Advertisement Revenue and Exposure Optimization for Digital Screens in Subway Networks Using Smart Card Data

The digital screens installed inside subway stations generate advertisement revenue by displaying advertisements to passengers. Digital advertisements are increasingly preferred by advertisers because of the high traffic volumes in subway. This paper designs a digital advertising system based on the historical demand information extracted from the smart card data. To this end, we propose a method of designing advertisement products tailored to the digital screens in subway. Next, we consider a reservation system for the designed products with an objective of maximizing the advertisement revenue. The linear programming model is used for the reservation control. If the reservation requests arrive with a Poisson process, the dynamic programming model is used for a more accurate control. The final problem to address is how to schedule the accepted reservations for a maximum exposure to subway passengers. The scheduling problem is the traditional knapsack problem, and the simple greedy method is optimal. Numerical study is performed using our real-life smart card data from Daegu, South Korea. Our data set does not have the demographic information. For the case where this information is available, this paper describes the model for the location-based targeted advertising.