PNR Analysis in Indian Railways BAIS Innovative Project Group 6 Section A

PNR Analysis in Indian Railways

BAIS Innovative ProjectGroup 6 Section AAbhishek Minz (0010/49) | N Venkatesh (0210/49) | Ankit Renee Topno (4010/49) | Ishan Pendam (0230/49) | Praveen Baskey (0243/49) | Rohit Bhirud (4014/49) BACKGROUND Indian Railways is biggest mover of people across the country on a daily basis (25 million passengers daily)Demand >>> Supply and it is very usual for busy trains to have a waiting list of more than 500Slow response time, crashes in IRCTC; people book multiple tickets well in advanceWL ticket holders do not have a right to board the train and will only get that privilege if some one with a confirmed ticket cancels his/her ticketGiven a date of booking and a date of journey, one often has to make a choice between n number of trains with varying waitlistsNo system to find out whether a WL ticket will get confirmed or not The IdeaScenario 1 Yet to book a ticketGiven a fixed train, date of journey and date of booking- the system tells whether to book a ticket or not. If the waiting list is too high, the chances of ticket getting confirmed will be too low.Advantage- Can book tickets in alternate modes of travel like bus, flights, money is not blocked

Scenario 2 Already have a waitlisted ticket Helps to instantly predict final charting status. Using an algorithm and historical data we can predict whether the waiting list ticket will get confirmed or not.Advantage Timely cancellations and refund, reduce multiple tickets3Possible ImplementationStep 1 Generating a Data RepositoryTap into social media initiatives and online forums to catch people initially. They can enter ticket details like From, To, Date of Booking, Date of Journey, Train, Class, Initial Waitlist, Hours before Departure, Final Waitlist/Status (once travelled)Example Facebook, IRFCA (Indian Railways Fan Club Association) website, online travel forums

Possible ImplementationStep 2 Using the data for Number CrunchingThis will generate the data for PNR prediction algorithm to work onStep 2.1 DataWe can select n (say, 15) PNR numbers from the database which match the characteristics of the PNR being queried. Each of these PNRs will have information about how the waiting list changed over time

Possible ImplementationStep 2.2 Visualization Visualization can be done by plotting a graph where y axis is the waitlist number (y