An intelligent identification method of user identity based on asynchronous trajectory

doi:10.13474/j.cnki.11-2246.2022.0222

Abstract

Abstract: Traditional trajectory identification methods have limit in feature selection and accuracy. Therefore, this paper proposes a mixed neural network of convolutional neural networks and recurrent neural networks(ConvGRU-Bidir).Firstly, the one-dimensional CNN and one-dimensional pooling neural networks will compress trajectory data and extract high-dimensional features. Then, the bidirectional GRU learns trajectory features from time positive and reverse simultaneously. Finally, the model can recognize users' ID number. This paper uses the GeoLife trajectory dataset to train and test the model, which contains 10837 trajectory samples from 122 users. The results show that the model has an identification accuracy of 97.28% for asynchronous trajectory data, which has improved by at least 30% compared with the existing methods, which proves deep learning's availability and effectiveness in such problems.

Key words: user trajectory, recurrent neural network, identity recognition, deep learning, convolutional neural network

CLC Number:

P208

CAI Roudan. An intelligent identification method of user identity based on asynchronous trajectory[J]. Bulletin of Surveying and Mapping, 2022, 0(7): 158-162,167.

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