MutliCell: Urban Population Modeling based on Multiple Cellphone Networks
文献类型：会议论文
| 作者 | Zhihan Fang; Fan Zhang; Ling Yin; Desheng Zhang |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | Boston |
| 英文摘要 | Exploring cellphone network data has been proved to be a very effective way to understand urban populations because of the high penetration rate of cellphones. However, the state-of-the-art population models driven by cellphone data are typically built upon single cellphone networks, assuming the users in a particular cellphone network used are representative for all residents in the studied city with multiple cellphone networks. This assumption usually does not hold in real world due to strategic spatial coverages and business concentrations of cellphone companies, which lead to data biases, and thus overfitting of resultant population models. To address this issue, we design a model called MultiCell to model real-time urban populations from multiple cellphone networks with two novel techniques: (i) a network realignment technique to integrate individual cell-tower spatial distributions from multiple cellphone networks for finer granular population modeling; (ii) a data fusion technique based on cross-network training to design a population model based on multiple network data. We implement MultiCell in the Chinese city Shenzhen based on three cellphone networks with 10 million active users and their daily data records at 11 thousand cell towers. We evaluate MultiCell by comparing it to the state-of-the-art models driven by single cellphone networks, and the evaluation results show that MultiCell outperforms them by 27% in terms of accuracy. Finally, we cross validate MultiCell with three transportation systems with more than 8 million passengers to investigate its performances. |
| 语种 | 英语 |
| URL标识 | 查看原文 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14061]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 推荐引用方式 GB/T 7714 | Zhihan Fang,Fan Zhang,Ling Yin,et al. MutliCell: Urban Population Modeling based on Multiple Cellphone Networks[C]. 见:. Boston. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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