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Understanding population displacements on location-based call records using road data

Related publications (33)

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Motorway Traffic Risks Identification Model - MyTRIM Methodology and Application

Minh Hai Pham

Road traffic crashes are becoming increasing concerns in many countries. In Europe, many efforts have been devoted to improve road traffic safety yet the important target of halving the number of yearly road deaths in 2010 could not be achieved in many Eur ...

EPFL2011

Probabilistic Mining of Socio-Geographic Routines from Mobile Phone Data

Daniel Gatica-Perez, Katayoun Farrahi

There is relatively little work on the investigation of large-scale human data in terms of multimodality for human activity discovery. In this paper we suggest that human interaction data, or human proximity, obtained by mobile phone Bluetooth sensor data, ...

2010

By their apps you shall understand them: mining large-scale patterns of mobile phone usage

Daniel Gatica-Perez

Mobile phones are becoming more and more widely used nowadays, and people do not use the phone only for communication: there is a wide variety of phone applications allowing users to select those that fit their needs. Aggregated over time, application usag ...

2010

Estimating and Learning the Trajectory of Mobile Phones

Mahdad Hosseini Kamal

This project is based on the ongoing data collection campaign by Nokia Re- search Center-Lausanne. We use location data sampled everyday by mobile phones in the campaign to estimate position of the participants. It is with emerging mobile systems that comb ...

2010

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Camera phones are ubiquitous, and consumers have been adopting them faster than any other technology in modern history. When connected to a network, though, they are capable of more than just picture taking: Suddenly, they gain access to the power of the c ...

SPIE2010

Tracking of Mobile Devices Through Bluetooth Contacts

Jean-Yves Le Boudec, Nikodin Ristanovic, Dang Khoa Tran

We investigate if it is possible to reconstruct a mobile phone’s mobility using its Bluetooth contacts with other mobile devices, some of which are equipped with GPS receivers. Our data mining analysis, based on two different data sets, shows that in certa ...

ACM2010

Learning and Predicting Multimodal Daily Life Patterns from Cell Phones

Daniel Gatica-Perez, Katayoun Farrahi

In this paper, we investigate the multimodal nature of cell phone data in terms of discovering recurrent and rich patterns in people's lives. We present a method that can discover routines from multiple modalities (location and proximity) jointly modeled, ...

2009

Discovering Human Routines from Cell Phone Data with Topic Models

Daniel Gatica-Perez, Katayoun Farrahi

We present a framework to automatically discover people's routines from information extracted by cell phones. The framework is built from a probabilistic topic model learned on novel bag type representations of activity-related cues (location, proximity an ...

2008

Discovering Human Routines from Cell Phone Data with Topic Models

Daniel Gatica-Perez, Katayoun Farrahi

IDIAP2008

Daily Routine Classification from Mobile Phone Data

Daniel Gatica-Perez, Katayoun Farrahi

The automatic analysis of real-life, long-term behavior and dynamics of individuals and groups from mobile sensor data constitutes an emerging and challenging domain. We present a framework to classify people's daily routines (defined by day type, and by g ...

2008