MobHet: Predicting human mobility using heterogeneous data sources

•We propose MobHet, a new family of data-driven models for human mobility prediction.•MobHet exploits region popularity, inter-region transitions and user contacts.•MobHet is evaluated in various scenarios of homogeneous and heterogeneous data.•Evaluation uses large real-world datasets of mobile pho...

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Bibliographic Details
Published in:Computer communications 2016-12, Vol.95, p.54-68
Main Authors: Silveira, Lucas M., de Almeida, Jussara M., Marques-Neto, Humberto T., Sarraute, Carlos, Ziviani, Artur
Format: Article
Language:English
Subjects:
Applications programs
Computer simulation
Contact
Data sources
Feature extraction
Georeferenced data
Graphs
Human mobility prediction
Mathematical models
Mobile phone data
Robustness
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Summary:•We propose MobHet, a new family of data-driven models for human mobility prediction.•MobHet exploits region popularity, inter-region transitions and user contacts.•MobHet is evaluated in various scenarios of homogeneous and heterogeneous data.•Evaluation uses large real-world datasets of mobile phone calls and tweets.•Results attest MobHet effectiveness when compared to single-source baseline models. The literature is rich in mobility models that aim at predicting human mobility. Yet, these models typically consider only a single kind of data source, such as data from mobile calls or location data obtained from GPS and web applications. Thus, the robustness and effectiveness of such data-driven models from the literature remain unknown when using heterogeneous types of data. In contrast, this paper proposes a novel family of data-driven models, called MobHet, to predict human mobility using heterogeneous data sources. Our proposal is designed to use a combination of features capturing the popularity of a region, the frequency of transitions between regions, and the contacts of a user, which can be extracted from data obtained from various sources, both separately and conjointly. We evaluate the MobHet models, comparing them among themselves and with two single-source data-driven models, namely SMOOTH and Leap Graph, while considering different scenarios with single as well as multiple data sources. Our experimental results show that our best MobHet model produces results that are better than or at least comparable to the best baseline in all considered scenarios, unlike the previous models whose performance is very dependent on the particular type of data used. Our results thus attest the robustness of our proposed solution to the use of heterogeneous data sources in predicting human mobility.
ISSN:0140-3664
1873-703X
DOI:10.1016/j.comcom.2016.04.013