Event Title

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.

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Apr 22nd, 8:50 AM Apr 22nd, 9:05 AM

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.