Travel Mode Recognition from GPS Data Based on LSTM

Authors

  • Shaowu Zhu College of Police Information Technology and Cyber Security, People's Public Security University of China, Beijing, China
  • Haichun Sun College of Police Information Technology and Cyber Security, People's Public Security University of China, Beijing, China
  • Yongcheng Duan College of Police Information Technology and Cyber Security, People's Public Security University of China, Beijing, China
  • Xiang Dai College of Police Information Technology and Cyber Security, People's Public Security University of China, Beijing, China
  • Sangeet Saha School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom

DOI:

https://doi.org/10.31577/cai_2020_1-2_298

Keywords:

GPS, LSTM, QGA, deep learning, travel mode

Abstract

A large amount of GPS data contains valuable hidden information. With GPS trajectory data, a Long Short-Term Memory model (LSTM) is used to identify passengers' travel modes, i.e., walking, riding buses, or driving cars. Moreover, the Quantum Genetic Algorithm (QGA) is used to optimize the LSTM model parameters, and the optimized model is used to identify the travel mode. Compared with the state-of-the-art studies, the contributions are: 1. We designed a method of data processing. We process the GPS data by pixelating, get grayscale images, and import them into the LSTM model. Finally, we use the QGA to optimize four parameters of the model, including the number of neurons and the number of hidden layers, the learning rate, and the number of iterations. LSTM is used as the classification method where QGA is adopted to optimize the parameters of the model. 2. Experimental results show that the proposed approach has higher accuracy than BP Neural Network, Random Forest and Convolutional Neural Networks (CNN), and the QGA parameter optimization method can further improve the recognition accuracy.

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Published

2020-02-29

How to Cite

Zhu, S., Sun, H., Duan, Y., Dai, X., & Saha, S. (2020). Travel Mode Recognition from GPS Data Based on LSTM. Computing and Informatics, 39(1-2), 298–317. https://doi.org/10.31577/cai_2020_1-2_298

Issue

Section

Special Section Articles