invention addresses these issues.
present invention relates to a system and a method for optimizing supply of rental vehicles,
comprising a processor; and a memory disposed in communication with the processor and
storing processor executable instructions for configuring the processor to receive customer data
associated with a plurality of customers from a plurality of sources, classify the customers into a
plurality of segments, based on said data, each segment being indicative of vehicle rental
preferences of customers in the segment, determine whether any customers have already opted
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for a vehicle rental; and predict likely vehicle demand for different vehicles based on said
classification and said determination.
Some embodiments of the present invention provide a solution to merchants by predicting
demand based on addendum (flight) data and vehicle rental data to suggest whether or not the
traveler would require car rental service.
Some embodiments of the present invention predict the kind of Car the customer would like to
book, e.g. Luxury or Mid-Priced Car with the use of Experian data (Automobile data).
Yet another object of the present invention is to determine the demand forecasting for the type
of Car and number of Cars required in particular area using lodging data.
It allows the merchants to maintain a fleet of Car based on the seasonality so that demand can be
managed. Further, the peak season can be identified and customer segmentation can be done
using the transaction data. This will help in managing the demand of particular kind of cars at a
particular location and based on historical data, car rental companies can estimate how many cars
in segment are required at each pickup and drop-off point.
It is also the object of the present invention to estimate the number of people who are going to
drop their car at a particular point and thus reduce the number of cars required at each node.
BRIEF DESCRIPTION OF THE DRAWINGS
The detailed description is described with reference to the accompanying figures. In the figures
the left-most digit of a reference number identifies the figure in which the reference number first
appears. The same numbers are used throughout the drawings to reference like features and
components
Figure 1 illustrates a block diagram of an exemplary system for optimizing supply of rental
vehicles.
Figure 2 illustrates a flow diagram of an exemplary method of optimizing supply of rental
vehicles.
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Figure 3 illustrates an exemplary map of pick-up and drop-off at a series of nodes.
DETAILED DESCRIPTION OF THE INVENTION
Exemplary embodiments now will be described with reference to the accompanying drawings.
The invention may, however, be embodied in many different forms and should not be construed
as limited to the embodiments set forth herein; rather, these embodiments are provided so that
this invention will be thorough and complete, and will fully convey its scope to those skilled in
the art. The terminology used in the detailed description of the particular exemplary
embodiments illustrated in the accompanying drawings is not intended to be limiting. In the
drawings, like numbers refer to like elements.
The specification may refer to “an”, “one” or “some” embodiment(s) in several locations. This
does not necessarily imply that each such reference is to the same embodiment(s), or that the
feature only applies to a single embodiment. Single features of different embodiments may also
be combined to provide other embodiments.
As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as
well, unless expressly stated otherwise. It will be further understood that the terms “includes”,
“comprises”, “including” and/or “comprising” when used in this specification, specify the
presence of stated features, integers, steps, operations, elements, and/or components, but do not
preclude the presence or addition of one or more other features, integers, steps, operations,
elements, components, and/or groups thereof. It will be understood that when an element is
referred to as being “connected” or “coupled” to another element, it can be directly connected or
coupled to the other element or intervening elements may be present. Furthermore, “connected”
or “coupled” as used herein may include wirelessly connected or coupled. As used herein, the
term “and/or” includes any and all combinations and arrangements of one or more of the
associated listed items.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the
5
same meaning as commonly understood by one of ordinary skill in the art to which this invention
pertains. It will be further understood that terms, such as those defined in commonly used
dictionaries, should be interpreted as having a meaning that is consistent with their meaning in
the context of the relevant art and will not be interpreted in an idealized or overly formal sense
unless expressly so defined herein.
The figures depict a simplified structure only showing some elements and functional entities, all
being logical units whose implementation may differ from what is shown. The connections
shown are logical connections; the actual physical connections may be different.
In addition, all logical units described and depicted in the figures include the software and/or
hardware components required for the unit to function. Further, each unit may comprise within
itself one or more components, which are implicitly understood. These components may be
operatively coupled to each other and be configured to communicate with each other to perform
the function of the said unit.
