SMSM: a similarity measure for trajectory stops and moves. Issue 9 (2nd September 2019)

• Record Type: Journal Article
• Authors: Lehmann, Andre L.; Alvares, Luis Otavio; Bogorny, Vania
• Is Part Of: International journal of geographical information science. Volume 33:Issue 9(2019)
• Page Start: 1847
• Page End: 1872
• Publication Date: 2019-09-02
• Subjects: Trajectory similarity measures -- semantic trajectory similarity -- stops and moves similarity -- episode similarity -- stay point similarity; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2019.1605074
• Languages: English
• ISSNs: 1365-8816

Abstract

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.