EP3080764A1 - Procédé et serveur pour fournir un ensemble d'estimations de prix, telles que des estimations de tarif aérien - Google Patents

Procédé et serveur pour fournir un ensemble d'estimations de prix, telles que des estimations de tarif aérien

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Publication number
EP3080764A1
EP3080764A1 EP13810989.7A EP13810989A EP3080764A1 EP 3080764 A1 EP3080764 A1 EP 3080764A1 EP 13810989 A EP13810989 A EP 13810989A EP 3080764 A1 EP3080764 A1 EP 3080764A1
Authority
EP
European Patent Office
Prior art keywords
price
fare
prices
estimates
server
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
EP13810989.7A
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German (de)
English (en)
Inventor
Gareth Williams
Joe SARRE
Andrej SNIEZYK
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Skyscanner Ltd
Original Assignee
Skyscanner Ltd
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Filing date
Publication date
Application filed by Skyscanner Ltd filed Critical Skyscanner Ltd
Publication of EP3080764A1 publication Critical patent/EP3080764A1/fr
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0283—Price estimation or determination

Definitions

  • the field of the invention relates to methods, servers and computer program products for providing a set of price estimates, such as estimates for prices for air fares, train fares, hotel prices, goods— in fact any type of goods or services whose prices are not fixed but instead are variable and it is therefore useful to be able to provide estimates for those prices.
  • the price of an airfare ticket for an itinerary is complex to compute— it is the result of calculations of possible routings, qualifying fares, and the availability of those fares. These calculations are typically performed in a Global Distribution System (GDS), and running a query against such a system can be slow, costs money and requires the use of energy to perform the calculations and to transmit the results.
  • GDS Global Distribution System
  • FR2841018(B1) discloses a method for loading flight schedule modifications comprising the steps of: receiving at least one set of flight modifications; retrieval of individual modifications within the set and storage within a register for future scheduling; simulating reassignment of the reservations concerned by the flight schedule modification by accessing, via the reservation distribution server, both the records and flight schedule databases; and final updating of the flight schedule databases of the reservation inventory.
  • flight pricing is conventionally obtained from a GDS (see Section C for a more detailed explanation of how GDS systems work, and more generally about how flight pricing works).
  • Flight comparison services such as Skyscanner, and some airlines, pay a fee to obtain live, bookable prices from a GDS; these prices are the actual bookable prices that a potential passenger can book.
  • many potential passengers are often just browsing and do not need definitively accurate bookable prices, but instead would be satisfied with an estimate. Consequently, it is very useful if an accurate price estimating method and system could be devised, especially if it removes the dependency on accessing third party resources, like a GDS, which not only cost money to access, but may not be available to provide the required information.
  • a method of providing price estimates including the steps of:
  • step (ii) configuring one or more processors to determine estimated prices from an incomplete historical price dataset by analysing patterns in that dataset, at any time with respect to step (i) above;
  • step (iv) providing the price estimates to an end-user computing device, such as a personal computer, smartphone or tablet.
  • an end-user computing device such as a personal computer, smartphone or tablet.
  • the method does not require the time or energy required to access one or more remote servers many times to actually establish fare availability and to calculate fare prices.
  • the method does not require the enormous data storage capacity needed to store results in advance for all possible queries.
  • a computer carrying out the method operates in a new way, and the perceived problems are overcome by the method. Note that steps (ii) and (iii) in this method are not necessarily separate steps but may instead be part of the same step.
  • the method may be one wherein the parameters defining those goods or services include one or more of the following: activity type, such as airfare, hotel booking, train fare; date range; destination; origin; desired weather conditions; star ratings; keywords; any other user defined preference;
  • the method may be one wherein determination of estimated prices is performed by inferring, deriving or predicting estimated prices.
  • step (ii) comprises:
  • step (iii) comprises: calculating a set of estimates for the requested price over the specified date range using statistics from the stored classifiers corresponding to the identified groups.
  • the method may be one, wherein step (ii) includes using rules in order to analyse patterns in the dataset.
  • step (ii) includes a naive Bayes classifier machine learning approach which produces a probabilistic model of prices, and that model is used to predict unobserved prices.
  • the method may be one wherein classifiers are trained using observed prices and sets of features that correspond to them.
  • the method may be one wherein the features relate to the request, and include one or more of: departure day of week, length of stay, does trip involve spending Saturday night at the destination?, airline, time to travel, route, month.
  • the method may be one wherein a classifier then predicts the price of an unobserved price by being given a set of features and providing a most likely price to have those features.
  • the method may be one wherein features may be derived by training multiple models with different features and comparing the predictive accuracy of the different models.
  • step (ii) includes building a statistical model from historical prices, identifying missing quote candidates, and pricing quote candidates based on the statistical model.
