Three years ago, for the first time in the United States, cellular network data in the form of texts, e-mail messages, streaming video and other services exceeded the data from voice calls.
In recent years, there have been studies that explored the use of cellular network data for different purposes.
A recently developed new approach from the cellular network data can also be used for population distribution modeling (Pulselli et al. 2008).
The issue of highly underdetermined link flow based methods in transportation networks is investigated, then solved using the proposed concept of cellpaths for cellular network data.
A new convex optimization framework is developed for the route flow estimation problem from the fusion of vehicle count and cellular network data.
Via a case study of Shanghai, this study shows how cellular network data can be processed to derive useful information, job and housing locations of commuters in particular, for those studies.
Based on cellular network data, this article quantifies and visualizes Shanghai's jobs-housing balance with a much larger sample (n = 6.3 million), finer spatial resolution and greater geographic coverage than ever before.
There is also a great body of literature that have analyzed the use of cellular network data in a wide range of issues, such as incident and traffic management [29]; urban sensing [30]; social networks, security, and privacy issues [31].
In this paper we study the use of fully anonymized and highly aggregate cellular network data, like Call Detail Records (CDRs) to analyze the telecommunication traffic and connect people, locations and events.
Furthermore, to satisfy the growing demand for high-quality multimedia services in next-generation cellular networks, high data rates have to be provided to the end users.
