Human Mobility Prediction Based on DBSCAN and RNN

Most human behaviors are related to hot regions. The regularity of region transition is always behind the location transitions. DBSCAN (density-based spatial clustering of applications with noise) is a kind of density-based clustering method which is suitable for spatial clustering. RNN (recurrent neural network) is a kind of network which has a excellent capacity of capturing the sequential transitions. In this paper, we propose combining DBSCAN with the RNN-based model DeepMove to predict human mobility. DBSCAN is applied to the corresponding coordinates of all non-repeating discrete locations to obtain the region identification that represents the hot region or non-hot region of the users for the specific dataset. Having inserted the region identification into each record, the data is fed into DeepMove for training. An experiment is conducted on a real-life dataset Foursquare, of which the result shows it improves top-1 accuracy by 12.9% compared to single DeepMove.