Travel Mode Recognition From GPS Data Based On LSTM

Shaowu Zhu, Haichun Sun, Yongcheng Duan, Xiang Dai, Sangeet Saha

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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. More-over, 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 pa-rameters 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 clas-sification 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.

Original languageEnglish
Pages (from-to)298-317
Number of pages20
JournalComputing and Informatics
Volume39
Issue number1-2
Publication statusPublished - 29 Feb 2020
Externally publishedYes

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