Paper Info
Title
DeepSpace: An Online Deep Learning Framework for Mobile Big Data to Understand Human Mobility Patterns.
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.
Year
Venue
Field
2016
arXiv: Computers and Society
Data mining,Convolutional neural network,Computer science,Support vector machine,Cellular traffic,Mobile device,Artificial intelligence,Cellular network,Deep learning,Big data,Mobile broadband,Machine learning
DocType
Volume
Citations 
Journal
abs/1610.07009
5
PageRank 
References 
Authors
0.42
12
4
Name
Order
Citations
PageRank
Xi Ouyang1131.22
Chaoyun Zhang21608.53
Pan Zhou338262.71
Hao Jiang4179.24