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 realtime 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.
|Original language||English (US)|
|Title of host publication||Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015|
|Editors||Feng Luo, Kemafor Ogan, Mohammed J. Zaki, Laura Haas, Beng Chin Ooi, Vipin Kumar, Sudarsan Rachuri, Saumyadipta Pyne, Howard Ho, Xiaohua Hu, Shipeng Yu, Morris Hui-I Hsiao, Jian Li|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|State||Published - Dec 22 2015|
|Event||3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States|
Duration: Oct 29 2015 → Nov 1 2015
|Name||Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015|
|Other||3rd IEEE International Conference on Big Data, IEEE Big Data 2015|
|Period||10/29/15 → 11/1/15|
Bibliographical noteFunding Information:
This work was supported in part by the Shanghai Recruitment Program of Global Experts, NSF CNS-1446640, NSFC 61573245, and NSFC 61174127.