  • the method may be one wherein estimating prices for each candidate quote occurs in the following steps: extracting category feature values from the quote; retrieving from a database a classifier trained for an extracted category; extracting all feature values from the quote candidate; classifying the candidate quote by calculating Bayes posterior probabilities for each price range stored in a classifier and choosing a price range class with a highest Bayes posterior probability, and attaching a price class to a candidate quote.
  • the method may be one wherein inputs for the statistical model include: list of routes, classifier categorization scheme, historical quotes, and a set of supported features with weights.
  • the method may be one wherein historical quotes are filtered by age.
  • the method may be one wherein inputs for the statistical model include reversed route equivalents.
  • the method may be one including the step of: including cached fare prices in the set of price estimates.
  • the method may be one wherein the set of price estimates is constructed without querying a Distribution System after step (i) .
  • the method may be one wherein the price is for a journey that is a one-way journey.
  • the method may be one wherein the price is for a journey that is a return journey.
  • the method may be one wherein the price includes an air fare price.
  • the method may be one wherein the price includes a train fare price.
  • the method may be one wherein the price includes a car hire price.
  • the method may be one wherein the price includes a hotel price.
  • the method may be one wherein the request comprises a flexible search request.
  • the method may be one wherein a final result of the method is up to one quote per input date or dates pair.
  • the method may be one including the steps of
  • step (A) configuring the one or more processors to determine confidence ranges of the estimated prices from an incomplete historical price dataset by analysing patterns in that dataset, at any time with respect to step (i), and
  • the method may be one further comprising the step of:
  • (C) providing the confidence ranges together with the fare price estimates to an end-user computing device, such as a personal computer, smartphone or tablet.
  • the method may be one further comprising the step of:
  • the method may be one wherein the probable range of prices are displayed as error bars.
  • the method may be one wherein a date range includes only one outward date.
  • the method may be one wherein a date range includes only one return date.
  • the method may be one including a method of providing a set of best return fare price estimates for a specified range of outward dates and a specified range of return dates, wherein:
  • step (i) includes receiving a request for a return fare price for a specified range of outward dates and a specified range of return dates for a journey from a starting location to a destination location, and step (iv) includes providing the set of best return fare price estimates for the specified range of outward dates and for the specified range of return dates.
  • the method may be one wherein the set of best return fare price estimates are provided in a bar chart format.
  • the method may be one wherein an estimation process is parameterized by one or more of: a minimum Bayes posterior probability required to accept a classification result; a maximum number of route operators involved in candidates' generation, or a random variation added to Bayes posterior probability to avoid ties.
  • the method may be one wherein the method is performed on a server from which estimation services for fare availability and price are provided.
  • a server configured to provide price estimates, the server arranged to:
  • the server may be one wherein for (ii) the server is arranged to:
  • the server may be one wherein for (iii) the server is arranged to: calculate a set of estimates for the requested price over the specified date range using statistics from the stored classifiers corresponding to the identified groups.
  • the server may be one further arranged to perform a method of any method according to a first aspect of the invention.
  • a computer program product embodied on a non-transient storage medium, the computer program product when running on a computer arranged to provide price estimates, the computer program product when running on a computer arranged to:
  • the computer program product may be arranged to perform a method of any of method according to a first aspect of the invention.
  • a computer terminal in connection with a server of any server according to the second aspect of the invention, the computer terminal arranged to send a request for a price estimate for a specified date range for a journey from a starting location to a destination location, wherein the computer terminal is further arranged to receive price estimates.
  • an item or a service such as an airline ticket
  • a price estimate for that item or service is provided using a method according to the first aspect of the invention or a server according to the second aspect of the invention.
  • a web-based price comparison web site with which end-users can interact by providing a request for a price for goods or services, and that web-based price comparison web site provides that request to a server that estimates the price(s) using the method of any method according to the first aspect of the invention.
  • Figure 1 shows an example of a system for estimating prices from other prices, with the purpose of using those prices to augment a cache of observed fares.
  • Figure 2 shows an example of how a 'difference' engine can be used to derive the availability of fare classes from a live update.
  • Figure 3 shows examples of equations suitable for use in Na ' ive Bayes classification.
  • Figure 4 shows an example in which feature values are represented by numbered areas and price ranges (buckets) represent frequency information.
  • Figure 5 shows an example of a full training process.
  • Figure 6 shows an example in which Na ' ive Bayes classifiers can be used to price each candidate quote placeholder.
  • Figure 7 shows an example of a full process of inferring missing quotes.
  • Figure 8 shows an example of connections between a server providing estimation services for fare availability and price, and input data sources, and output to partners and to users.
  • Figure 9 shows an example of a user interface.
  • Figure 10 shows an example of a user interface.
