Deep flight track clustering based on spatial–temporal distance and denoising auto-encoding

作者：

Highlights：

• A novel metric of track similarity based on the spatial–temporal characteristics.

• A deep trajectory clustering model based on the denoising autoencoder.

• Improved performance for flight track clustering.

摘要

•A novel metric of track similarity based on the spatial–temporal characteristics.•A deep trajectory clustering model based on the denoising autoencoder.•Improved performance for flight track clustering.

论文关键词：Clustering,Similarity,Denoising auto-encoding,Spatial,Temporal

论文评审过程：Received 21 June 2021, Revised 21 February 2022, Accepted 22 February 2022, Available online 5 March 2022, Version of Record 15 March 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116733