Efficiently Targeted Billboard Advertising Using Crowdsensing Vehicle Trajectory Data

Different from online promotion, the outdoor billboard advertising industry suffers from a lack of audience-targeted delivery and quantitative dissemination evaluation, which undermine its impact in practice and hinder it from fast development. To bridge this gap, in this paper, we leverage crowdsensing vehicle trajectory data to empower audience-targeted billboard advertising. More specifically, by integrating the information of mobility transition, traffic conditions (traffic volume and average speed), and advertisement semantic topics, we propose a quantitative model to quantify advertisement influence spread, with a special consideration on influence overlapping among mobile users. Based on it, an influence maximization-targeted billboard advertising problem is formulated to find k advertising units over spatiotemporal dimensions, with the goal of maximizing the total expected advertisement influence spread. To tackle the efficiency issue for solving large combinatorial optimization problem, we employ a divide-and-conquer mechanism, and propose a utility evaluation-based optimal searching approach. Extensive experiments on real-world taxicab trajectories clearly validate the effectiveness and efficiency of our proposed approach.