WO2020230042A1

WO2020230042A1 - A computer implemented system for determining an optimal route and a method thereof

Info

Publication number: WO2020230042A1
Application number: PCT/IB2020/054517
Authority: WO
Inventors: Siddharth KUMAR
Original assignee: Sunmoribus Innovation Llp
Priority date: 2019-05-13
Filing date: 2020-05-13
Publication date: 2020-11-19
Also published as: WO2020230042A4

Abstract

The present invention envisages a computer implemented system (100) for determining an optimal route for a transportation service for a new route. The system (100) is configured to perform the steps of: gathering, at a cloud server (104), data from a plurality of data sources (102-1, 102-2,..., 102-n) pertaining to a set of factors affecting starting of a new route for a 5 return service between at least two locations, the plurality of data sources (102-1, 102-2,..., 102-n) including at least one real-time data source, and creating a data pool (106) from gathered data and processing the gathered data from the data pool (106) by employing at least one machine learning technique and based on at least one historical trend to provide an optimal route determination score for the return service.

Description

A COMPUTER IMPLEMENTED SYSTEM FOR DETERMINING AN OPTIMAL ROUTE AND A METHOD THEREOF

FIELD OF INVENTION

The present disclosure relates to transportation services and in particular relates to determining an optimal route in various transportation services.

DEFINITIONS OF TERMS USED IN THE SPECIFICATION

As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used indicate otherwise.

The expression‘machine learning’ used hereinafter in the specification refers to, but is not limited to, an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

The expression‘data pool’ used hereinafter in the specification refers to, but is not limited to, collection of data from a plurality of data sources, such as, databases, excel sheet, and web sources.

These definitions are in addition to those expressed in the art.

BACKGROUND

The background information herein below relates to the present disclosure but is not necessarily prior art.

Due to time saving nature of air travelling, a lot of people prefer using air transportation services over the other means of the transportation. Therefore, aviation sector is constantly looking to add new travelling routes that are cost-effective and at the same time meets the demands of passengers.

Decision of adding a new route for air transportation cannot be done overnight. It involves a lot of money and therefore making it a subject of hours upon hours of research. For starting a new route to a particular destination, each airline takes into account several considerations, such as, demand of that destination by passengers, airfares to that destination, expenses involved in buying new airplanes and so forth. The research involves extensive manual exercise ranging from collecting data related to passengers demands for that particular destination, fuel cost, airfares at different routes etc. that is later on analysed by concerned group of people to making profitability forecasts based on intuition or sheer tribal wisdom of experts. However, making such an important decision merely on the basis of intuition or wisdom of experts involves a lot of risk in terms of money and time/efforts. Moreover, the human based analysis of such an enormous amount of data is prone to errors and may lead to complete failure.

Most of the airlines are till date making such an important decision based on wisdom of experts and analysis performed by humans. There is no automated system for analysis of factors affecting opening up the new route, determining the exact profitability of introducing the new route and suggesting modification of the present ones according to their profitability analysis.

Therefore, there is a need to provide an automated system that determines an optimal route that is cost-effective and minimal human intervention is required in doing so. OBJECTS

Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:

It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative. An object of the present disclosure is to provide a computer implemented system that determines an optimal route for various transportation services.

Another object of the present disclosure is to provide a computer implemented system that employs artificial intelligence.

Still another object of the present disclosure is to provide a computer implemented system that analyzes data affecting opening of a new route in real-time.

Yet another object of the present disclosure is to provide a computer implemented system that requires minimal human intervention in decision making. Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.

SUMMARY

The present invention envisages a computer implemented system for determining an optimal route for a transportation service. The system is configured to perform the steps of: gathering, at a cloud server data from a plurality of data sources pertaining to a set of factors affecting starting of a new route for a return service between at least two locations, the plurality of data sources including at least one real-time data source, and creating a data pool from gathered data; and processing the gathered data from the data pool by employing at least one machine learning technique and based on at least one historical trend to provide an optimal route determination score for the return service.

In an embodiment, the processing is based on at least one input derived from a variance between the optimal route determination score and actual profitability in operation, wherein the system is configured to capture said at least one input and modify the set of factors. In another embodiment, the computer implemented system (100) as claimed in claim 1, wherein the factors are selected from the group consisting of transportation cost, region-wise daily fuel pricing, daily currency exchange rate, ticket pricing of existing transportation services between the at least two locations, destination type, user expenditure behavior at the at least two locations, and number of users using existing transportation services between the at least two locations.

In still another embodiment, the system is configured to provide comparative route determination analysis for two or more routes.

In yet another embodiment, the transportation service is selected from the group consisting of airline service, road transport service, railroad service, and water transport service. In a further embodiment, the processing includes employing mind map analysis for each of the set of factors.

