.png){kind=icon}
返回
Statistical Enrichment Models for Activity Inference from Imprecise Location Data
.png){kind=icon}
PRE
AI
DOI：10.1109/infocom.2019.8737535.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Inference on location data has generated a great amount of interest and there are two data sources. GPS location data from GPS-based endpoint devices have high precision, but suffer from intermittent availability, small coverage of users and extra demand on devices' battery. Telecom mobility data, on the other hand, have the complementary advantages of continuous availability and complete coverage but generally with coarse location accuracy. The focus of this paper is on the development of a location insight system for activity labeling and user segment inference, based only on Telecom mobility data enriched with point-of-interest (POI) data. Specifically, we estimate activity patterns based on Bayesian techniques while infer user segments via a tree-based classification algorithm. We test the performance of our inference system via simulated mobility data and investigate the impact of important factors such as granularity of data. We conclude that imprecise location data, enriched with other source of information, can be used for the development of new generation activity inference models.
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
I
IF:
0
论文数:
29
被引数:
0
机构
引用论文
.png){kind=icon}
Current map-matching algorithms for transport applications: State-of-the art and future research directions当前用于运输应用的地图匹配算法: 最新技术和未来研究方向
OAAI
OAAI分享收藏
查看更多内容