Clustering Trajectories via Sparse Auto-encoders

Abstract

With the development of satellite navigation, communication and positioning technology, more and more trajectory data are collected and stored. Exploring such trajectory data can help us understand human mobility. A typical task of group-level mobility modeling is trajectory clustering. However, trajectories usually vary in length and shape, also contain noises. These exert a negative influence on trajectory representation and thus hinder trajectory clustering. Therefore, this paper proposes a U-type robust sparse autoencoder model(uRSAA), which is robust against noise and form variety. Specifically, a sparsity penalty is applied to constrain the output to decrease the effect of noise. By introducing skip connections, our model can strengthen the data exchange and preserve the information. Experiments are conducted on both synthetic datasets and real datasets, and the results show that our model outperforms the existing models.