In this study, with Singapore as an example, we demonstrate how we can use mobile phone
call detail record (CDR) data, which contains millions of anonymous users, to extract …

Understanding individual daily activity patterns is essential for travel demand management
and urban planning. This research introduces a new method to infer individuals' activities …

Understanding residents' daily activity chains provides critical support for various
applications in transportation, public health and many other related fields. Recently, mobile …

With the rapid growth of cell phone networks during the last decades, call detail records
(CDR) have been used as approximate indicators for large scale studies on human and …

Massive and passive data such as cell phone traces provide samples of the whereabouts
and movements of individuals. These are a potential source of information for models of …

Advancements of information, communication and location-aware technologies have made
collections of various passively generated datasets possible. These datasets provide new …

In this work, we present three classes of methods to extract information from triangulated
mobile phone signals, and describe applications with different goals in spatiotemporal …

Passively generated mobile phone dataset is emerging as a new data source for research in
human mobility patterns. Information on individuals' trajectories is not directly available from …

Individual human travel patterns captured by mobile phone data have been quantitatively
characterized by mathematical models, but the underlying activities which initiate the …

Due to the pervasiveness of mobile devices, a vast amount of geolocated data is generated,
which allows us to gain deep insight into human behavior. Among other data sources, the …