Most existing trajectory clustering methods use point matching similarity measurement strategies to compare trajectory objects; however, such methods have poor similarity measurement accuracy, which reduces clustering quality. To address this research gap, this article considers a combination of trajectory-feature learning and clustering. A deep trajectory clustering (DTC) method toward clustering-friendly spaces is proposed to simultaneously learn trajectory representations (i.e., low-dimensional vectors are used to represent trajectories) and cluster centroids, and further acquire the cluster assignments end to end. We first use a sequence-to-sequence autoencoder network for pretraining to obtain preliminary trajectory vectors, and then jointly optimize the learned representations and cluster centroids under the same framework. The effectiveness of DTC is demonstrated in synthetic datasets, and the learned representations in the real world can be used to identify hot routes in cities.
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)