An Empirical Study of Human Mobility Patterns

  • Douglas do Couto Teixeira UFMG
  • Jussara M. Almeida UFMG


This paper documents our efforts towards understanding which factors are more relevant in human mobility prediction. Our work is divided into two phases. First, we characterize a dataset consisting of more than 200,000 user check-ins in the Foursquare social network, inferring important patterns in human mobility. Second, we use factorial design to quantify the importance of several types of contextual information in human mobility prediction. Our results show that the proximity of the users possible next check-in to his or her home and work location are the most important factors (among the ones we analyzed) to be used by mobility prediction models.


Cho, E., Myers, S. A., and Leskovec, J. (2011). Friendship and mobility: User moveIn Proceedings of the 17th ACM SIGKDD ment in location-based social networks. International Conference on Knowledge Discovery and Data Mining, KDD ’11, pages 1082–1090, New York, NY, USA. ACM.

Dong, W., Dufeld, N., Ge, Z., Lee, S., and Pang, J. (2013). Modeling cellular user mobility using a leap graph. In Proceedings of the 14th International Conference on Passive and Active Measurement, PAM’13, pages 53–62, Berlin, Heidelberg. SpringerVerlag.

Hasan, S., Zhan, X., and Ukkusuri, S. V. (2013). Understanding urban human activity and mobility patterns using large-scale location-based data from online social media. In Proceedings of the 2Nd ACM SIGKDD International Workshop on Urban Computing, UrbComp ’13, pages 6:1–6:8, New York, NY, USA. ACM.

Herrera, J. C., Work, D. B., Herring, R., Ban, X. J., Jacobson, Q., and Bayen, A. M. (2010). Evaluation of trafc data obtained via gps-enabled mobile phones: The mobile century eld experiment. Transportation Research Part C: Emerging Technologies, 18(4):568 – 583.

Jain, R. (1991). The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling. Wiley.

Jiang, S., Fiore, G. A., Yang, Y., Ferreira, Jr., J., Frazzoli, E., and González, M. C. (2013). A review of urban computing for mobile phone traces: Current methods, challenges and opportunities. In Proceedings of the 2Nd ACM SIGKDD International Workshop on Urban Computing, UrbComp ’13, pages 2:1–2:9, New York, NY, USA. ACM.

Jurdak, R., Zhao, K., Liu, J., AbouJaoude, M., Cameron, M., and Newth, D. (2015). Understanding human mobility from twitter. PLOS ONE, 10(7):1–16.

L. Silveira, J. Almeida, H. N. A. Z. (2015). Mobdatu: Um novo modelo de previsao de mobilidade humana para dados heterogeneos. Simposio Brasileiro de Redes de Computadores.

Lu, X., Wetter, E., Bharti, N., Tatem, A. J., and Bengtsson, L. (2013). Approaching the limit of predictability in human mobility. Scientic Reports, 3.

Ma, S., Zheng, Y., and Wolfson, O. (2013). T-share: A large-scale dynamic taxi ridesharing service. In Data Engineering (ICDE), 2013 IEEE 29th International Conference on, pages 410–421.

Munjal, A., Camp, T., and Navidi, W. C. (2011). Smooth: A simple way to model human In Proceedings of the 14th ACM International Conference on Modeling, mobility. Analysis and Simulation of Wireless and Mobile Systems, MSWiM ’11, pages 351– 360, New York, NY, USA. ACM.

Silveira, L. M., Almeida, J. M., Marques-Neto, H. T., Sarraute, C., and Ziviani, A. (2016). Mobhet: Predicting human mobility using heterogeneous data sources. Computer Communications, 95:54–68.

Song, C., Qu, Z., Blumm, N., and Barabási, A.-L. (2010). Limits of predictability in human mobility. Science, 327(5968):1018–1021.

Toole, J. L., Ulm, M., González, M. C., and Bauer, D. (2012). Inferring land use from mobile phone activity. In Proceedings of the ACM SIGKDD International Workshop on Urban Computing, UrbComp ’12, pages 1–8, New York, NY, USA. ACM.

Wang, H., Xu, F., Li, Y., Zhang, P., and Jin, D. (2015). Understanding mobile trafc In Proceedings of the patterns of large scale cellular towers in urban environment. 2015 Internet Measurement Conference, IMC ’15. ACM.

Yang, D., Zhang, D., Zheng, V. W., and Yu, Z. (2015). Modeling user activity preference IEEE Transactions on by leveraging user spatial temporal characteristics in lbsns. Systems, Man, and Cybernetics: Systems, 45(1):129–142.

Zheng, Y., Capra, L., Wolfson, O., and Yang, H. (2014). Urban computing: Concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol., 5(3):38:1–38:55.

Zheng, Y. and Xie, X. (2011). Learning travel recommendations from user-generated gps traces. ACM Trans. Intell. Syst. Technol., 2(1):2:1–2:29.
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TEIXEIRA, Douglas do Couto; ALMEIDA, Jussara M.. An Empirical Study of Human Mobility Patterns. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 36. , 2018, Campos do Jordão. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 127-140. ISSN 2177-9384. DOI:

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