Track: Statistics and Optimization

Abstract

The “Tatkal" category of tickets in India generally helps last minute travelers to book railway tickets by paying a premium price. The Indian Railways recently made changes to the Tatkal Category of Tickets.

A system of dynamic pricing was introduced where-in in some specific trains, 50% of existing Tatkal Quota of tickets shall be earmarked for a new category called “Premium Tatkal”. Premium Tatkal works on a dynamic model, where the prices of the tickets increase as the demand for them increases (Similar to Flight ticket pricing).

Two criticisms exist about the current pricing scheme of premium tatkal tickets. Firstly, there is a view that many trains do not run to full occupancy due to the exorbitant prices charged. Secondly, even in the revenue front, due to poor occupancy levels, there is a big scope for improvement.

Our paper focuses on designing a new pricing scheme for this dynamic category of tickets so that Revenues can be maximized (which in our case also means a significant improvement in occupancy levels).

The demand data is estimated for one particular train, after which an optimal pricing scheme is determined for the same using Linear Programming. We found a potential revenue increase of about 30%.

This methodology can be easily generalized for each and every other train operated.