  Understanding human mobility is essential for many fields, including
transportation planning. Currently, surveys are the primary source for such
analysis. However, in the recent past, many researchers have focused on Call
Detail Records (CDR) for identifying travel patterns. CDRs have shown
correlation to human mobility behavior. However, one of the main issues in
using CDR data is that it is difficult to identify the precise location of the
user due to the low spacial resolution of the data and other artifacts such as
the load sharing effect. Existing approaches have certain limitations. Previous
studies using CDRs do not consider the transmit power of cell towers when
localizing the users and use an oversimplified approach to identify load
sharing effects. Furthermore, they consider the entire population of users as
one group neglecting the differences in mobility patterns of different segments
of users. This research introduces a novel methodology to user position
localization from CDRs through improved detection of load sharing effects, by
taking the transmit power into account, and segmenting the users into distinct
groups for the purpose of learning any parameters of the model. Moreover, this
research uses several methods to address the existing limitations and validate
the generated results using nearly 4 billion CDR data points with travel survey
data and voluntarily collected mobile data.
