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In the recent years, the rapid spread of mobile device has create the vastamount of mobile data. However, some shallow-structure models such as supportvector machine (SVM) have difficulty dealing with high dimensional data withthe development of mobile network. In this paper, we analyze mobile data topredict human trajectories in order to understand human mobility pattern via adeep-structure model called "DeepSpace". To the best of out knowledge, it isthe first time that the deep learning approach is applied to predicting humantrajectories. Furthermore, we develop the vanilla convolutional neural network(CNN) to be an online learning system, which can deal with the continuousmobile data stream. In general, "DeepSpace" consists of two differentprediction models corresponding to different scales in space (the coarseprediction model and fine prediction models). This two models constitute ahierarchical structure, which enable the whole architecture to be run inparallel. Finally, we test our model based on the data usage detail records(UDRs) from the mobile cellular network in a city of southeastern China,instead of the call detail records (CDRs) which are widely used by others asusual. The experiment results show that "DeepSpace" is promising in humantrajectories prediction.

DeepSpace: An Online Deep Learning Framework for Mobile Big Data to Understand Human Mobility Patterns