SYSTEM AND METHOD FOR DETECTING EFFECTIVE TRAVEL OPTION AND TICKETS BETWEEN A SOURCE AND DESTINATION WITH DIFFERENT MODES OF TRANSPORTS

• US 20170344913A1
• Filed: 05/29/2017
• Published: 11/30/2017
• Est. Priority Date: 05/29/2016
• Status: Active Application

• Current Assignee: Confirm Ticket Online Solutions Private Limited
• Original Assignee: Confirm Ticket Online Solutions Private Limited
• Inventors: VAIDYA, SRIPAD, KUMAR, KOTHA DINESH
• Application Number:
  US15/607,630
• Publication Number:
  US 20170344913A1
• Time in Patent Office: Days
• CPC Class Codes: G06Q 10/025 Coordination of plural reservations, e.g. plural trip segments, transportation combined with accommodation
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083

Abstract

The embodiments herein provide a system and method for effective detection and identification of travel options between a source and destination pair. The system and method identifies a plurality of alternative travel options between a source and destination pair for effective utilization of unused reservation quotas in a plurality of modes of travel. Currently, there are no systems available to enable travelers with information on alternative reservation options. The system and method utilizes graph data-structure to determine the available travel options between given source and destination pair is provided. The system and method enables travelers to reserve tickets by connecting or chaining multiple vehicles in the same or different modes of travel between given source and destination pair. The system also enables an effective travel solution by identifying a plurality of journey break options.

Claims

1. A system comprising a hardware processor and a memory storing instructions that are run on the hardware processor for effective detection, identification and booking of travel options between a source and a destination pair, the system comprising:

a client module configured for enabling one or more users to specify one or more travel journey related details, and wherein the one or more travel journey related details comprise name of a source station, name of a destination station, preferred time and date of travel and preferred mode of transport;

a server communicatively coupled to the client module, and wherein the server is configured for presenting one or more available travel options to the user and for booking travel tickets for most suitable travel option selected by the user, and wherein a travel option comprises a direct travel or a break journey with one or more hops between source station and destination station using one or more modes of transport, and wherein the one or more modes of transport comprise train, bus, taxi and flight;

an authentication module provided in the server and run on a hardware processor, and wherein the authentication module is configured for authenticating each user using one or more personal details provided by the user, and wherein the one or more personal details comprise a mobile number, Facebook credentials and Google credentials, and wherein the authentication module is further configured for generating a unique token number for identifying each user associated with the system;

a real time ticket discovery engine provided in the server and run on a hardware processor, and wherein the real time ticket discovery engine is configured for searching one or more available travel options for the user to reach the destination station based on seat availability in one or more modes of transport between the source station and the destination station, and wherein the real time ticket discovery engine is further configured for preparing and sharing a travel itinerary with the user from source station to destination station by chaining one or more modes of transport based on seat availability;

an availability check module provided in the server and run on a hardware processor, and wherein the availability check module is configured for checking and retrieving seat availability data from a plurality of ticket booking portals for one or more modes of transport and providing the retrieved data to the real time ticket discovery engine; and

a booking engine provided in the server and run on a hardware processor, and wherein the booking engine is configured for booking one or more tickets for the user based on the most suitable travel option selected by the user, and wherein one or more details related to the booked ticket are stored in a database.

2. The system according to claim 1, wherein the real time ticket discovery engine is further configured for searching an alternate option to reach the destination station in case no seats are available in any mode of transport, and wherein the alternate option to reach the destination station comprise a wait listed ticket in one or more modes of transport with highest chances of confirmation, a hop journey using a combination of more than one modes of transport and a longer route with confirmed tickets.

3. The system according to claim 1, wherein the system further comprises a historical data store configured for storing historical data related to availability of tickets, change of ticket status, price of tickets, delay in arrival/departure of the selected mode of transport and preferred mode of transport for each user.

4. The system according to claim 1, wherein the system further comprises a data processing engine configured for processing a logged historical passenger name record number (PNR) data available from one or more ticket booking portals along with plurality of journey details for one or more users to determine trends related to confirmation of wait-listed tickets for a particular mode of transport, and wherein the plurality of journey details comprise class of travel, booking status, journey date, quota and final charting status, and wherein the data processing engine is further configured for grouping the historical passenger frame record number (PNR) data into one or more groups and marking whether particular ticket is confirmed or wait-listed, and wherein the one or more groups comprise a weekend season group, a normal season group, a holiday season group, a quota-wise group and a class-wise group.

5. The system according to claim 1, wherein the system further comprises a prediction engine configured for processing the historical passenger name record number (PNR) data logged using the data processing engine to determine a threshold cancellation value related to each mode of transport using one or more machine learning algorithm, and wherein the threshold cancellation value is used for determining the chances of a ticket booking getting confirmed.

6. The system according to claim 1, wherein the system further comprises a staging database configured for storing raw data collected from all the ticket booking portals associated with the system, and wherein the data processing engine is further configured for moving the raw data to a production database after cleansing, processing and validation.

