Abstract
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<em> </em>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,<em> </em>the prediction error or RMSE value reached score 3.551 by setting LSTM with parameter of <em>epoch</em> and <em>batch_size</em> is 100 and 20 respectively.
Highlights
The research of that human mobility prediction in mobile computing area increased due to the availability of large-scale dataset contained individual’s location trajectory
The prediction of human mobility itself is related to an estimate of the place, which will be visited by people in a city
It suggests strong temporal presence such as people go to work during the day regularly and go shopping routine from by the data of the location of mobile users [1]–[4]
Summary
The research of that human mobility prediction in mobile computing area increased due to the availability of large-scale dataset contained individual’s location trajectory. The prediction of human mobility itself is related to an estimate of the place, which will be visited by people in a city. It suggests strong temporal presence such as people go to work during the day regularly and go shopping routine from by the data of the location of mobile users [1]–[4]. Is a general definition of a problem prediction on human mobility: for example, dj represents one's location at a time (1 ≤ j ≤ n). For this person, we have a history of the locations visited in order Dn = d1, d2, d3, ...
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