Trajectory pattern construction and next location prediction of individual human mobility with deep learning models

Abstract

Many modern portable devices, especially smartphones, are equipped with positioning functionality. The rapid growth in the use of such devices has allowed for the accumulation of a vast amount of positioning data. Combined with deep learning methods, these data may be used for many novel applications. Herein, a trajectory pattern tree generation method via deep learning is proposed. The convolutional neural network (CNN) and recurrent neural network (RNN) model of deep learning were applied for trajectory generation and prediction. Several volunteers provided their raw positioning data. The trajectory generation and prediction are for individual mobility patterns and were performed for every volunteer. We present the results obtained from seven volunteers. The preciseness of prediction can be measured both for CNN and RNN. Consequently, we can predict an individual's location with 32.98% accuracy, and predict the top-five up to 69.22% for unit area size of 0.030 km2. Category: Information Retrieval / Web. © Korean Institute of Information Scientists and Engineers.