7. The system according to claim 1, wherein the system further comprises a cache layer configured for storing plurality of details related to one or more modes of transport associated with the system, and wherein the plurality of details comprises schedule and seat availability for each mode of transport, and wherein the cache layer is further configured for caching the plurality of details related to one or more modes of transport for a pre-determined period of time.

8. The system according to claim 1, wherein the real time ticket discovery engine is further configured for searching the one or more available travel options for the user based on one or more factors, and wherein the one or more factors comprise fastest way to reach the destination station, waiting time, number of hops, cheaper mode of transport, convenience for the user and the preference of the user.

9. The system according to claim 1, wherein the system is further configured for identifying one or more stations for which the seat availability changes by changing the source station and by keeping the destination station constant and vice versa, and wherein the identified one or more stations are marked with a data change flag, and wherein the one or more stations marked using data change flag are used by the real time ticket discovery engine for determining seat availability between a source station to a destination station, a source station to an intermediate station and an intermediate station to a destination station.

10. The system according to claim 1, wherein the system is further configured for utilizing unused reservation quotas/tickets available in special category between the source station and the destination station in one or more modes of transport, and wherein reservation quota/special category comprises reservation quota, senior citizen quota, ladies quota, very important people (VIP) quota and military quota.

11. The system according to claim 1, wherein the real time ticket discovery engine is further configured for searching the one or more available travel options for the user using a graph data structure, and wherein the graph data structure comprises a connected graph with plurality of nodes representing one or more hop/intermediate stations between the source station and the destination station.

12. The system according to claim 1, wherein the system is further configured for sharing one or more predictions with the user about a status of a travel ticket getting confirmed from an unconfirmed status based on analysis of historical data on ticket confirmation.

13. The system according to claim 1, wherein the system is further configured for providing a Web interface or a mobile interface to the one or more users for effective discovery and booking of one or more travel options between the source station and the destination station.

14. A computer implemented method comprising instructions stored on a non-transitory computer readable storage medium and run a computing device provided with a hardware processor and memory for effective detection, identification and booking of travel options between a source and destination pair, the method comprising the steps of:

enabling one or more users to specify one or more travel journey related details using a client module, and wherein the one or more travel journey related details comprise name of a source station, name of a destination station, preferred time and date of travel and preferred mode of transport;

authenticating one or more users using one or more personal details using an authentication module, and wherein the one or more personal comprise a mobile number, Facebook credentials and Google credentials;

searching one or more available travel options for the user to reach the destination station based on seat availability in one or more modes of transport between the source station and the destination station using a real time ticket discovery engine, and wherein the one or more modes of transport comprise train, bus, taxi and flight;

preparing and sharing a travel itinerary with the user from source station to destination station by chaining one or more modes of transport based on availability of tickets using the real time ticket discovery engine;

checking and retrieving seat availability data from a plurality of ticket booking portals for one or more modes of transport and providing the retrieved data to the real time ticket discovery engine using an availability check module; and

booking one or more tickets for the user based on the most suitable travel option selected by the user, and wherein one or more details related to the booked ticket are stored in a database.

15. The method according to claim 14, further comprises searching an alternate option to reach the destination station in case no seats are available in any mode of transport, and wherein the alternate option to reach the destination station comprise a wait listed ticket in one or more modes of transport with highest chances of confirmation, a hop journey using a combination of more than one modes of transport and a longer route with confirmed tickets.

16. The method according to claim 14, further comprises processing a logged historical passenger name record number (PNR) data available from one or more ticket booking portals along with plurality of journey details for one or more users to determine trends related to confirmation of wait-listed tickets for a particular mode of transport using a data processing engine, and wherein the plurality of journey details comprise class of travel, booking status, journey date, quota and final charting status.

17. The method according to claim 14, further comprises grouping the historical passenger name record number (PNR) data into one or more groups and marking whether particular ticket is confirmed or wait-listed, and wherein the one or more groups comprise a weekend season group, a normal season group, a holiday season group, a quota-wise group and a class-wise group.

18. The method according to claim 14, further comprises processing the historical passenger name record number (PNR) data logged using the data processing engine to determine a threshold cancellation value related to each mode of transport using one or more machine learning algorithms through a prediction engine, and wherein the threshold cancellation value is used for determining the chances of a ticket booking getting confirmed.

19. The method according to claim 14, further comprises searching the one or more available travel options for the user based on one or more factors, and wherein the one or more factors comprise fastest way to reach the destination station, waiting time, number of hops, cheaper mode of transport, convenience for the user and the preference of the user.

20. The method according to claim 14, further comprises identifying one or more stations for which the seat availability changes by changing the source station and by keeping the destination station constant and vice versa, and wherein the identified one or more stations are marked with a data change flag, and wherein the one or more stations marked using data change flag are used by the real time ticket discovery engine for determining seat availability between a source station to a destination station, a source station to an intermediate station and an intermediate station to a destination station.