Figure 1 illustrates a block diagram of an exemplary system (100) for optimizing supply of rental
vehicles. Figure 1(a) illustrates a system (100) representing database connection comprises a
communication device (101) which is configured to extract required data information from the
server (102). The server is coupled with the Addendum database (103) and Transaction database
(104). The Addendum database (103) comprises passenger plane addendum and vehicle rental
addendum data. The passenger place addendum comprises information about the passenger
travelling including the transaction data and the sequence number which creates a key unique to
the passenger. It comprises fields including but not limited to origin airport, destination airport,
type of class (business, first or economy), travel data and time, transaction date and sequence
number. The vehicle rental addendum data comprises information regarding car rentals. Again
here, same as in the passenger plane addendum, a unique key is created using transaction data and
the sequence number. It comprises fields including but not limited to transaction data, sequence
number, pick up location, return location, check out data and return data. The Transaction
6
database (104) comprises transaction attributes for each transaction done through the network
(107). It comprises fields including but not limited to account number, the amount spent by the
cardholder, the industry name in which the cardholder spends, the transaction date and the
sequence number. The account number is encrypted and is unique to each cardholder. The
Transaction database (104) further comprises a Passenger account holder database (105) and a
Passenger history database (106). The Passenger account holder database (105) comprises a list
of passengers having unique accounts assigned to them. The passenger history database (106)
comprises passenger transaction history which also serves as an indicator towards the behavioral
pattern of the passenger when it comes to transaction.
Figure 1(b) illustrates hardware configuration inside the server (102). The server comprises
input/out (I/O) device (108) which is used by the end user to interface with the system (100).
There is also present a central processing unit (109) which is coupled to the I/O (108) , the
volatile RAM (112) , the ROM (113), the secondary storage (111) and the network (110). RAM
(112) allows data information to be accessed in almost the same amount of time irrespective of
the physical location of data inside the memory and is volatile. ROM (113) is usually hardwired
and is difficult to alter or cannot be altered at all. The secondary storage (111) is a non-volatile
memory which is an external device.
Figure 2 (200) illustrates a flow diagram of an exemplary method of optimizing supply of rental
vehicles. The system receives customer data from a variety of sources (201) i.e from the lodging
data, transaction data, and addendum (Flights and Vehicle renting) data residing inside the
addendum database (103), as mentioned in the preceding figures.
Thus the number of travelers arriving at airport locations and travelers who may require car
rental services is estimated (202). This is done by collecting The Passenger plane addendum and
the Vehicle Rental Addendum data from the addendum database (103).
o
The Passenger Plane Addendum data may include the following information about Passengers:
Origin Airport, Destination Airport, Class (Business, First, Economy), Travel Date and Time,
Transaction Date and/or the Sequence Number. Using this above information from the
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passenger plane addendum inside addendum database (103), the number of passengers arriving
to the destination Airport on particular day is identified. Since the passenger plane addendum
comprises the information about the passenger travelling, said number can be determined, The
Vehicle Rental Addendum data has information regarding Car rentals and may include:
Transaction Date, Sequence Number, Pickup Location, Return Location, and/or Checkout Date.
Both Vehicle and Passenger Plane Addendum data includes the transaction date and sequence
number. Using these columns, the addendum data is associated with transaction data.
The transaction data includes but is not limited to the transaction date and the transaction
behaviour of each passenger which determines the segmentation of the passengers.
The data from all the sources is merged to identify whether the passenger travelling to destination
Airport has already opted for Car Rental so that such passengers can be excluded (203). The data
is merged from all the sources i.e vehicle addendum data, passenger plane data residing inside the
addendum database (103) and transaction data residing inside the transaction database (104)
The passenger travelling to destination Airport may opt-in for Car Rental and based on Passenger
transactions history, Airline Class (Business, First, Economy); the Passenger are classified into
Luxury or Economy Class (204). The current location i.e the source location and the destination
location of the customer are determined as per what is described under Figure 2 given herein
under below (205).
Such information is provided in advance to Vehicle Rental Merchants in an aggregated form.
(206). Various customers and travelers are classified into various segments (Luxury, Economy
etc.) based on the transaction history data residing inside the passenger history database (106) in
the car rental industry and the airline class to identify the car type they may book for (204) based
on the customer data received to predict the likely vehicle they may require at the next predicted
location (207).
Figure 3 illustrates a map of pick and drop from a series of nodes, including two particular
nodes labelled A and B. These may be pick up and drop locations of a particular passenger and a
a series of such data is used to deduce travel behaviour of the passenger and hence to better
determine the vehicle availability at a particular location. By estimating the number of people who
8
are going to drop their car at a particular point we can reduce the number of cars required at each
node. For example in the network given below if a customer drops a mid-segment car from point
A to B we can reduce the number of mid segment cars required at B from 2 to 1.
As will be appreciated by one of skill in the art, the present invention may be embodied as a
method, system, or computer program product. Accordingly, the present invention may take the
form of an entirely hardware embodiment, a software embodiment or an embodiment
combining software and hardware aspects. Furthermore, the present invention may take the form
of a computer program product on a computer-usable storage medium having computer-usable
program code embodied in the medium.