  • Figure 11 shows an example of a system for performing a search process.
  • Figure 12 shows an example of a user interface.
  • Figure 13 shows an example of user interface output.
  • Figure 14 shows an example of user interface output.
  • Figure 15 shows an example of an architecture for browse searches.
  • Figure 16 shows an example of an overall system architecture for a system which includes provision of estimation services for fare availability and price.
  • Figure 17 shows an example GDS configuration.
  • Figure 18 shows an example of fares regarding a fare class record, fare categories, and category data tables.
  • Figure 19 shows an example of a process which provides fare pricing options.
  • Figure 20 shows an example of a relationship between a pricing engine, AVS/NAVS,
  • Figure 21 shows an example system including an Availability Server.
  • Figure 22 shows an example a graphical indication of a confidence range for estimated prices using a bar, which spans a price range from the lowest price to the highest price of a range in which the price is to be found.
  • the bar is provided for a range of outward journey dates and for a range of return journey dates.
  • the price of a ticket for an itinerary is complex to compute — it is the result of calculations of possible routings, qualifying fares, and the availability of those fares. These calculations are typically performed in a Global Distribution System (GDS), and running a query against such a system can be slow and also costs money.
  • GDS Global Distribution System
  • the prices, returned by the GDS are the result of applying fare rules and availability.
  • the estimator uses statistics derived from the quotes in order to predict other quotes.
  • a very simple estimator could compute that all missing fares are equal to the average of all observed fares.
  • the level of prediction can be improved by making the model more sophisticated, in order to take into account the type of rules used to generate the fares in the first place. For example, a common 'rule' that defines whether a fare is valid for an itinerary is whether the itinerary involves staying on a Saturday night. Typically such rules mean that dates that involve not spending Saturday night at the destination are much more expensive— the rule is designed to penalize business travellers who usually desire to only be away during the work week.
  • a statistical model may be provided for predicting fares, using statistics and rules which may have been derived by a human, for example.
  • An alternative method of producing the statistical model is also possible— this approach uses a machine learning approach called a 'Naive Bayes classifier' to produce a probabilistic model of fares, and that model is used to predict the unobserved fares.
  • the classifier is trained using observed prices and sets of 'features' that correspond to them.
  • the features relate to the query that generated the fares, and could include: departure day of week, length of stay, stay Saturday, airline, etc.
  • the classifier then predicts the price of an unobserved fare by being given a set of features and providing the most likely price to have those features.
  • the statistical model for predicting prices can then be consulted in order to predict previously unobserved fares, or observed fares in the event of wanting to measure the accuracy of the model.
  • the model may be used in the flight industry in order to estimate previously unobserved prices, in order to display them to users, or to determine whether it is worth querying to return the price e.g. is it likely to be good value.
  • the model may be used in other fields where there is a disadvantage to obtaining a price (e.g. cost of querying), but where the price was generated by structured means (e.g. pricing rules). This includes travel products such as hotels, trains, and car hire cars; as well as other products with structured pricing, such as insurance.
  • a statistical model is a set of Naive Bayes classifiers trained periodically (e.g. daily) with historical prices. Missing quotes are inferred based on route operators' data and priced using classifiers.
  • Example of a Statistical model Naive Bayes classification is based on dependency between the probability that an object described by a given set of features belongs to a given class and the probability that an object from this class is characterized by a given set of features: see Eqs. (1) and (2) in Figure 3.
  • Eqs. (1) and (2) can be simplified to Eq. (3) in Figure 3.
  • C is a class to which an input object can be classified (eg. in our case: price range e.g. 75-125 EUR)
  • F is a feature value (eg. in our case: Friday, Air France airline website etc.) Classification can be expressed using Eq. (4) in Figure 3.
  • An intention of classifier training is to calculate how frequently feature values occur for observed price ranges.
  • feature values are represented by numbered areas and price ranges (buckets) represent frequency information (e.g. feature values quantity in range 75-125 EUR, 126-175 EUR etc.).
  • a category defines a scope of a given classifier and is expressed by a set of feature values.
  • An experimentally chosen category consists of a city-level route and airline (example: London— Paris, Air France), but other options are possible (e.g. city-level route without airline).
  • a classifier is trained only with historical quotes matching its category (e.g. only quotes for London— Paris route operated by Air France).
  • a first step of a training process a given number of the most recent historical quotes with a certain maximum age is retrieved from a database. Categories are evaluated for every quote. Quotes are grouped by categories. Each group becomes a training set for one classifier.
  • a training set can be extended using quotes for reversed route equivalents.
  • Weights applied to a number of quotes can differ between features to emphasize a larger influence of some of them (e.g. a trip including a weekday may be more important than stay length).
