Abstract
In a study of mobility and urban behaviour, we analyse a longitudinal mobility data set from a sequence mining perspective using a technique that discovers behavioural constraints in sequences of movements between venues. Our contribution is two-fold. First, we propose a methodology to convert aggregated mobility data into insightful patterns. Second, we discover distinctive behavioural patterns in the sequences relative to when in the day they were formed. We analyse sequences of venues as well as sequences of subcategories and categories to discover how people move through Tokyo. The results indicate that our methodology is capable of discovering meaningful behavioural patterns, that can be potentially used to improve urban mobility.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
De Smedt, J., Deeva, G., De Weerdt, J.: Mining behavioral sequence constraints for classification. IEEE Trans. Knowl. Data Eng. (2019)
Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779 (2008)
Long, X., Jin, L., Joshi, J. Exploring trajectory-driven local geographic topics in foursquare. In: Proceedings of the 2012 ACM conference on ubiquitous computing, pp. 927–934. ACM (2012)
Martí, P., Serrano-Estrada, L., Nolasco-Cirugeda, A.: Social media data: challenges, opportunities and limitations in urban studies. Comput. Environ. Urban Syst. 74, 161–174 (2019)
Masuda, N., Porter, M.A., Lambiotte, R.: Random walks and diffusion on networks. Phys. Rep. 716, 1–58 (2017)
Noë, N., Whitaker, R.M., Chorley, M.J., Pollet, T.V.: Birds of a feather locate together? Foursquare checkins and personality homophily. Comput. Hum. Behav. 58, 343–353 (2016)
Noulas, A., Scellato, S., Lathia, N., Mascolo, C., A random walk around the city: new venue recommendation in location-based social networks. In: 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Conference on Social Computing, pp. 144–153. IEEE (2012)
Noulas, A., Scellato, S., Lambiotte, R., Pontil, M., Mascolo, C.: A tale of many cities: universal patterns in human urban mobility. PloS One 7(5), e37027 (2012)
Pianese, F., An, X., Kawsar, F., Ishizuka, H. Discovering and predicting user routines by differential analysis of social network traces. In: 2013 IEEE 14th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1–9. IEEE (2013)
Yue, Y., Lan, T., Yeh, A.G., Li, Q.Q.: Zooming into individuals to understand the collective: a review of trajectory-based travel behaviour studies. Travel. Behav. Soc. 1(2), 69–78 (2014)
Pesic, M., Schonenberg, H., Van der Aalst, W.M.: Declare: full support for loosely-structured processes. In: 11th IEEE International Enterprise Distributed Object Computing Conference, p. 287. IEEE, October 2007
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Deeva, G., De Smedt, J., De Weerdt, J., Óskarsdóttir, M. (2020). Mining Behavioural Patterns in Urban Mobility Sequences Using Foursquare Check-in Data from Tokyo. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 882. Springer, Cham. https://doi.org/10.1007/978-3-030-36683-4_74
Download citation
DOI: https://doi.org/10.1007/978-3-030-36683-4_74
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36682-7
Online ISBN: 978-3-030-36683-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)