Dual supervised autoencoder based trajectory classification using enhanced spatio-temporal information

Abstract

With the rapid development of mobile internet and location awareness techniques, massive spatio-temporal data is collected every day. Trajectory classification is critically important to many real-world applications such as human mobility understanding, urban planning, and intelligent transportation systems. A growing number of studies took advantage of the deep learning method to learn the high-level features of trajectory data for accurate estimation. However, some of these studies didn't interpret spatio-temporal information well, more importantly, they didn't fully utilize the high-level features extracted by neural networks. To overcome these drawbacks, this paper utilizes the proposed stop state and turn state to enhance spatial information, and at the same time, extracts stronger time information via Recurrence Plot (RP). Moreover, a novel Dual Convolutional neural networks based Supervised Autoencoder (Dual-CSA) is proposed by making the network aware of Predefined Class Centroids (PCC). Experiments conducted on two real-world datasets demonstrate that Dual-CSA can learn the high-level features well. The highest accuracy of the Geolife and SHL datasets are 89.475% and 89.602%, respectively, proving the superiority of our method.