  • the process ends with one classifier for each training set (e.g. one classifier for a London-Paris route operated by Air France) containing statistical information.
  • a model is stored for future use.
  • a classifier can be trained again e.g. periodically or when a number of historical quotes change.
  • a classifier may only be created if a certain number of quotes matching its category are available.
  • a training process can be parameterized by:
  • a user searches for one-way or return flights for a given route and dates range.
  • a system responds to a query with a set of cheapest quotes from a database: up to one per each date (or dates pair— for return flights). The response may not include quotes for certain dates (date pairs). Missing quotes can be inferred using trained Naive Bayes classifiers. The following elements may be input for an inferring process:
  • Classifiers can only be used to price existing quotes, so candidate quotes must be evaluated first. Candidates are combinations of every applicable airport pairs, date (dates pair), airline and agent available for queried route. A list is generated based on the process inputs and a route operators' database.
  • Na ' ive Bayes classifiers can be used to price each candidate quote placeholder. See Figure 6 for example.
  • Inferring prices may occur for each candidate quote in the following steps:
  • a process can result in multiple priced quote candidates per date (dates pair). For this reason a next step is to choose a single quote for each date (dates pair). A choice can be made based on price e.g. cheapest quote per date (dates pair) is selected. A final result of the process is up to one quote per input date (dates pair). Inferred quotes can be included in a response returned to a user. An example of a full process of inferring missing quotes is shown in Figure 7.
  • an inferring process can be parameterized by: • a minimum Bayes posterior probability required to accept a classification result
  • Estimation methods may be provided which give a confidence to the price, e.g. we are 99% confident it is within $5, $1000, etc. Hence the estimation methods may provide for:
  • a number of methods of estimating a price are capable of returning a measure of confidence for the price.
  • the selected class is the class with the highest probability, but that probability can be used as an indicator of the confidence in the estimate—the closer the probability is to 1 the more confident it is that the predicted price is the correct one.
  • the resulting confidence may be used in a number of ways. If there is a very low confidence in the estimate, the decision may be made:
  • a confidence range for estimated prices spans a price range from the lowest price to the highest price of a range in which the results of an estimation method lead to confidence that the price is to be found.
  • a confidence range for estimated prices may be indicated graphically using a bar.
  • the bar may be displayed for each date in a range of dates.
  • the bar may be displayed for each date in a range of dates for an outward journey and for a return journey.
  • the bar may be colour-coded eg. to indicate that the bar corresponds to recent prices which are likely to be stable relative to the booking function, to price ranges derived from multiple tickets sharing a departure date, and to price ranges estimated from similar itineraries.
  • One bar may be provided for a given date. More than one bar may be provided for a given date. An example is given in Figure 22.
  • the 'scheduled' price model means that two single quotes cannot be used to create a return quote, and therefore a query has to be run for every possible date pair, and the large number of date pairs makes it prohibitive to do this due to the cost of obtaining the data and the space required to store it.
  • the return prices are the result of combining available fares available for the outward and inward legs, along with some rules. Thus if it is possible to know the availability and rules, it is possible to decompose a return query into 'reusable' legs that can be used to construct new 'return' prices, in a similar (but different) manner to that used for airlines with a budget price model.
  • Step 1 may be achieved in routes services. Specifically, it may be achieved by using timetables and rules about what can be sold; given a user query we can identify the valid itineraries that a ticket can be bought for. Airlines submit fares to ATPCO, and ATPCO provide subscriptions to these data, but transforming these raw fares and rules into a system for determining the correct fare for a given query would be a massive effort. Fortunately, ATPCO supply merged fare and rule data in a data feed called FROP (Fare Rule Output Product). FROP data are transmitted in a fixed-length file of records containing fare information and summarized rule conditions for key categories. Therefore Step 2 can be accomplished using the FROP data. It can also be achieved by combining fare rules and prices from airlines, using a pricing engine.
  • FROP Field Rule Output Product
  • ATPCO also provide a feed of itinerary and service fee taxes.
  • a tax and surcharge engine can be built that takes the data in these subscriptions and uses them to calculate the overall price a customer would pay for a given fare, thereby emulating Step 4.
  • Step 3 The only remaining item for low cost calculation of GDS fares is the availability— Step 3. It is possible to derive availability by comparing the result of a live update (the net result of Steps 1-4) with the calculation of Steps 1, 2, and 4 outlined above, i.e. by combining the routes service, fares from FROP and a tax and surcharge engine it is possible to compute all the possible fares, and the observed fares from a live update can be used to infer which of possible fares was used, and therefore which of the computed fares were available.
  • the diagram in Figure 2 shows how such a 'difference' engine can be used to derive the availability of fare classes from a live update.
