Abstract
Within the mobility mining discipline, several solutions for the classification of spatio-temporal trajectories have been proposed. However, they usually do not fully consider the particularities of trajectories from human-generated data like online social networks. For that reason, this work introduces a novel classifier based on Support Vector Machines (SVM), which fits the low resolution of this type of geographic data. This solution is applied in a use case for the detection of tourist mobility exhibiting quite promising results.
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
Bellavista, P., Montanari, R., Das, S.K.: Mobile social networking middleware: a survey. Pervasive Mob. Comput. 9(4), 437–453 (2013)
Renso, C., Stefano Spaccapietra, E.Z.: Mobility Data - Modeling, Management, and Understanding. Cambridge University Press, Cambridge (2013)
Comito, C., Falcone, D., Talia, D.: Mining human mobility patterns from social geo-tagged data. Pervasive Mob. Comput. 33, 91–107 (2016)
Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. KDD 96, 226–231 (1996)
Girardin, F., Calabrese, F., Fiore, F.D., Ratti, C., Blat, J.: Digital footprinting: uncovering tourists with user-generated content. IEEE Pervasive Comput. 7(4), 36–43 (2008)
Han, J., Lee, H.: Adaptive landmark recommendations for travel planning: personalizing and clustering landmarks using geo-tagged social media. Pervasive Mob. Comput. 18, 4–17 (2015)
Hu, Y., Gao, S., Janowicz, K., Yu, B., Li, W., Prasad, S.: Extracting and understanding urban areas of interest using geotagged photos. Comput. Environ. Urban Syst. 54, 240–254 (2015)
Lee, J.G., Han, J., Li, X.: Trajectory outlier detection: a partition-and-detect framework. In: 2008 IEEE 24th International Conference on Data Engineering, pp. 140–149, April 2008
Lee, J.G., Han, J., Li, X., Gonzalez, H.: Traclass: trajectory classification using hierarchical region-based and trajectory-based clustering. Proc. VLDB Endow. 1(1), 1081–1094 (2008)
Lee, J.G., Han, J., Whang, K.Y.: Trajectory clustering: a partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, SIGMOD 2007, pp. 593–604. ACM, New York (2007)
Li, X., Han, J., Lee, J.-G., Gonzalez, H.: Traffic density-based discovery of hot routes in road networks. In: Papadias, D., Zhang, D., Kollios, G. (eds.) SSTD 2007. LNCS, vol. 4605, pp. 441–459. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73540-3_25
Pelekis, N., Theodoridis, Y.: Mobility data mining and knowledge discovery. In: Pelekis, N., Theodoridis, Y. (eds.) Mobility Data Management and Exploration, pp. 143–167. Springer, New York (2014). https://doi.org/10.1007/978-1-4939-0392-4_7
Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)
Terroso-Sáenz, F., Cuenca-Jara, J., González-Vidal, A., Skarmeta, A.F.: Human mobility prediction based on social media with complex event processing. Int. J. Distrib. Sens. Netw. 12(9), 5836392 (2016)
Thomee, B., Shamma, D.A., Friedland, G., Elizalde, B., Ni, K., Poland, D., Borth, D., Li, L.J.: YFCC100M: the new data in multimedia research. Commun. ACM 59(2), 64–73 (2016)
Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. 6(3), 29:1–29:41 (2015)
Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.Y.: Understanding mobility based on GPS data. In: Proceedings of the 10th International Conference on Ubiquitous Computing, UbiComp 2008, pp. 312–321. ACM, New York (2008)
Zheng, Y., Xie, X.: Learning travel recommendations from user-generated GPS traces. ACM Trans. Intell. Syst. Technol. 2(1), 2:1–2:29 (2011)
Zhu, Y., Zheng, Y., Zhang, L., Santani, D., Xie, X., Yang, Q.: Inferring taxi status using GPS trajectories. Technical report, May 2012. arXiv:1205.4378
Acknowledgements
This work has been sponsored by the Spanish Ministry of Economy and Competitiveness through the PERSEIDES project (contract TIN2017-86885-R) and by the European Union under the framework of the H2020 IoTCrawler project (contract 779852).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Cuenca-Jara, J., Terroso-Saenz, F., Sanchez-Iborra, R., Skarmeta-Gomez, A.F. (2018). Classification of Spatio-Temporal Trajectories Based on Support Vector Machines. In: Demazeau, Y., An, B., Bajo, J., Fernández-Caballero, A. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018. Lecture Notes in Computer Science(), vol 10978. Springer, Cham. https://doi.org/10.1007/978-3-319-94580-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-94580-4_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94579-8
Online ISBN: 978-3-319-94580-4
eBook Packages: Computer ScienceComputer Science (R0)