Abstract
The traffic networks reflect the pulse and structure of a city and shows some dynamic characteristic. Previous research in mining structure from networks mostly focus on static networks and fail to exploit the temporal patterns. In this paper, we aim to solve the problem of discovering the urban spatio-temporal structure from time-evolving traffic networks. We model the time-evolving traffic networks into a 3-order tensor, each element of which indicates the volume of traffic from i-th origin area to j-th destination area in k-th time domain. Considering traffic data and urban contextual knowledge together, we propose a regularized Non-negative Tucker Decomposition (rNTD) method, which discovers the spatial clusters, temporal patterns and relations among them simultaneously. Abundant experiments are conducted in a large dataset collected from Beijing. Results show that our method outperforms the baseline method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010)
Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.-L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)
Jonsson, P.F., Cavanna, T., Zicha, D., Bates, P.A.: Cluster analysis of networks generated through homology: automatic identification of important protein communities involved in cancer metastasis. BMC Bioinformatics 7(1), 2 (2006)
Kim, M., Leskovec, J.: Nonparametric multi-group membership model for dynamic networks. In: Advances in Neural Information Processing Systems, pp. 1385–1393 (2013)
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Review 51(3), 455–500 (2009)
Lee, D.D., Seung, S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
McAuley, J.J., Leskovec, J.: Learning to discover social circles in ego networks. In: NIPS, vol. 272, pp. 548–556 (2012)
Mørup, M., Hansen, L.K., Arnfred, S.M.: Algorithms for sparse nonnegative tucker decompositions. Neural Computation 20(8), 2112–2131 (2008)
Peng, C., Jin, X., Wong, K.-C., Shi, M., Liò, P.: Collective human mobility pattern from taxi trips in urban area. PloS One 7(4), e34487 (2012)
Sun, J., Tao, D., Faloutsos, C.: Beyond streams and graphs: dynamic tensor analysis. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 374–383. ACM (2006)
Wang, F., Li, T., Wang, X., Zhu, S., Ding, C.: Community discovery using nonnegative matrix factorization. Data Mining and Knowledge Discovery 22(3), 493–521 (2011)
Xu, Y., Yin, W.: A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion. SIAM Journal on Imaging Sciences 6(3), 1758–1789 (2013)
Yang, J., McAuley, J., Leskovec, J.: Community detection in networks with node attributes. In: 2013 IEEE 13th International Conference on Data Mining, pp. 1151–1156 (2013)
Yuan, J., Zheng, Y., Xie, X.: Discovering regions of different functions in a city using human mobility and pois. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 186–194. ACM (2012)
Zhang, F., Wilkie, D., Zheng, Y., Xie, X.: Sensing the pulse of urban refueling behavior. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 13–22. ACM (2013)
Zheng, Y., Liu, F., Hsieh, H.-P.: U-air: when urban air quality inference meets big data. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1436–1444. ACM (2013)
Zheng, Y., Liu, Y., Yuan, J., Xie, X.: Urban computing with taxicabs. In: Proceedings of the 13th International Conference on Ubiquitous Computing, pp. 89–98. ACM (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, J., Gao, F., Cui, P., Li, C., Xiong, Z. (2014). Discovering Urban Spatio-temporal Structure from Time-Evolving Traffic Networks. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8709. Springer, Cham. https://doi.org/10.1007/978-3-319-11116-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-11116-2_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11115-5
Online ISBN: 978-3-319-11116-2
eBook Packages: Computer ScienceComputer Science (R0)