  • SLC Single Leg Combinable
  • yield management software may continuously alter what fares are available, so cheaper tickets may just disappear as yield management withdraws them to increase yield.
  • a server (which may be real or virtual) from which estimation services for fare availability and price are provided.
  • a related website smartphone apps, business- to-business services, connections with traditional airlines (eg. British Airways, Qantas, KLM etc.), connections with budget airlines (eg. RyanAir, easyjet, germanwings etc.) and travel booking websites (eg. lastminute.com, opodo, Thomson etc.) may be provided. Travel booking websites and airlines may provide input data for the server from which estimation services for fare availability and price are provided.
  • Output may be provided by the server to a public application programming interface (API) e.g. to partner companies.
  • API application programming interface
  • Output may be provided by the server to a website for searching, to a mobile website for searching, and to apps on smartphones, for users.
  • An example connection configuration between the server and input data sources, and output to partners and to users, is shown in Figure 8.
  • a user may specify a start location (which could be an individual airport, a city including a plurality of airports, or a country including a plurality of airports, for example).
  • a user may specify a destination location (which could be an individual airport, a city including a plurality of airports, or a country including a plurality of airports, for example).
  • a user may specify a departure date or a range of departure dates (which could be a date range, or a calendar month, or an unspecified date which could imply a date range such as any date in the coming year, for example).
  • a user may specify a return date or a range of return dates (which could be a date range, or a calendar month, or an unspecified date which could imply a date range such as any date in the coming year, for example).
  • a user may specify a one way ticket or a return ticket.
  • a user may specify the number of passengers in specific age ranges.
  • a user may specify a class of ticket (eg. economy, premium economy, business class or first class, for example).
  • a parallel search for hotels may be performed.
  • a parallel search for car hire may be performed.
  • a preference for direct flights may be indicated.
  • An example user interface is shown in Figure 9.
  • a further example user interface is shown in Figure 12, which is an example of a flexible search, because the departure indicates the airport used in London (UK) may be any, and the destination indicates that any airport in Australia is permitted.
  • Output search results may include a list of flights which satisfy the search criteria.
  • a graphical indicator eg. a slider bar
  • a graphical indicator eg. a slider bar
  • a selectable tab may be provided such that flights may be arranged in order of increasing cost.
  • a selectable tab may be provided such that flights may be arranged in order of increasing travel time.
  • a selectable tab may be provided such that flights may be arranged in order of airline name in alphabetical order.
  • a flight between a start and a destination is requested (which may be a one way or a return flight), for an outbound flight date, and a return flight date if relevant.
  • partner search websites from which to show output are selected.
  • relevant prices that are already cached are identified.
  • relevant prices that are not available are identified and updated.
  • the new data is cleaned up and saved (for example, taxes and fees may be stripped out, and who the real flight operator is may be identified).
  • prices are returned to a user.
  • output data may provide a list of destination airports which satisfy a flexible search criterion (eg. destination is any airport in Australia, starting from any airport in London, UK), which may be listed in ascending order of price.
  • a flexible search criterion eg. destination is any airport in Australia, starting from any airport in London, UK
  • An example of user interface output is shown in Figure 13.
  • a minimum price for an outward leg of the journey may be presented as a function of outward departure date eg. for a selectable calendar month
  • a minimum price for a return leg of the journey may be presented as a function of return departure date eg. for a selectable calendar month, for selected start and destination cities or airports.
  • An example of user interface output is shown in Figure 14.
  • a browse service may be provided to users of smartphone apps or to users of a search website.
  • the browse service may access browsed prices data.
  • Browsed price data may include data that has been scraped from website content.
  • the browse service may be connected to a public application programming interface (API).
  • API application programming interface
  • the browse service might not be connected to airline servers or to the servers of other travel information service providers.
  • An example of an architecture for browse searches is shown in Figure 15.
  • An example of an overall system architecture for a system which includes provision of estimation services for fare availability and price is shown in Figure 16. The following section provides background information on how airfares are calculated and the role of the GDS.
  • GDS Global Distribution System
  • the GDS makes these functions available to subscribing travel agents, booking engines, airlines and to other subscribers.
  • An example GDS configuration is shown in Figure 17.
  • a more general term is a "Distribution System", which may include the ability to make reservations for modes of transport other than air transport, for example, e.g. trains.
  • the MCT Minimum Connect Time
  • OAG OAG
  • MCT Minimum Connect Time
  • the MCT distributed by OAG, is a complex document which specifies general rules how flights / carriers can connect in terms of time e.g. IF long-haul US - EU connect at LHR to a EU - EU, apply 120 min MCT.
  • the general rules are easy to interpret, but are important— a manual override is needed if someone wants to place a booking outside the MCT parameters.
