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.
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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).
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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
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