Implementation of Deep Learning Predictor (LSTM) Algorithm for Human Mobility Prediction

Ida Nurhaida, Handrie Noprisson, Vina Ayumi, Hong Wei, Erwin Dwika Putra, Marissa Utami, Hadiguna Setiawan


The studies of human mobility prediction in mobile computing area gained due to the availability of large-scale dataset contained history of location trajectory. Previous work has been proposed many solutions for increasing of human mobility prediction result accuration, however, only few researchers have addressed the issue of human mobility for implementation of LSTM networks. This study attempted to use classical methodologies by combining LSTM and DBSCAN because those algorithms can tackle problem in human mobility, including large-scale sequential data modeling and number of clusters of arbitrary trajectory identification. The method of research consists of DBSCAN for clustering, long short-term memory (LSTM) algorithm for modelling and prediction, and Root Mean Square Error (RMSE) for evaluation. As the result, the prediction error or RMSE value reached score 3.551 by setting LSTM with parameter of epoch and batch_size is 100 and 20 respectively.


human mobility, long short-term memory (LSTM), DBSCAN

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International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923
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