Furthermore, the present invention was described in part above with reference to flowchart
illustrations and/or block diagrams of methods, apparatus (systems), and computer program
products according to embodiments of the invention.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and
combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented
by computer program instructions. These computer program instructions may be provided to a
processor of a general purpose computer, special purpose computer, or other programmable
data processing apparatus to produce a machine, such that the instructions, which execute via the
processor of the computer or other programmable data processing apparatus, create means for
implementing the functions/acts specified in the flowchart and/or block diagram block or
blocks.
These computer program instructions may also be stored in a computer- readable memory that
can direct a computer or other programmable data processing apparatus to function in a
particular manner, such that the instructions stored in the computer-readable memory produce
an article of manufacture including instruction means which implement the function/act
specified in the flowchart and/or block diagram block or blocks.
9
The computer program instructions may also be loaded onto a computer or other programmable
data processing apparatus like a scanner/check scanner to cause a series of operational steps to
be performed on the computer or other programmable apparatus to produce a computer
implemented process such that the instructions which execute on the computer or other
programmable apparatus provide steps for implementing the functions/acts specified in the
flowchart and/or block diagram block or blocks.
The flowchart and schematic diagrams illustrate the architecture, functionality, and operations of
some embodiments of methods, systems, and computer program products for managing security
associations over a communication network. In this regard, each block may represent a module,
segment, or portion of code, which comprises one or more executable instructions for
implementing the specified logical function(s). It should also be noted that in other
implementations, the function(s) noted in the blocks may occur out of the order noted in the
figures. For example, two blocks shown in succession may, in fact, be executed substantially
concurrently or the blocks may sometimes be executed in the reverse order, depending on the
functionality involved.
In the drawings and specification, there have been disclosed exemplary embodiments of the
invention. Although specific terms are employed, they are used in a generic and descriptive sense
only and not for purposes of limitation, the scope of the invention being defined by the
following claims.
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We Claim:
1. A system for optimizing supply of rental vehicles, comprising:
a processor; and
a memory disposed in communication with the processor and storing processor-executable
instructions for configuring the processor to:
receive customer data associated with a plurality of customers from a plurality of sources;
classify the customers into a plurality of segments, based on said data, each segment being
indicative of vehicle rental preferences of customers in the segment;
determine whether any customers have already opted for a vehicle rental;
determine the destination and source location of the customer and
predict likely vehicle demand for different vehicles based on said classification and said
determination.
2. The system as claimed in claim 1, wherein the customer data further comprises at least
one of itinerary data, transaction data, demographic data, location data, flight data, vehicle rental
data, lodging data.
3. The system as claimed in claim 1 or claim 2, wherein the data received from a plurality of
sources includes one or more of: flight information, origin and destination data, class, travel date
and time, transaction date, sequence number of the flight, vehicle rental information, transaction
data, and sequence number.
4. The system as claimed in any one of the preceding claims, wherein classifying the data to
create the customer data to determine the preferences of the customer comprises associating the
flight and rental data with the transaction data.
5. The system as claimed in any one of the preceding claims, wherein the data from all the
sources is merged to exclude the customers who have already opted for a car rental.
6. The system as claimed in any one of the preceding claims, wherein based on the
customer data created and the source and destination location of the customer a car type
preference of the customer is predicted.
7. The system as claimed in any one of the preceding claims, wherein a car type preference
of the customer is predicted and communicated to the vehicle rental merchant in advance.
8. A method for optimising supply of rental vehicles, comprising:
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receiving customer data associated with a plurality of customers from a plurality of sources;
classifying the customers into a plurality of segments, based on said data, each segment being
indicative of vehicle rental preferences of customers in the segment;
determining whether any customers have already opted for a vehicle rental;
determining the destination and source location of the customer ;and
predicting likely vehicle demand for different vehicles based on said classification and said
determination.
9. The method as claimed in claim 8, wherein the customer data further comprises at least
one of itinerary data, transaction data, demographic data, location data, flight data, vehicle rental
data, lodging data.
10. The method as claimed in claim 8 or claim 9, wherein the data received from a plurality
of sources includes one or more of: flight information, origin and destination data, class, travel
date and time, transaction date, sequence number of the flight, vehicle rental information,
transaction data, and sequence number.
11. The method as claimed in any one of the preceding claims, wherein the step of
classifying the data to create the customer data further comprises determining the preferences of
the customer associated with the flight and rental data with the transaction data.
12. The method as claimed in any one of the preceding claims, wherein the data from all the
sources is merged to exclude the customers who have already opted for a car rental.
13. The method as claimed in any one of the preceding claims, wherein based on the
customer data created and the source and destination location of the customer a car type
preference of the customer is predicted.
14. The method as claimed in any one of the preceding claims, wherein a car type preference
of the customer is predicted and communicated to the vehicle rental merchant in advance.