Combining Residual and LSTM Recurrent Networks for Transportation Mode Detection Using Multimodal Sensors Integrated in Smartphones

In recent years, with the rapid development of public transportation, the ways people travel has become more diversified and complicated. Transportation mode detection, as a significant branch of human activity recognition (HAR), is of great importance in analyzing human travel patterns, traffic pre...

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Vydáno v:IEEE transactions on intelligent transportation systems Ročník 22; číslo 9; s. 5473 - 5485
Hlavní autoři: Wang, Chenxing, Luo, Haiyong, Zhao, Fang, Qin, Yanjun
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.09.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accuracy
activity recognition
Algorithms
Context awareness
Feature extraction
Global Positioning System
Human activity recognition
Intelligent sensors
Machine learning
Moving object recognition
multimodal sensors
Public transportation
Sensors
Smart phones
Smartphones
Traffic planning
transportation pattern recognition
Travel patterns
Weight reduction
ISSN:1524-9050, 1558-0016
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Shrnutí:In recent years, with the rapid development of public transportation, the ways people travel has become more diversified and complicated. Transportation mode detection, as a significant branch of human activity recognition (HAR), is of great importance in analyzing human travel patterns, traffic prediction and planning. Though many works have been devoted to transportation mode detection, there remains challenge for accurate and robust transportation pattern identification. In this paper, we propose a residual and LSTM recurrent networks-based transportation mode detection algorithm using multiple light-weight sensors integrated in commodity smartphones. Feature representation learning is adopted separately on multiple preprocessed sensor data using deep residual and LSTM network, which can enhance the identification accuracy and support one or more sensors. Residual units are introduced to accelerate the learning speed and enhance the accuracy of transportation mode detection. Furthermore, we also leverage the attention model to learn the significance of different features and different timesteps to enhance the recognition accuracy. Extensive experimental results on three datasets indicate that using our proposed model can achieve the best recognition accuracy for eight transportation modes including being stationary, walking, running, cycling, taking a car, taking a bus, taking a subway and taking a train, which outperforms other benchmark algorithms.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2020.2987598