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Dynamic Topic Analysis and Visual Analytics for Trajectory Data: A Spatial Embedding Approach
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DOI：10.3390/electronics14244873.png)
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Abstract
En 中文
Analyzing the evolution of trajectory topics is fundamental to understanding urban mobility and human activity. Existing methods, however, often struggle to capture complex spatio-temporal semantics and are constrained by fixed time windows, which limits multi-scale temporal analysis. This paper presents a novel method to model the dynamic topics of trajectories to address these limitations. The proposed method combines a domain-specific trajectory embedding strategy, a flexible dynamic topic modeling pipeline, and an interactive visualization system to address these limitations. Firstly, the method introduces a novel embedding method that uses a retrained RoBERTa model with a word-level tokenizer on Morton-coded trajectories to effectively learn spatial context and sequential patterns. Secondly, a BERTopic-based approach is employed for topic modeling, featuring an adjustable time window that allows for flexible analysis of topic dynamics across different temporal scales without model retraining. Furthermore, an interactive visualization system with coordinated spatio-temporal views translates abstract model outputs into an intuitive format, enabling direct exploration of evolving trajectory topics. Experiments on a large-scale taxi trajectory dataset demonstrate the proposed method’s effectiveness in identifying coherent and meaningful patterns of dynamic trajectory topics.
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