Towards Data Summarization of Multi-Aspect Trajectories Based on Spatio-Temporal Segmentation
DOI:
https://doi.org/10.5753/jidm.2025.4110Keywords:
Multiple aspect trajectory, representative trajectory, trajectory summarizationAbstract
This paper presents a new method for summarizing multiple aspect trajectories (MATs). This kind of data holds several challenges in terms of analysis and extraction of meaningful insights due to their spatial, temporal, and semantic dimensions. In order to address them, our method leverages a combination of spatial grid-based segmentation and temporal sequence analysis. It segments the trajectory data into spatial cells using a grid-based approach. The spatial segmentation enables a finer-grained analysis of the trajectories within each cell. Next, we consider the temporal sequence of points within each cell to capture the temporal intervals of the trajectories. By combining spatial and temporal perspectives, the method identifies representative trajectories that capture the main behavior of semantically enriched object movements. We evaluated the utility of our method by applying two distinct strategies: (i) the RMMAT measure, assessing the quality of representative MAT in terms of similarity and coverage of information, and (ii) the Average Recall (AR) metric, measuring the ability of our representative MAT to capture essential data characteristics. Our evaluation demonstrates the effectiveness of MAT-SGT in summarizing MATs. The proposed method holds potential applications across diverse domains, including transportation planning, urban analytics, and human mobility analysis, where the concise representation of trajectories is crucial for decision-making and knowledge discovery.
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