基于手机上网记录数据的个体相遇预测

李倩; 江昊; 杨锦涛

doi:10.11959/j.issn.1000-0801.2017249

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基于手机上网记录数据的个体相遇预测

研究与开发 | 更新时间：2024-06-05

- 基于手机上网记录数据的个体相遇预测
- Individual encounter prediction based on mobile internet record data
- 电信科学 2017年33卷第10期页码：115-123
- 作者机构：
- 作者简介：
  
  [ "李倩（1992-），女，武汉大学电子信息学院硕士生，主要研究方向为大数据分析与挖掘、移动互联网等。" ]
  [ "江昊（1976-），男，武汉大学电子信息学院教授、博士生导师，主要研究方向为大数据分析与挖掘、移动互联网、无线网络、空间综合信息网络等。" ]
  [ "杨锦涛（1993-），男，武汉大学电子信息学院硕士生，主要研究方向为大数据分析与挖掘、移动互联网、物联网等。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61371126);国家高技术研究发展计划（“863”计划）基金资助项目;The National High Technology Research and Development Program of China(863 Program)(2014AA01A707);武汉应用基础研究计划资助项目;The Applied Basic Research Programs of Wuhan(2014010101010026)
- DOI：10.11959/j.issn.1000-0801.2017249
  中图分类号： TP393
- 网络出版日期：2017-10，
  
  纸质出版日期：2017-10-15
- 稿件说明：
移动端阅览
李倩, 江昊, 杨锦涛. 基于手机上网记录数据的个体相遇预测[J]. 电信科学, 2017,33(10):115-123.

Qian LI, Hao JIANG, Jintao YANG. Individual encounter prediction based on mobile internet record data[J]. Telecommunications science, 2017, 33(10): 115-123.
李倩, 江昊, 杨锦涛. 基于手机上网记录数据的个体相遇预测[J]. 电信科学, 2017,33(10):115-123. DOI： 10.11959/j.issn.1000-0801.2017249.

Qian LI, Hao JIANG, Jintao YANG. Individual encounter prediction based on mobile internet record data[J]. Telecommunications science, 2017, 33(10): 115-123. DOI： 10.11959/j.issn.1000-0801.2017249.

摘要

随着反映个体位置信息的数据大量涌现，人类移动行为的研究引起了业界学者的广泛关注。针对个体相遇预测问题，使用用户手机终端上网产生的会话数据进行分析。首先基于用户相遇关系进行复杂网络建模，然后分析了网络拓扑结构特征，同时引入了用户移动性特征和上网行为特征，构造基于随机森林的预测模型，进行个体相遇预测。实验结果表明，相比于传统的网络拓扑结构特征，通过引入用户移动性特征和上网行为特征，能够显著地提升预测性能。

Abstract

Studies on human movement behavior have drawn much attention with the availability of unprecedented amount of records with high accuracy involving individuals’ trajectories.The encounter prediction problem based on the session data generated by users’ mobile terminal was studied when users accessed the internet for data usage.Firstly

the network based on the encounter relations between users was constructed.Secondly

the network topology features were analyzed and user mobility characteristics and user internet behavior characteristics were introduced.Finally

the prediction model based on random forest was applied.The experimental results show that compared with the traditional network topology features

the prediction performance can be significantly improved by introducing the user mobility characteristics and user internet behavior characteristics.

关键词

Keywords

references

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