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

TL;DR

DeepSpace introduces a novel deep learning framework using hierarchical CNN models for online prediction of human mobility patterns from mobile data streams, outperforming traditional shallow models.

Contribution

This paper is the first to apply deep learning, specifically hierarchical CNNs, for real-time human trajectory prediction from mobile big data.

Findings

DeepSpace effectively predicts human trajectories in real-time.

Hierarchical models improve prediction accuracy at different spatial scales.

The approach outperforms traditional shallow models like SVM.

Abstract

In the recent years, the rapid spread of mobile device has create the vast amount of mobile data. However, some shallow-structure models such as support vector machine (SVM) have difficulty dealing with high dimensional data with the development of mobile network. In this paper, we analyze mobile data to predict human trajectories in order to understand human mobility pattern via a deep-structure model called "DeepSpace". To the best of out knowledge, it is the first time that the deep learning approach is applied to predicting human trajectories. Furthermore, we develop the vanilla convolutional neural network (CNN) to be an online learning system, which can deal with the continuous mobile data stream. In general, "DeepSpace" consists of two different prediction models corresponding to different scales in space (the coarse prediction model and fine prediction models). This two models constitute a hierarchical structure, which enable the whole architecture to be run in parallel. 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 as usual. The experiment results show that "DeepSpace" is promising in human trajectories prediction.