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Exploring the potential of phone call data to characterize the relationship between social network and travel behavior

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Abstract

Social network contacts have significant influence on individual travel behavior. However, transport models rarely consider social interaction. One of the reasons is the difficulty to properly model social influence based on the limited data available. Non-conventional, passively collected data sources, such as Twitter, Facebook or mobile phones, provide large amounts of data containing both social interaction and spatiotemporal information. The analysis of such data opens an opportunity to better understand the influence of social networks on travel behavior. The main objective of this paper is to examine the relationship between travel behavior and social networks using mobile phone data. A huge dataset containing billions of registers has been used for this study. The paper analyzes the nature of co-location events and frequent locations shared by social network contacts, aiming not only to provide understanding on why users share certain locations, but also to quantify the degree in which the different types of locations are shared. Locations have been classified as frequent (home, work and other) and non-frequent. A novel approach to identify co-location events based on the intersection of users’ mobility models has been proposed. Results show that other locations different from home and work are frequently associated to social interaction. Additionally, the importance of non-frequent locations in co-location events is shown. Finally, the potential application of the data analysis results to improve activity-based transport models and assess transport policies is discussed.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007–2013 under grant agreement n° 318367 (EUNOIA project) and n° 611307 (INSIGHT project). The work of ML has been funded under the PD/004/2013 project, from the Conselleria de Educación, Cultura y Universidades of the Government of the Balearic Islands and from the European Social Fund through the Balearic Islands ESF operational program for 2013–2017.

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Authors and Affiliations

  1. Nommon Solutions and Technologies, Madrid, Spain

    Miguel Picornell

  2. Universitat Politècnica de València, València, Spain

    Tomás Ruiz

  3. Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), 07122, Palma de Mallorca, Spain

    Maxime Lenormand & José J. Ramasco

  4. Institute for Transport Planning and Systems (IVT), ETH Zurich, 8093, Zurich, Switzerland

    Thibaut Dubernet

  5. Telefonica Research, Madrid, Spain

    Enrique Frías-Martínez

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  1. Miguel Picornell
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  2. Tomás Ruiz
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  3. Maxime Lenormand
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  4. José J. Ramasco
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  5. Thibaut Dubernet
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Correspondence to Miguel Picornell.

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Picornell, M., Ruiz, T., Lenormand, M. et al. Exploring the potential of phone call data to characterize the relationship between social network and travel behavior. Transportation 42, 647–668 (2015). https://doi.org/10.1007/s11116-015-9594-1

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