Analyzing Location-Based Advertising Pricing
Session Number
Project ID: CMPS 1
Advisor(s)
Dr. Randall Berry; Northwestern University
Discipline
Computer Science
Start Date
22-4-2020 8:50 AM
End Date
22-4-2020 9:05 AM
Abstract
Vehicle service providers can display commercial ads in their vehicles based on passengers’ origins and destinations to create a new revenue stream. In this work, we study a vehicle service provider who can generate different ad revenues when displaying ads on different arcs (i.e., origin-destination pairs). The provider needs to ensure the vehicle flow balance at each location, which makes it challenging to analyze the provider’s vehicle assignment and pricing decisions for different arcs. For example, the provider’s price for its service on an arc depends on the ad revenues on other arcs as well as on the arc in question. We capitalize on theory and assumptions that this traffic network corresponds to an electrical network and, to tackle this problem, develop modules within Python that simulate the outlined algorithmic structure. This code enables us to determine the resulting equilibrium for a network with one or two links with potential for a fully multi-linked system. We later hope to investigate the performance of our advertiser selection strategy based on a real-world dataset.
Analyzing Location-Based Advertising Pricing
Vehicle service providers can display commercial ads in their vehicles based on passengers’ origins and destinations to create a new revenue stream. In this work, we study a vehicle service provider who can generate different ad revenues when displaying ads on different arcs (i.e., origin-destination pairs). The provider needs to ensure the vehicle flow balance at each location, which makes it challenging to analyze the provider’s vehicle assignment and pricing decisions for different arcs. For example, the provider’s price for its service on an arc depends on the ad revenues on other arcs as well as on the arc in question. We capitalize on theory and assumptions that this traffic network corresponds to an electrical network and, to tackle this problem, develop modules within Python that simulate the outlined algorithmic structure. This code enables us to determine the resulting equilibrium for a network with one or two links with potential for a fully multi-linked system. We later hope to investigate the performance of our advertiser selection strategy based on a real-world dataset.