  • a fare is a pricing contract, which specifies a price (base fare) for a service provided by the carrier, defined by service class, class type (Y, Q, J, M, H, etc) and type of trip (one-way, round-trip, multi-city, round-the-world (RTW)).
  • each fare comes with an attached number of rules and restrictions that apply to it— think of a fare as a contract that is 20-30 pages long, rather than just an excel raw (a spreadsheet or CSV file). So rather than thinking of a fare as a row in a big excel spreadsheet with columns indicating which features a fare has, instead one should think of a fare as a complex thing in its own right with many parameters and restrictions and therefore rather more like a contract.
  • the fare defines who, when, and how one can use the fare— passenger type, when the fare is applicable (NVB, NVA— not valid before, not valid after) and a whole lot of other restrictions.
  • a fare could be a public fare (accessible to every reseller) or a private fare (accessible to specified reseller/ seller).
  • Fares are provided by carriers; they are defined at a general level to a market, and are limited by travel and ticketing dates. Naturally, a fare represents a price point.
  • ATPCO is an industry body that handles the fares and fares distribution in the industry. There's a small number of carriers which distribute only to SITA (Bulgaria Air is an example) .
  • ATPCO would release the fares on a specific schedule, but per carrier request, the organization started releasing the fares hourly. This allows airlines to ensure quick reaction to market changes that are not suitable only for availability controls.
  • GDS-specific fares, or negos these fares are entirely under the control of the GDSs and do not follow a general distribution schedule.
  • Fare data, fare class data, and fare restriction data are provided.
  • Rules are calculated by a pricing engine in a very strict order of rule records, tables, and sub-tables. If no specific rule records are found, then there is a General Rule that governs them all.
  • Category Data tables are last, when the engines read to get the relevant information
  • fare class tells engines there is a rule attached, which is Category 15 (Sales restrictions), which, after digging into the Category Data Tables pulls additional information on it— what type, what are the rules and restrictions.
  • Category 15 Sales restrictions
  • Availability knowledge is very important in the industry. Availability allows airlines to control their inventory (seats) and it adds another way to manage how profitable a route is by opening up and closing availability buckets, which is managed by revenue management systems (KM) . While certain RMs have great functionality to manage complex rules, most of the availability rules are created by internal departments of statisticians and mathematicians, so the complexity is high.
  • NAVS simple, numeric availability per seat class.
  • AVS availability status— is the flight open/ closed, open for waitlist, etc. n) Polling
  • CXR (a carrier) could use NAVS/AVS only as an indication, if a better connection goes down. These CXR use revenue management rules and they need to be polled for real availability and all availability information.
  • a caching mechanism may be fed by offline traffic, but used by an online channel.
  • An Availability Server may have the capability to update cached data based on rules (interactive polling) and flag any inconsistencies for booking.
  • An example system including an Availability Server is shown in Figure 21.
  • Figs. 1-17 and 22 support combinations of means for performing the specified functions, combinations of steps for performing the specified functions, and program instruction means for performing the specified functions. It is to be understood that the above-referenced arrangements are only illustrative of the application for the principles of the present invention. Numerous modifications and alternative arrangements can be devised without departing from the spirit and scope of the present invention.
  • step (ii) configuring one or more processors to determine estimated prices from an incomplete historical price dataset by analysing patterns in that dataset, at any time with respect to step (i) above;
  • the above may include additionally any of the following, alone or in combination:
  • parameters defining those goods or services include one or more of the following: activity type, such as airfare, hotel booking, train fare; date range; destination; origin; desired weather conditions; star ratings; keywords; any other user defined preference.
  • step (ii) comprises: (a) obtaining historical price quotes from a computer data store; (b) grouping the historical price quotes by category; (c) deriving statistics for each group; (d) storing on a computer for each group a classifier including the derived statistics, and (e) identifying groups with stored classifiers to which the requested price corresponds.
  • step (iii) comprises: calculating a set of estimates for the requested price over the specified date range using statistics from the stored classifiers corresponding to the identified groups.
  • step (ii) includes using rules in order to analyse patterns in the dataset.
  • step (ii) includes a na ' ive Bayes classifier machine learning approach which produces a probabilistic model of prices, and that model is used to predict unobserved prices.
  • classifiers are trained using observed prices and sets of features that correspond to them.
  • the features relate to the request, and include one or more of: departure day of week, length of stay, stay Saturday, airline, time to travel, route, month.
  • a classifier then predicts the price of an unobserved price by being given a set of features and providing a most likely price to have those features.
  • features may be derived by training multiple models with different features and comparing the predictive accuracy of the different models.
  • step (ii) includes building a statistical model from historical prices, identifying missing quote candidates, and pricing quote candidates based on the statistical model.
