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|Title:||Generating synthetic trajectory data using GRU||Authors:||Liu, Xinyao
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2022||Source:||Liu, X., Cui, B. & Xing, L. (2022). Generating synthetic trajectory data using GRU. Intelligent Automation and Soft Computing, 34(1), 295-305. https://dx.doi.org/10.32604/iasc.2022.020032||Journal:||Intelligent Automation and Soft Computing||Abstract:||With the rise of mobile network, user location information plays an increasingly important role in various mobile services. The analysis of mobile users’ trajectories can help develop many novel services or applications, such as targeted advertising recommendations, location-based social networks, and intelligent navigation. However, privacy issues limit the sharing of such data. The release of location data resulted in disclosing users’ privacy, such as home addresses, medical records, and other living habits. That promotes the develop-ment of trajectory generators, which create synthetic trajectory data by simulating moving objects. At current, there are some disadvantages in the process of gen-eration. The prediction of the following position in the trajectory generation is very dependent on the historical location data, but the relationship between trajectory positions tends to be ignored. Most commonly used methods only adopt the probability distribution of users’ positions to generate synthetic data. On the one hand, this type of statistical method is too rough, and on the other hand, it cannot bring more benefits in availability by increasing data volume. We propose a new trajectory generation method in this paper–Trajectory Generation Model with RNNs(TGMRNN), to address the deficiencies above. It adopts the RNN model to replace the traditional Markov model to generate trajectory data with higher availability. Meanwhile, it solves the problem that RNNs are unsuitable for continuous location data by representing trajectories as discretized data with the grid method. We have conducted experiments in a real data set. Compared with the Markov model, the results of TGMRNN demonstrate that it is superior to some existing methods.||URI:||https://hdl.handle.net/10356/162763||ISSN:||1079-8587||DOI:||10.32604/iasc.2022.020032||Schools:||School of Electrical and Electronic Engineering||Rights:||© The Authors. This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
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