Abstract
Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and extensively in the setting where moving objects can move freely on the euclidean space. In this paper, we study the problem of clustering trajectories of vehicles whose movement is restricted by the underlying road network. We model relations between these trajectories and road segments as a bipartite graph and we try to cluster its vertices. We demonstrate our approaches on synthetic data and show how it could be useful in inferring knowledge about the flow dynamics and the behavior of the drivers using the road network.
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
Benkert, M., J. Gudmundsson, F. Hübner, and T. Wolle. 2006. Reporting flock patterns. In ESA’06: Proceedings of the 14th conference on annual European symposium, 660–671. London: Springer.
Boullé, M. 2011. Data grid models for preparation and modeling in supervised learning. In Hands-on pattern recognition: Challenges in machine learning, vol. 1, 99–130. Microtome.
Brinkhoff, T. 2002. A framework for generating network-based moving objects. Geoinformatica 6: 153–180.
El Mahrsi, M.K., and F. Rossi. 2012a. Graph-based approaches to clustering network-constrained trajectory data. In Proceedings of the workshop on new frontiers in mining complex patterns (NFMCP 2012), 184–195. Bristol, Royaume-Uni.
El Mahrsi, M.K., and F. Rossi. 2012b. Modularity-based clustering for network-constrained trajectories. In Proceedings of the 20-th European symposium on artificial neural networks, computational intelligence and machine learning (ESANN 2012), 471–476. Bruges, Belgique.
Guigourès, R., M. Boullé, and F. Rossi. 2012. A triclustering approach for time evolving graphs. In ICDM workshops.
Guo, D., S. Liu, and H. Jin. 2010. A graph-based approach to vehicle trajectory analysis. Journal of Location-Based Services 4: 183–199.
Hansen, P., and N. Mladenovic. 2001. Variable neighborhood search: Principles and applications. European Journal of Operational Research 130(3): 449–467.
Hwang, J.-R., H.-Y. Kang, and K.-J. Li. 2005. Spatio-temporal similarity analysis between trajectories on road networks. In ER (workshops), Lecture notes in computer science, 280–289. Springer.
Jeung, H., H.T. Shen, and X. Zhou. 2008. Convoy queries in spatio-temporal databases. In ICDE’08: Proceedings of the 2008 IEEE 24th international conference on data engineering, 1457–1459. Washington: IEEE Computer Society.
Kalnis, P., P. Kalnis, N. Mamoulis, and S. Bakiras. 2005. On discovering moving clusters in spatio-temporal data. In SSTD, 364–381.
Kharrat, A., I.S. Popa, K. Zeitouni, and S. Faiz. 2008. Clustering algorithm for network constraint trajectories. In SDH, Lecture notes in geoinformation and cartography, 631–647. Springer.
Kharrat, A., I.S. Popa, K. Zeitouni, and S. Faiz. 2009. Caractérisation de la densité de trafic et de son évolution à partir de trajectoires d’objets mobiles. In UbiMob, ed. D. Menga and F. Sedes, ACM international conference proceeding series, 33–40. ACM.
Lee, J.-G., J. Han, and K.-Y. Whang. 2007. Trajectory clustering: A partition-and-group framework. In SIGMOD’07: Proceedings of the 2007 ACM SIGMOD international conference on management of data, 593–604. New York: ACM.
Li, Y., J. Han, and J. Yang. 2004. Clustering moving objects. In KDD’04: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, 617–622. New York: ACM.
Liu, W., Z. Wang, and J. Feng. 2008. Continuous clustering of moving objects in spatial networks. In KES’08: Proceedings of the 12th international conference on knowledge-based intelligent information and engineering systems, part II, 543–550. Berlin: Springer.
Lou, Y., C. Zhang, Y. Zheng, X. Xie, W. Wang, and Y. Huang. 2009. Map-matching for low-sampling-rate gps trajectories. In Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, GIS’09, 352–361. New York: ACM.
Meila, M., and J. Shi. 2000. Learning segmentation by Random Walks. In NIPS, 873–879.
Noack, A. and R. Rotta. 2009. Multi-level algorithms for modularity clustering. In Proceedings of the 8th international symposium on experimental algorithms, SEA’09, 257–268. Berlin: Springer.
Roh, G.-P., and S.-W. Hwang. 2010. Nncluster: An efficient clustering algorithm for road network trajectories. In Database systems for advanced applications, Lecture notes in computer science, vol. 5982, 47–61. Springer: Berlin.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
El Mahrsi, M.K., Guigourès, R., Rossi, F., Boullé, M. (2016). Co-Clustering Network-Constrained Trajectory Data. In: Guillet, F., Pinaud, B., Venturini, G., Zighed, D. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 615. Springer, Cham. https://doi.org/10.1007/978-3-319-23751-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-23751-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23750-3
Online ISBN: 978-3-319-23751-0
eBook Packages: EngineeringEngineering (R0)