Paper Info
Title
Everyonecounts: Data-Driven Digital Advertising With Uncertain Demand Model In Metro Networks
Abstract
Nowadays most metro advertising systems schedule advertising slots on digital advertising screens to achieve the maximum exposure to passengers by exploring passenger demand models. However, our empirical results show that these passenger demand models experience uncertainty at fine temporal granularity (e.g., per min). As a result, for fine-grained advertisements (shorter than one minute), a scheduling based on these demand models cannot achieve the maximum advertisement exposure. To address this issue, we propose an online advertising approach, called EveryoneCounts, based on an uncertain passenger demand model. It combines coarse-grained statistical demand modeling and fine-grained bayesian demand modeling by leveraging real-time card-swiping records along with both passenger mobility patterns and travel periods within metro systems. Based on this uncertain demand model, it schedules advertising time online based on robust receding horizon control to maximize the advertisement exposure. We evaluate the proposed approach based on an one-month sample from our 530 GB real-world metro fare dataset with 16 million cards. The results show that our approach provides a 61.5% lower traffic prediction error and 20% improvement on advertising efficiency on average.
Year
Venue
Field
2015
PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIG DATA
Demand modeling,Data modeling,Data-driven,Advertising,Scheduling (computing),Computer science,Online advertising,Schedule,Market research,Bayesian probability
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
12
8
Name
Order
Citations
PageRank
Desheng Zhang135645.96
Riiobing Jiang200.34
Shiiai Wang300.34
Yanmin Zhu41767142.50
Bo Yang536140.37
Jian Cao64111.40
Fan Zhang713314.80
Tian He86869447.17