SMSM: a similarity measure for trajectory stops and moves

ABSTRACT

For many years trajectory similarity research has focused on raw trajectories, considering only space and time information. With the trajectory semantic enrichment, emerged the need for similarity measures that support space, time, and semantics. Although some trajectory similarity measures deal with all these dimensions, they consider only stops, ignoring the moves. We claim that, for some applications, the movement between stops is as important as the stops, and they must be considered in the similarity analysis. In this article, we propose SMSM, a novel similarity measure for semantic trajectories that considers both stops and moves. We evaluate SMSM with three trajectory datasets: (i) a synthetic trajectory dataset generated with the Hermoupolis semantic trajectory generator, (ii) a real trajectory dataset from the CRAWDAD project, and (iii) the Geolife dataset. The results show that SMSM overcomes state-of-the-art measures developed either for raw or semantic trajectories.

KEYWORDS:

• Trajectory similarity measures
• semantic trajectory similarity
• stops and moves similarity
• episode similarity
• stay point similarity

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work has been partially supported by the Brazilian agencies CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Finance Code 001), CNPQ (Conselho Nacional de Desenvolvimento Científico e Tecnológico) and FAPESC (Fundação de Amparo a Pesquisa e Inovação do Estado de Santa Catarina - Project Match - Co-financing of H2020 Projects - Grant 2018TR 1266).

Notes on contributors

Andre L. Lehmann

Andre L. Lehmann received his Master Degree in Computer Science (2019) at Universidade Federal de Santa Catarina.

Luis Otavio Alvares

Luis Otavio Alvares is professor at the Department of Informatics and Statistics (Departamento de Informática e Estatistica) at Universidade Federal de Santa Catarina, Florianopolis, Brazil. Until 2010 he had been professor at Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil. He received his Ph.D. in Computer Science from Université Joseph Fourier, Grenoble, France, in 1988, and his M.Sc. in Computer Science from PPGC/UFRGS in 1982. He did a post-doctoral stage at Laboratoire LEIBNIZ/IMAG, Grenoble, France, with Yves Demazeau in 1994/1995. He has served as reviewer,organizing, and technical committee member of several conferences and journals. His research interest include trajectory analysis, spatial and spatio-temporal Data Mining, and Multiagent Systems. He advised 24 M.Sc. and 4 Ph.D. thesis.

Vania Bogorny

Vania Bogorny is professor at Departamento de Informatica e Estatística of Universidade Federal de Santa Catarina (UFSC) Brazil. She received her  PhD (2006) in Computer Science from Universidade Federal do Rio Grande do Sul, Porto Alegre/Brazil. During the PhD Program she was visitor scholar at University of Minnesota, USA (September/2004 to March/2005). From November/2006 to January/2008 she joined the research staff of the Theoretical Computer Science Group, of Hasselt University, Belgium, to work in the context of the European project GeoPKDD. In 2007, she received the Best PhD Thesis Award from the Brazilian Computer Society. She has published in refereed journals and conference proceedings, such as the International Conference on Data Mining (IEEE ICDM), International Symposium on Advances in Geographic Information Systems (ACMGIS), International Conference on Intelligent Systems (IEEE IS), Transactions in GIS, DKE, and International Journal of Geographical Information Systems (IJGIS). She has served as reviewer and technical committee member of international journals and European Projects. She is currently the UFSC coordinator of the MASTER H2020 Project. Her general areas of interest are moving object trajectories, spatial and spatio-temporal data mining, spatial data modeling, and mobility data similarity.