Clustering of Human Movement Trajectories based on Distributional Representations Derived from Bi-directional LSTM Network with Geographical Coordinates

Abstract

As the ubiquity of such wearable devices as smart-phones continues to deepen its presence in modern societies, it has become possible to analyze and visualize people who are moving as part of a trajectory of big data. In this study, we cluster human movement trajectories using time-series distributional representations. For the clustering, we calculated the distance of the representation vectors derived from neural network models. Previous work leveraged the Long short-term memory (LSTM) network to train the next mesh prediction. In this study, we propose using the Bi-directional LSTM (Bi-LSTM) network and the integrated additional geographical coordinates (latitude and longitude information) in models to accurately predict the next mesh and construct user clusters. As a result, we improved the accuracy of the next mesh prediction and obtained and visualized clusters of human movement trajectories.