Efficient Semantic Enrichment Process for Spatiotemporal Trajectories

Abstract

The increasing availability of location-acquisition technologies has enabled collecting large-scale spatiotemporal trajectories, from which we can derive semantic information in urban environments, including location, time, direction, speed, and point of interest. Such semantic information can give us a semantic interpretation of movement behaviors of moving objects. However, existing semantic enrichment process approaches, which can produce semantic trajectories, are generally time-consuming. In this paper, we propose an efficient semantic enrichment process framework to annotate spatiotemporal trajectories by using geographic and application domain knowledge. The framework mainly includes preannotated semantic trajectory storage phase, spatiotemporal similarity measurement phase, and semantic information matching phase. Having observed the common trajectories in the same geospatial object scenes, we propose a semantic information matching algorithm to match semantic information in preannotated semantic trajectories to new spatiotemporal trajectories. In order to improve the efficiency of this approach, we build a spatial index to enhance the preannotated semantic trajectories. Finally, the experimental results based on a real dataset demonstrate the effectiveness and efficiency of our proposed approaches.

STO2Vec: A Multiscale Spatio-Temporal Object Representation Method for Association Analysis

“…There are currently numerous studies analyzing the association relationships based on spatio-temporal co-occurrence from different perspectives. These studies can be classified into semantic trajectory-based approaches [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] and location embedding-based approaches [23][24][25][26][27][28][29][30][31][32][33] according to the analysis methods.…”

“…Alvares et al proposed the stop-move model, which converts trajectories into sequences with labels through semantic annotation, thereby mining and analyzing the interaction and association of moving objects in geographic space [11]. Based on the stop-move model, some of the research work has focused on how to better geographically associate this semantic annotation of trajectories [9,[12][13][14]; meanwhile, many studies have constructed semantic trajectory models and designed corresponding association analysis algorithms for different application domains. For instance, Ying et al used the frequent pattern of geography-time-semantics in semantic trajectories for location prediction of moving objects [15].…”

“…Associations are universal [3], making association analysis applicable to various fields with different relationship types. Sharma et al [2] grouped spatio-temporal associations into three types based on whether a temporal sequence was considered: sequential (e.g., analyzing event-oriented spatio-temporal association in video surveillance [4]), cascading (e.g., studying relationships between events, locations, and criminal activities in criminal geography [5]), and co-occurrences (e.g., similar associations between trajectories [6], co-location patterns between geographic entities [7], semantic annotation of trajectories [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22], and location embedding [23][24][25][26][27][28][29][30][31][32][33], etc.). By comparing their frequency of co-occurrence, spatio-temporal co-occurrence-based association analysis can reveal implicit associations between entities.…”