In a still further embodiment, the processing includes consolidating mind map analysis performed for the each of the set of factors. In an embodiment, the system is configured to modify the set of factors based on type of the transportation service.

A computer implemented method for determining an optimal route for a transportation service for a new route, the method comprising the steps of:

• gathering data from a plurality of data sources pertaining to a set of factors affecting starting of a new route for a return service between at least two locations;

• creating a data pool from gathered data;

• processing the gathered data from the data pool by employing at least one machine learning technique; and

• providing an optimal route determination score for the return service based on at least one historical trend.

In an embodiment, the plurality of data sources includes at least one of a real-time data source, a historical data source and a human input derived from a variance between said optimal route determination score and actual profitability in operation.

In another embodiment, the human input is captured to modify the set of factors based on the variance.

BRIEF DESCRIPTION OF ACCOMPANYING DRAWING

A computer implemented system for determining an optimal route for a transportation service and a method of the present disclosure will now be described with the help of the accompanying drawing, in which:

Figure 1 illustrates an exemplary network diagram of a computer implemented system for determining an optimal route for a transportation service, in accordance with an embodiment of the present disclosure;

Figure 2 illustrates an exemplary method for determining an optimal route for a transportation service, in accordance with another embodiment of the present disclosure; and

Figure 3 illustrates an exemplary mind map for an air transportation service, in accordance with another embodiment of the present disclosure. LIST OF REFERENCE NUMERALS USED IN DETAILED DESCRIPTION AND DRAWING

100 - System for Determining An Optimal Route 102-1, 102-2, ..., 102-n - Plurality of Data Sources 104 - Cloud server

106 - Data Pool

DETAILED DESCRIPTION

Embodiments, of the present disclosure, will now be described with reference to the accompanying drawing. Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to the person skilled in the art. Numerous details, are set forth, relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well-known apparatus structures, and well-known techniques are not described in detail.

The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms "a,” "an," and "the" may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms "comprises," "comprising,"“including,” and“having,” are open ended transitional phrases and therefore specify the presence of stated features, integers, steps, operations, elements, modules, units and/or components, but do not forbid the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The particular order of steps disclosed in the method and process of the present disclosure is not to be construed as necessarily requiring their performance as described or illustrated. It is also to be understood that additional or alternative steps may be employed.

When an element is referred to as being "mounted on,"“engaged to,” "connected to," or "coupled to" another element, it may be directly on, engaged, connected or coupled to the other element. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed elements.

A preferred embodiment of a computer implemented system for determining an optimal route for a transport service(s) for a new route of the present disclosure is now being described in detail with reference to the Figure 1.

The computer implemented system 100 for determining an optimal route for a transportation service, hereinafter referred to as“system 100”, in accordance with an embodiment of the present disclosure, includes a plurality of data sources 102-1, 102-2, ..., 102-n from which the system 100 is configured to fetch data pertaining to a set of factors affecting starting of a new route for a return service between two locations. In an embodiment, determining the optimal route includes identifying profitability of opening the new route. In another embodiment, the transportation service is selected from the group consisting of airline service, road transport service, railroad service, and water transport service. In still another embodiment, the set of factors affecting starting of a new route is selected from the group consisting of transportation cost, region-wise daily fuel pricing, daily currency exchange rate, ticket pricing of existing transportation services between two locations, destination type, user expenditure behavior at the two locations, and number of users using existing transportation services between the two locations.

In an embodiment, the transportation cost includes, but is not limited to, cost of airlines type, for example say, Boeing or Airbus, costs for type of crew members required on airlines, aircraft parking charges, cost of commissioning a new aircraft, maintenance costs of the aircraft, aircraft rental costs, cost of engine, and so forth. In another embodiment, the destination type includes, but is not limited to, a metropolitan city, a rural area, an urban area, a developed country, an under developed country, a developing country and so forth. In still another embodiment the system 100 utilizes factors, such as, destination type, ticket pricing between two locations, user expenditure behavior in deciding ticket fares. In yet another embodiment, the set of factors may be pre-defined factors or may be added by a user instantaneously.

The plurality of data sources 102-1, 102-2, ..., 102-n includes a real-time data source, such as websites showing current currency exchange rate, current fuel costs, cargo transport charges, peak seasons for travelling to a particular destination, and so forth. The example of websites may include IATA, World Bank, Google current currency converter, other data freely available on travelling or booking agencies and so on. In an embodiment, the plurality of data sources 102-1, 102-2, ..., 102-n includes a historical data source, for example, excel sheets or paid databases, such as Amadeus, global distribution system (GDS) from which historical trends are derived by the system 100. In another embodiment, the system 100 employs at least one algorithm/technique on the data to derive historical trends including, but not limited to, linear regression, fuzzy logic, statistical, regression, and wavelet techniques, and other machine learning algorithms.

