Spatiotemporal data, and more specifically origin–destination matrices, are critical inputs to
mobility studies for transportation planning and urban management purposes. Traditionally …

Understanding and correctly modeling urban mobility is a crucial issue for the development
of smart cities. The estimation of individual trips from mobile phone positioning data (ie, call …

Mobile phone data collected by network operators can provide fundamental insights into
individual and aggregate mobility of people, at unprecedented spatiotemporal scales …

In this study, with Estonia as an example, we established an approach based on Hidden
Markov Model to extract large-scale commuting patterns at different geographical levels …

Due to the ubiquity of mobile phones, mobile phone network data (eg, Call Detail Records,
CDR; and cellular signaling data, CSD), which are collected by mobile telecommunication …

Cellular networks' signaling data provide means for analyzing the efficiency of an underlying
transportation system and assisting the formulation of models to predict its future use. This …

Mobile phone operators produce enormous amounts of data. In this paper we present
applications performed with a dataset (communication events+ handover and Location Area …

Mobile phone operators produce enormous amounts of data. In this paper we present
applications performed with a dataset (probe data) collected by the operator Orange in 2017 …

Fast urbanization generates increasing amounts of travel flows, urging the need for efficient
transport planning policies. In parallel, mobile phone data have emerged as the largest …

Properly extracting patterns of individual mobility with high resolution data sources such as
the one extracted from smartphone applications offers important opportunities. Potential …