Human Mobility Characterization from Cellular Network Data

被引：202

作者：

Becker, Richard ^{[1
]}

Caceres, Ramon ^{[1
]}

Hanson, Karrie ^{[1
]}

Isaacman, Sibren ^{[2
]}

Loh, Ji Meng ^{[3
]}

Martonosi, Margaret ^{[4
]}

Rowland, James ^{[1
]}

Urbanek, Simon ^{[1
]}

Varshavsky, Alexander ^{[1
]}

Volinsky, Chris ^{[1
]}

机构：

[1] AT&T Labs Res, Florham Pk, NJ USA

[2] Loyola Univ Maryland, Baltimore, MD USA

[3] New Jersey Inst Technol, Newark, NJ 07102 USA

[4] Princeton Univ, Princeton, NJ 08544 USA

来源：

COMMUNICATIONS OF THE ACM | 2013年 / 56卷 / 01期

基金：

美国国家科学基金会;

关键词：

Compendex;

D O I：

10.1145/2398356.2398375

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

AN IMPROVED UNDERSTANDING of human-mobility patterns would yield insight into a variety of important societal issues. For example, evaluating the effect of human travel on the environment depends on knowing how large populations move about in their daily lives. Likewise, understanding the spread of a disease requires a clear picture of how humans move and interact. Other examples abound in such fields as urban planning, where knowing how people come and go can help determine where to deploy infrastructure and how to reduce traffic congestion. human-mobility researchers traditionally rely on expensive data-collection methods (such as surveys and direct observation) to glimpse the way people move about. This cost typically results in infrequent data collection or small sample sizes; for example © 2013 ACM 0001-0782/13/01.

引用

页码：74 / 82

页数：9