and electronic equipment |
| CN111899059A (en) * | 2020-08-12 | 2020-11-06 | 科技谷（厦门）信息技术有限公司 | Navigation driver revenue management dynamic pricing method based on block chain |
| CN112308618A (en) * | 2020-11-02 | 2021-02-02 | 沈阳民航东北凯亚有限公司 | Data processing method and device, electronic equipment and storage medium |
| CN112396243A (en) * | 2020-11-30 | 2021-02-23 | 中国民航信息网络股份有限公司 | Flight booking value processing method and system based on addition model |
| CN112907158A (en) * | 2021-05-10 | 2021-06-04 | 北京人人云图信息技术有限公司 | Flight passenger flow generation method and system based on game learning |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109858671A (en) * | 2018-12-26 | 2019-06-07 | 携程旅游网络技术（上海）有限公司 | Predict the method and system of the visiting rate of target pattern |
| CN109858671B (en) * | 2018-12-26 | 2021-06-18 | 携程旅游网络技术（上海）有限公司 | Method and system for predicting passenger seat rate of target airline |
| CN110516873A (en) * | 2019-08-28 | 2019-11-29 | 贵州优策网络科技有限公司 | A kind of airline's Slot Allocation optimization method |
| CN111192090A (en) * | 2019-12-31 | 2020-05-22 | 广州优策科技有限公司 | Seat allocation method and device for flight, storage medium and electronic equipment |
| CN111899059A (en) * | 2020-08-12 | 2020-11-06 | 科技谷（厦门）信息技术有限公司 | Navigation driver revenue management dynamic pricing method based on block chain |
| CN112308618A (en) * | 2020-11-02 | 2021-02-02 | 沈阳民航东北凯亚有限公司 | Data processing method and device, electronic equipment and storage medium |
| CN112396243A (en) * | 2020-11-30 | 2021-02-23 | 中国民航信息网络股份有限公司 | Flight booking value processing method and system based on addition model |
| CN112907158A (en) * | 2021-05-10 | 2021-06-04 | 北京人人云图信息技术有限公司 | Flight passenger flow generation method and system based on game learning |
| CN112907158B (en) * | 2021-05-10 | 2021-07-23 | 北京人人云图信息技术有限公司 | Flight passenger flow generation method and system based on game learning |
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