  • estimating prices for each candidate quote occurs in the following steps: extracting category feature values from the quote; retrieving from a database a classifier trained for an extracted category; extracting all feature values from the quote candidate; classifying the candidate quote by calculating Bayes posterior probabilities for each price range stored in a classifier and choosing a price range class with a highest Bayes posterior probability, and attaching a price class to a candidate quote.
  • inputs for the statistical model include: list of routes, classifier categorization scheme, historical quotes, and a set of supported features with weights, historical quotes are filtered by age.
  • inputs for the statistical model include reversed route equivalents.
  • the method including the step of: including cached fare prices in the set of price estimates.
  • the set of price estimates is constructed without querying a Distribution System after step (i). • the price is for a journey that is a one-way journey.
  • the request comprises a flexible search request.
  • the method including the steps of: (A) configuring the one or more processors to determine confidence ranges of the estimated prices from an incomplete historical price dataset by analysing patterns in that dataset, at any time with respect to step (i), and (B) configuring the one or more processors to calculate confidence ranges of the estimated prices for the requested price for the goods or services that satisfy the parameters.
  • the method further comprising the step of: (C) providing the confidence ranges together with the fare price estimates to an end-user computing device, such as a personal computer, smartphone or tablet.
  • an end-user computing device such as a personal computer, smartphone or tablet.
  • the method further comprising the step of: using the confidence ranges to decide whether to display a price to a user, or to provide a probable range of prices to a user.
  • step (i) includes receiving a request for a return fare price for a specified range of outward dates and a specified range of return dates for a journey from a starting location to a destination location
  • step (iv) includes providing the set of best return fare price estimates for the specified range of outward dates and for the specified range of return dates.
  • an estimation process is parameterized by one or more of: a minimum Bayes posterior probability required to accept a classification result; a maximum number of route operators involved in candidates' generation, or a random variation added to Bayes posterior probability to avoid ties.
  • a server configured to provide price estimates, the server arranged to:
  • the server may include additionally any of the following, alone or in combination:
  • the server is arranged to: obtain historical prices from a computer data store; group the historical prices by category; derive statistics for each group; store for each group a classifier including the derived statistics, and identify groups with stored classifiers to which the requested price corresponds.
  • the server is arranged to: calculate a set of estimates for the requested price over the specified date range using statistics from the stored classifiers corresponding to the identified groups.
  • the server is further arranged to perform a method of any of aspect of concept A.
  • a computer program product embodied on a non-transient storage medium, the computer program product when running on a computer arranged to provide price estimates, the computer program product when running on a computer arranged to:
  • the computer program product may be arranged to perform a method of any aspect of concept A.
  • a computer terminal in connection with a server of any server of concept A, the computer terminal arranged to send a request for a price estimate for a specified date range for a journey from a starting location to a destination location, wherein the computer terminal is further arranged to receive price estimates.
  • an item or a service such as an airline ticket, and in which a price estimate for that item or service is provided using the method defined in any aspect of concept A, or a server defined in any aspect of concept A.
  • a web-based price comparison web site with which end-users can interact by providing a request for a price for goods or services, and that web-based price comparison web site provides that request to a server that estimates the price(s) using the method of any aspect of concept A.
  • step (iii) calculating estimates for the requested fare prices using the results of step (ii), (iv) sending the request to a Distribution System for fare prices for the journey from the starting location to the destination location on a particular date;
  • step (vi) comparing the calculated estimates for the requested fare prices from step (iii) with the Distribution System's fare prices received in step (v) so as to infer which fare classes are available for the journey from the starting location to the destination location on the particular date, and
  • the above may include additionally any of the following, alone or in combination:
  • step (ii) comprises:
  • step (iii) comprises: calculating a set of estimates for the requested fare prices over the specified date range using statistics from the stored classifiers corresponding to the identified groups.
  • the Distribution System is a Global Distribution System.
  • the server is an airline server.
  • step (ii) includes using rules in order to analyse patterns in the dataset.
  • step (ii) includes a na ' ive Bayes classifier machine learning approach which produces a probabilistic model of fares, and that model is used to predict unobserved fares.
  • classifiers are trained using observed prices and sets of features that correspond to them.
  • the features relate to the request, and include one or more of: departure day of week, length of stay, stay Saturday, airline, time to travel, route, month.
  • a classifier then predicts the price of an unobserved fare by being given a set of features and providing a most likely price to have those features.
  • features may be derived by training multiple models with different features and comparing the predictive accuracy of the different models.
  • step (ii) includes building a statistical model from historical prices, identifying missing quote candidates, and pricing quote candidates based on the statistical model.
  • • estimating prices for each candidate quote occurs in the following steps: extracting category feature values from the quote; retrieving from a database a classifier trained for an extracted category; extracting all feature values from the quote candidate; classifying the candidate quote by calculating Bayes posterior probabilities for each price range stored in a classifier and choosing a price range class with a highest Bayes posterior probability, and attaching a price class to a candidate quote.
