Towards a representativeness measure for summarized trajectories with multiple aspects

Large trajectory datasets have led to the development of summarization methods. However, evaluating the efficacy of these techniques can be complex due to the lack of a suitable representativeness measure. In the context of multi-aspect trajectories, current summarization lacks evaluation methods. To address this, we introduce RMMAT, a novel representativeness measure that combines similarity metrics and covered information to offer adaptability to diverse data and analysis needs. Our innovation simplifies summarization technique evaluation and enables deeper insights from extensive trajectory data. Our evaluation of real-world trajectory data demonstrates RMMAT as a robust Representativeness Measure for Summarized Trajectories with Multiple Aspects.