How meaningful are similarities in deep trajectory representations?

Taghizadeh Saeed; Elekes Abel; Schaeler Martin; Boehm Klemens

摘要

Finding similar trajectories is an important task in moving object databases. However, classical similarity models face several limitations, including scalability and robustness. Recently, an approach named t2vec proposed transforming trajectories into points in a high dimensional vector space, and this transformation approximately keeps distances between trajectories. t2vec overcomes that scalability limitation: Now it is possible to cluster millions of trajectories. However, the semantics of the learned similarity values - and whether they are meaningful - is an open issue. One can ask: How does the configuration of t2vec affect the similarity values of trajectories? Is the notion of similarity in t2vec similar, different, or even superior to existing models? As for any neural-network-based approach, inspecting the network does not help to answer these questions. So the problem we address in this paper is how to assess the meaningfulness of similarity in deep trajectory representations. Our solution is a methodology based on a set of well-defined, systematic experiments. We compare t2vec to classical models in terms of robustness and their semantics of similarity, using two real-world datasets. We give recommendations which model to use in possible application scenarios and use cases. We conclude that using t2vec in combination with classical models may be the best way to identify similar trajectories. Finally, to foster scientific advancement, we give the public access to all trained t2vec models and experiment scripts. To our knowledge, this is the biggest collection of its kind. (C) 2019 Elsevier Ltd. All rights reserved.

机译：查找类似的轨迹是移动对象数据库中的重要任务。然而，经典的相似性模型面临多个限制，包括可扩展性和稳健性。最近，一种名为T2VEC的方法提出将轨迹转换为高维矢量空间中的点，并且该变换大致保持轨迹之间的距离。 T2VEC克服可扩展性限制：现在可以集聚数百万轨迹。但是，学习了相似性值的语义 - 以及它们是否有意义 - 是一个开放的问题。可以问：T2VEC的配置如何影响轨迹的相似性值？是T2VEC中相似性的相似之处的概念，不同，甚至优于现有模型？对于任何基于神经网络的方法，检查网络没有帮助回答这些问题。因此，我们在本文中解决的问题是如何评估深度轨迹陈述中相似性的有意义。我们的解决方案是一种基于一组明确的系统实验的方法。我们使用两个现实世界数据集比较T2VEC到古典模型及其相似性的语义。我们提供了在可能的应用程序方案和用例中使用的模型的建议。我们得出结论，使用T2VEC与经典模型的组合可能是识别类似轨迹的最佳方式。最后，为了促进科学进步，我们提供公众访问所有培训的T2VEC模型和实验脚本。为了我们的知识，这是它的最大系列。（c）2019 Elsevier Ltd.保留所有权利。

How meaningful are similarities in deep trajectory representations?

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