  • inputs for the statistical model include: list of routes, classifier categorization scheme, historical quotes, and a set of supported features with weights.
  • the method including the step of: including cached fare prices in the set of fare price estimates.
  • the request comprises a flexible search request.
  • an estimation process is parameterized by one or more of: a minimum Bayes posterior probability required to accept a classification result; a maximum number of route operators involved in candidates' generation, or a random variation added to Bayes posterior probability to avoid ties. • the method is performed on a server from which estimation services for fare availability and price are provided.
  • a server configured to infer which fare classes are available for a journey from a starting location to a destination location on a particular date, the server arranged to:
  • the server wherein for (iii) the server is arranged to: calculate a set of estimates for the requested fare price over the specified date range using statistics from the stored classifiers corresponding to the identified groups.
  • the server further arranged to perform a method including any of the above method limitations.
  • a computer program product embodied on a non-transient storage medium, the computer program product when running on a computer arranged to infer which fare classes are available for a journey from a starting location to a destination location on a particular date, the computer program product when running on a computer arranged to:
  • the computer program product arranged to perform a method according any of the above method limitations.
  • a computer in connection with a server of any of the above server concepts of Concept B, the computer arranged to send a request defining the journey from the starting location to the destination location on the particular date, wherein the computer is further arranged to receive the outputted inferred fare class availability for the journey from the starting location to the destination location on the particular date.
  • a method of estimating a fare price including the steps of: (i) obtaining historical fare quotes from a computer data store;
  • a method of estimating a fare price including the steps of:
  • step (iii) calculating an estimate for the requested fare price using the results of step (ii), and (iv) providing the requested fare price estimate.
  • a method of providing a set of best return fare price estimates for a specified range of outward dates and a specified range of return dates including the steps of:
  • Method of providing a set of return fare price estimates including the steps of:
  • step (iii) calculating a set of estimates for the requested return fare price for the specified outward and return dates using the results of step (ii), and
  • a method of providing a set of best return fare price estimates for a specified range of outward dates and a specified range of return dates including the steps of:
  • step (iii) calculating a set of estimates for the requested return fare price for pairs of outward and return dates using the results of step (ii), and

Abstract

L'invention concerne des procédés, des serveurs et des produits programme d'ordinateur pour fournir un ensemble de prix. Un serveur informatique reçoit une requête d'un prix pour des produits ou des services, tel que des tarifs aériens, conjointement avec des paramètres définissant ces produits ou ces services, par exemple : un type d'activité, tel qu'un tarif aérien, une réservation d'hôtel, un tarif-voyageur ; une plage de données ; une destination ; une origine ; des conditions météorologiques souhaitées ; des normes de classement ; des mots-clés ; une toute autre préférence définie par l'utilisateur. Un ou plusieurs processeurs programmés avec un logiciel déduisent, estiment ou prédisent ensuite des prix estimés à partir d'un ensemble de données de prix historique incomplet par analyse de modèles dans cet ensemble de données et fournissent les estimations de prix à un dispositif informatique d'utilisateur final, tel qu'un ordinateur personnel, un téléphone intelligent ou une tablette.
EP13810989.7A 2013-12-11 2013-12-11 Procédé et serveur pour fournir un ensemble d'estimations de prix, telles que des estimations de tarif aérien Ceased EP3080764A1 (fr)

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PCT/GB2013/053261 WO2015087026A1 (fr) 2013-12-11 2013-12-11 Procédé et serveur pour fournir un ensemble d'estimations de prix, telles que des estimations de tarif aérien

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EP (1) EP3080764A1 (fr)
JP (1) JP6473158B2 (fr)
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WO (1) WO2015087026A1 (fr)

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CN112288505A (zh) * 2019-07-22 2021-01-29 广州酷旅旅行社有限公司 一种基于ota平台的酒店房型自动跟价方法
CN110751522A (zh) * 2019-10-21 2020-02-04 中国民航信息网络股份有限公司 航空销售信息的处理方法及装置
CN112330476A (zh) * 2020-11-27 2021-02-05 中国人寿保险股份有限公司 一种团单保险业务预测方法及装置
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AU2013407812B2 (en) 2020-07-02
JP2016540319A (ja) 2016-12-22
JP6473158B2 (ja) 2019-02-20
AU2013407812A2 (en) 2016-07-28
CN106104615A (zh) 2016-11-09
WO2015087026A1 (fr) 2015-06-18
AU2020244405A1 (en) 2020-10-29
AU2022221550A1 (en) 2022-09-22
AU2013407812A1 (en) 2016-07-07

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