Data-Driven Digital Advertising with Uncertain Demand Model in Metro Networks

作者：Jiang, Ruobing; Feng, Zhenni; Zhang, Desheng^*; Wang, Shuai; Zhu, Yanmin; Zhang, Fan; He, Tian

来源：IEEE Transactions on Big Data, 2021, 7(2): 313-326.

DOI：10.1109/TBDATA.2017.2725913

• Advertising
• Data models
• Predictive models
• Schedules
• Real-time systems
• Optimization
• Big Data
• Digital advertising
• uncertain passenger demand
• data-driven traffic volume prediction

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 FineUDM, based on the uncertain passenger demand modeling for both entering passengers and exiting passengers. FineUDM combines coarse-grained statistical demand modeling and fine-grained real-time demand modeling by leveraging historical passenger demands, real-time card-swiping records, and passenger mobility patterns. 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 percent lower traffic prediction error and 20 percent improvement on advertising efficiency on average.

• 出版日期2021-6-1
• 单位上海交通大学; 中国科学院深圳先进技术研究院