Abstract
Understanding human mobility patterns is of great importance to traffic forecasting, urban planning, epidemic spread and many other socioeconomic dynamics covering spatiality and human travel. Based on the records of Beijing subway, we presented a preliminary study of human mobility patterns at urban scale, including return ratio and trip distance. Especially, both linear distance and actual route distance are considered. We found that for a single mode of transportation, the displacement distribution not only decays exponentially, but also has a peak, which represents the characteristics of travel radius (CTR). The CTR of actual route distance is significantly greater than that of linear distance, which indicates that quite of the passengers make detours relative to the linear path when traveling by subway.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Horner, M.W., O’Kelly, M.E.: Embedding economies of scale concepts for hub network design. J. Transp. Geogr. 9, 255–265 (2001)
Um, J., Son, S.W., Lee, S.L., Jeong, H., Kim, B.J., Stanley, H.E.: Scaling laws between population and facility densities. Proc. Natl. Acad. Sci. 106, 14236–14240 (2009)
Balcan, D., Vespignani, A.: Phase transitions in contagion processes mediated by recurrent mobility patterns. Nat. Phys. 7, 581–586 (2011)
Shunjiang, N., Wenguo, W.: Impact of travel patterns on epidemic dynamics in heterogeneous spatial metapopulation networks. Phys. Rev. E 79, 016111 (2009)
Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Collaborative Location and activity recommendations with GPS history data. In: International Conference on World Wide Web, pp.1029–1038 (2010)
Scellato, S., Noulas, A., Mascolo, C.: Exploiting place features in link prediction on location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1046–1054. ACM (2011)
Brockmann, D., Hufnagel, L., Geisel, T.: The scaling laws of human travel. Nature 439, 462–465 (2006)
González, M.C., Hidalgo, C.A., Albert-László, B.: Understanding individual human mobility patterns. Nature 453, 779–782 (2008)
Xin, L., Linus, B., Petter, H.: Predictability of population displacement after the 2010 haiti earthquake. Proc. Natl. Acad. Sci. 109, 11576–11581 (2012)
Yoon, J., Noble, B.D., Liu, M., Kim, M.: Building realistic mobility models from coarse-grained traces. In: Proceedings of the 4th International Conference on Mobile Systems, Applications and Services, pp.177–190. ACM (2006)
Jiang, B., Yin, J., Zhao, S.: Characterizing the human mobility pattern in a large street network. Phys. Rev. E – Stat. Nonlinear, Soft Matter Phys. 80, 1711–1715 (2009)
Yan, X.Y., Han, X.P., Wang, B.H., Zhou, T.: Diversity of individual mobility patterns and emergence of aggregated scaling laws. Sci. Rep. 3, 454 (2013)
Baidu Map. http://api.map.baidu.com/lbsapi/getpoint/index.html
Zhao, B., Ni, S., Yong, N., Ma, X., Shen, S., Ji, X.: A preliminary study on spatial spread risk of epidemics by analyzing the urban subway mobility data. J. Biosci. Med. 3, 15 (2015)
Acknowledgments
The authors deeply appreciate support for this paper by the National Natural Science Foundation of China (Grant No. 91546111 and 71573154), the Research on the development strategy of national public safety science and technology (Grant No. 2014-ZD-02) and the Collaborative Innovation Center of Public Safety.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Yong, N., Ni, S., Shen, S. (2016). A Preliminary Study of Mobility Patterns in Urban Subway. In: Xu, K., Reitter, D., Lee, D., Osgood, N. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2016. Lecture Notes in Computer Science(), vol 9708. Springer, Cham. https://doi.org/10.1007/978-3-319-39931-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-39931-7_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39930-0
Online ISBN: 978-3-319-39931-7
eBook Packages: Computer ScienceComputer Science (R0)