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

Miguel Picornell1, Tomás Ruiz2, Maxime Lenormand3, José J. Ramasco3, Thibaut Dubernet4 and Enrique Frías-Martínez5

1Nommon Solutions and Technologies, calle Cañas 8, 28043 Madrid, Spain.
2Universitat Politècnica de València, València, Spain.
3Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Palma de Mallorca, Spain.
4Institute for Transport Planning and Systems (IVT), ETH Zurich, 8093 Zurich, Switzerland.
5Telefónica Research, 28050 Madrid, Spain.

(April 2015)

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\u2019 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.