How meaningful are similarities in deep trajectory representations?

作者：

Highlights：

• We address the meaningfulness of similarity values in deep trajectory models.

• We evaluate the robustness of the deep trajectory model to parameterization.

• Similarity values are different for models trained with different parameters.

• The deep model captures different semantics of similarity than the classical models.

• T2vec is faster, and it is better for clustering than classical similarity models.

摘要

•We address the meaningfulness of similarity values in deep trajectory models.•We evaluate the robustness of the deep trajectory model to parameterization.•Similarity values are different for models trained with different parameters.•The deep model captures different semantics of similarity than the classical models.•T2vec is faster, and it is better for clustering than classical similarity models.

论文关键词：Trajectory similarity,Trajectory embedding models,Moving object databases,Trajectory databases,Trajectory clustering,Deep learning

论文评审过程：Received 31 December 2018, Revised 23 September 2019, Accepted 25 September 2019, Available online 11 October 2019, Version of Record 15 February 2021.

论文官网地址：https://doi.org/10.1016/j.is.2019.101452