Human mobility characterization from cellular network data

Abstract

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.