Context-aware Advertisement Modeling and Applications in Rapid Transit Systems

TL;DR

This paper introduces a context-aware advertisement model for rapid transit systems that leverages behavioral data and agent-based modeling to improve targeted marketing while respecting user privacy.

Contribution

It presents a novel agent-based modeling approach for targeted advertising in transit systems using behavioral and tracking data analysis.

Findings

Effective user segmentation for transit advertising

Enhanced targeting accuracy through behavioral pattern mining

Privacy-preserving data analysis techniques

Abstract

In today's businesses, marketing has been a central trend for growth. Marketing quality is equally important as product quality and relevant metrics. Quality of Marketing depends on targeting the right person. Technology adaptations have been slow in many fields but have captured some aspects of human life to make an impact. For instance, in marketing, recent developments have provided a significant shift toward data-driven approaches. In this paper, we present an advertisement model using behavioral and tracking analysis. We extract users' behavioral data upholding their privacy principle and perform data manipulations and pattern mining for effective analysis. We present a model using the agent-based modeling (ABM) technique, with the target audience of rapid transit system users to target the right person for advertisement applications. We also outline the Overview, Design, and Details concept of ABM.