A Robust Measure for Evaluating Representativeness of Summarized Trajectories with Multiple Aspects
DOI:
https://doi.org/10.5753/jidm.2025.4082Keywords:
Multiple aspect trajectory, representative trajectory, trajectory similarity, representativeness measureAbstract
As trajectory datasets grow larger, summarization techniques become increasingly important. However, current methods often lack a suitable measure of representativeness, making evaluation a complex task. This is especially true in the context of multi-aspect trajectories, where evaluating summarization techniques is particularly challenging. To address this, we have developed a novel representativeness measure called RMMAT. This innovative method combines similarity metrics and covered information, offering adaptability to diverse data and analysis needs. With RMMAT, evaluating summarization techniques is simplified, and deeper insights can be gained from extensive trajectory data. Our evaluation of real-world trajectory datasets demonstrates that RMMAT is a robust Representativeness Measure for Summarized Trajectories with Multiple Aspects. This measure could help researchers and analysts to evaluate and empower them to make informed decisions about the quality and relevance of representative data for their analytical goals.
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