The data gathered from the plurality of data sources 102-1, 102-2, ..., 102-n at a cloud server 104 is pre-processed by the cloud server 104. In an embodiment, pre-processing of gathered data includes performing filtering, cleaning, normalization, transformation, feature extraction and selection. In another embodiment, a data pool 106 from the gathered data is created by the cloud server 104 and stored in a database (not shown in the figure).

Any database discussed herein may include relational, hierarchical, graphical, or object- oriented structure and/or any other database configurations. Common database products that may be used to implement the databases include DB2 by IBM (White Plains, N.Y.), various database products available from Oracle Corporation (Redwood Shores, Calif.), Microsoft Access or Microsoft SQL Server by Microsoft Corporation (Redmond, Wash.), MySQL, or any other suitable database product. Moreover, the databases may be organized in any suitable manner, for example, as data tables or lookup tables. Each record may be a single file, a series of files, a linked series of data fields or any other data structure. Association of certain data may be accomplished through any desired data association technique such as those known or practiced in the art.

In an implementation, the cloud server 104 can be implemented as one or more microprocessors, microcomputers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the cloud server 104 is configured to fetch and execute a set of pre-determined instructions from the database which when executed provides one or more executable commands.

In an embodiment, the data from the database or data pool is extracted, transformed and loaded into the cloud server 104 and the cloud server 104 is configured to employ at least one machine learning technique on the data to generate an optimal route determination score for the return service. In an embodiment, the determination score may be in the form of percentage, score, grade and the like. The return service may include, but not limited to, service between a source and a destination airport, which includes parking back at the source airport. The return service may further include having an intermediate/connecting airport between the source and the destination airports. The cloud server 104 suggests travelling via the intermediate airport based on the optimal route determination score. For instance, the user is interested in starting a new route from New Delhi to Goa, the cloud server 104 may suggest the user to return via Mumbai as the cost of refueling in Mumbai is cheaper and gives a better determination score than the Goa to Delhi direct route.

Further, the cloud server 104 is configured to process the data gathered based on the historical trend identified from the historical data source and real-time data obtained from the real-time data source. In an embodiment, the cloud server 104 is configured to process the gathered data based on an input derived from a variance between the optimal route determination score and actual profitability in operation. In another embodiment, the cloud server 104 is configured to calculate the actual profitability based on values entered by the user pertaining to actual results achieved after starting the new route. In still another embodiment, the cloud server 104 is configured to capture the values entered by the user using a user interface (not shown in the figure) of the cloud server 104. Further, the cloud server 104 is configured to compare the determination score with the actual profitability in operation and based on comparison configured to modify/update the set of factors affecting the starting of a new route.

In an embodiment, the cloud server 104 is configured to modify the set of factors based on type of transportation service. For example, the water transportation service may require crew members with different type of skills that will affect an overall score of the route determination and therefore making it necessary to modify the set of factors.

In an embodiment, the system 100 is configured to provide comparative route determination analysis for two or more routes. For example, the user may be interested in starting a new route between source airport X and destination airport Y. The system 100 may suggest all the nearby airports with a better optimal route determination score. The system 100 may further suggest best time to start a new route, for example the time with high demand of flights on the destination Y, according to the trend observed. Figure 2 illustrates a method for determining an optimal route for a transport service for a new route, in accordance with an embodiment of the present disclosure.

The order in which the method is described above is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the methods, or an alternative method. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.

A computer implemented method 200 for determining an optimal route for a transportation service for a new route will now be described in accordance with a preferred embodiment of the present invention.

At step 202, data from a plurality of data sources 102-1, 102-2, ..., 102-n is fetched by a cloud server 104. The cloud server 104 pre-processes the data and stores the data in a database. In an embodiment, the plurality of data sources 102-1, 102-2, ..., 102-n includes at least one of a real-time data source, and a historical data source.

At step 204, a data pool 106 is created from fetched data at the database. In an embodiment, the data gathered by fetching from the plurality of data sources 102-1, 102-2, ..., 102-n pertains to a set of factors affecting starting of a new route or a return service between two locations.

At step 206, gathered data is processed by the cloud server 104 from the data pool by employing at least one of machine learning techniques. In an embodiment, processing of the gathered data is based on a historical trend identified from the historical data source and real time data obtained from the real-time data source. In another embodiment, the processing includes employing mind map analysis for each of the set of factors. In still another embodiment, the processing further includes consolidating mind map analysis performed for each of the set of factors.

At step 208, an optimal route determination score is provided for the return service based on historical trend. In an embodiment, the optimal route determination score is determined for starting a new route between source and destination airports. In another embodiment, the optimal route determination score is provided based on comparative route determination analysis for two or more routes. In still another embodiment, further processing is done based on a human input derived from a variance between the optimal route determination score and actual profitability in operation. In yet another embodiment, the human input is captured to modify the set of factors based on the variance. In a further embodiment, the actual profitability is entered by the user and captured by the system 100 which is related to the costs involved in starting the new route.

