Deep Spatially and Temporally Aware Similarity Computation for Road Network Constrained Trajectories

Fang, Ziquan; Du, Yuntao; Zhu, Xiaodong; Chen, Lu; Gao, Yunjun; Jensen, Christian S.

doi:10.48550/arxiv.2112.09339

Cited by 1 publication

(3 citation statements)

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“…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.…”

Section: Related Workmentioning

confidence: 99%

“…To mine the travel purpose of urban moving objects, Wan et al developed the SMOPAT algorithm which analyzes frequent patterns in private car trajectories [18]. There are also many studies that focus on association analysis between moving objects such as the similarity metric calculation of trajectories [19,20].…”

Section: Semantic Trajectorymentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Yang

Chen

et al. 2023

IJGI

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Spatio-temporal association analysis has attracted attention in various fields, such as urban computing and crime analysis. The proliferation of positioning technology and location-based services has facilitated the expansion of association analysis across spatio-temporal scales. However, existing methods inadequately consider the scale differences among spatio-temporal objects during analysis, leading to suboptimal precision in association analysis results. To remedy this issue, we propose a multiscale spatio-temporal object representation method, STO2Vec, for association analysis. This method comprises of two parts: graph construction and embedding. For graph construction, we introduce an adaptive hierarchical discretization method to distinguish the varying scales of local features. Then, we merge the embedding method for spatio-temporal objects with that for discrete units, establishing a heterogeneous graph. For embedding, to enhance embedding quality for homogeneous and heterogeneous data, we use biased sampling and unsupervised models to capture the association strengths between spatio-temporal objects. Empirical results using real-world open-source datasets show that STO2Vec outperforms other models, improving accuracy by 16.25% on average across diverse applications. Further case studies indicate STO2Vec effectively detects association relationships between spatio-temporal objects in a range of scenarios and is applicable to tasks such as moving object behavior pattern mining and trajectory semantic annotation.

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