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Behavior Analysis for Trajectory Graph Signal Using Graph Wavelet Transform
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AI
DOI：10.1109/JSEN.2024.3434459.png)
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Abstract
En 中文
Trajectory data mining can help us mine valuable information such as human activity patterns and movement characteristics, and provide data support for transportation planning, urban design, crime prevention, and behavior analysis. Graph data have been gradually applied in the field of trajectory data processing due to their advantages in easy construction and ability to solve complex relationship problems quickly. However, existing algorithms that combine graph data for trajectory data processing only consider movement patterns and traffic from a spatial perspective, with less attention paid to human behavioral characteristics. The algorithm for processing trajectory data is complex and lacks interpretability when faced with results. Aiming at the above problems, this article proposes a new graph construction method based on trajectory clustering and label classification and performs multiscale analysis based on graph wavelet transform. Experimental results show that this method can effectively extract the behavioral characteristics from trajectory data, achieving higher accuracy in abnormal behavior detection and group partitioning than existing methods.
Keywords:
Behavioral characteristics
graph signal
graph wavelet transform
trajectory data
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4.5
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