Figure 3 illustrates an exemplary mind map 300 for an air transportation service, which will be now described in accordance with another embodiment of the present disclosure.

A cloud server 104 is configured to employ mind map analysis on each of the set of factors affecting the route determination for a transportation service. In an embodiment, the cloud server 104 performs the mind map analysis for each sub-factors of the airline transportation cost, such as, analysis can be performed for aircraft related cost parameters, flight related cost parameters, cabin wise cost parameters and so forth. In another embodiment, the cloud server 104 is further configured to consolidate mind map analysis performed for each of the set of factors. For example, the cloud server 104 is configured to generate the consolidated mind map of the transportation cost by combining the analyzed mind maps of the sub-factors of the transportation cost.

The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer- readable medium. Other examples and implementations are within the scope and spirit of the disclosure and appended claims. For example, due to the nature of software, functions described above can be implemented using software executed by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations.

In addition, any disclosure of components contained within other components or separate from other components should be considered exemplary because multiple other architectures may potentially be implemented to achieve the same functionality, including incorporating all, most, and/or some elements as part of one or more unitary structures and/or separate structures.

TECHNICAL ADVANCEMENTS

The present disclosure described herein above has several technical advantages including, but not limited to, the realization of a computer implemented system and a method that:

• determines profitability of starting a new route;

• requires minimal human intervention in decision making;

• includes real-time factors for optimal route determination;

• is cost effective; and

• is easy to use and implement.

The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

The foregoing description of the specific embodiments so fully revealed the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.

The use of the expression“at least” or“at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the disclosure to achieve one or more of the desired objects or results. Any discussion of documents, acts, materials, devices, articles or the like that has been included in this specification is solely for the purpose of providing a context for the disclosure. It is not to be taken as an admission that any or all of these matters form a part of the prior art base or were common general knowledge in the field relevant to the disclosure as it existed anywhere before the priority date of this application. The numerical values mentioned for the various physical parameters, dimensions or quantities are only approximations and it is envisaged that the values higher/lower than the numerical values assigned to the parameters, dimensions or quantities fall within the scope of the disclosure, unless there is a statement in the specification specific to the contrary.

While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.

Claims

CLAIMS:

1. A computer implemented system (100) for determining an optimal route for a transportation service, said system (100) configured to perform the steps of:

• gathering, at a cloud server (104), data from a plurality of data sources (102-1, 102-2, ..., 102-n) pertaining to a set of factors affecting starting of a new route for a return service between at least two locations, said plurality of data sources (102-1, 102-2, ..., 102-n) including at least one real-time data source, and creating a data pool (106) from gathered data; and

• processing said gathered data from said data pool (106) by employing at least one machine learning technique and based on at least one historical trend to provide an optimal route determination score for said return service.

2. The computer implemented system (100) as claimed in claim 1, wherein said processing is based on at least one input derived from a variance between said optimal route determination score and actual profitability in operation, wherein the system (100) is configured to capture said at least one input and modify said set of factors.

3. The computer implemented system (100) as claimed in claim 1, wherein said factors are selected from the group consisting of transportation cost, region-wise daily fuel pricing, daily currency exchange rate, ticket pricing of existing transportation services between said at least two locations, destination type, user expenditure behavior at said at least two locations, and number of users using existing transportation services between said at least two locations.

4. The computer implemented system (100) as claimed in claim 1, wherein said system (100) is configured to provide comparative route determination analysis for two or more routes.

5. The computer implemented system (100) as claimed in claim 1, wherein said transportation service is selected from the group consisting of airline service, road transport service, railroad service, and water transport service.

6. The computer implemented system (100) as claimed in claim 1, wherein said processing includes employing mind map analysis for each of said set of factors.

7. The computer implemented system (100) as claimed in claim 6, wherein said processing includes consolidating mind map analysis performed for said each of said set of factors.

8. The computer implemented system (100) as claimed in claim 1, wherein said system (100) is configured to modify said set of factors based on type of said transportation service.

9. A computer implemented method (200) for determining an optimal route for a transportation service, said method (200) comprising the steps of:

• gathering (202) data from a plurality of data sources pertaining to a set of factors affecting starting of a new route for a return service between at least two locations;

• creating (204) a data pool from gathered data;

· processing (206) said gathered data from said data pool by employing at least one machine learning technique; and

• providing (208) an optimal route determination score for said return service based on at least one historical trend.

10. The computer implemented method (200) as claimed in claim 9, wherein said plurality of data sources includes at least one of a real-time data source, a historical data source and a human input derived from a variance between said optimal route determination score and actual profitability in operation.

11. The computer implemented method (200) as claimed in claim 9, wherein said human input is captured to modify said set of factors based on said variance.