Toward Transportation Mode Recognition Using Deep Convolutional and Long Short-Term Memory Recurrent Neural Networks
Open accessAbstract
With the rapid development of mobile Internet techniques, using the sensor-rich smartphones to sense various contexts attracts much attention, such as transportation mode recognition. The transportation mode information can help to improve urban planning, traffic management and journey planning. Though much work has been done on the transportation mode recognition using classic machine learning algorithms, the performance of these methods is not reasonable and heavily relies on the effectiveness of handcrafted features. In this paper, we leverage the strong representation ability of deep learning method and present a deep-learning-based algorithm for transportation mode recognition, namely CL-TRANSMODE, which is capable of accurately detecting multiple transportation modes. The algorithm first uses a convolutional neural network (CNN) to learn appropriate and robust feature representations for transportation modes recognition. Then, an LSTM network performs a further learning of the temporal dependencies characteristics on the feature vectors of CNN output. To further enhance the accuracy of transportation mode recognition, several artificial segments and peak features are extracted from the raw sensor measurements. These features characterize the transportation modes over a much long period of time (minutes or hours). By combining the CNN-extracted features and handcrafted features, our proposed CL-TRANSMODE transportation mode recognition algorithm can accurately differentiate eight transportation modes, i.e., walking, running, bicycling, driving a car, riding a bus, taking a metro, taking a train, or being stationary. Extensive experiments on both the SHL and HTC datasets demonstrate that use our proposed CL-TRANSMODE transportation mode recognition algorithm which outperforms the state-of-the-art comparative algorithms. On the SHL dataset, which contain barometric data, the accuracy using the CL-TRANSMODE algorithm can reaches 98.1%.
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Qin et al. achieved 98.1% accuracy on the SHL transportation mode classification dataset with CNN-extracted and hand-crafted features as input.
“They achieved 98.1% accuracy on the SHL transportation mode classification dataset with CNN-extracted and hand-crafted features as input.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [210]
Qin et al. proposed a deep-learning algorithm that combines CNN and LSTM networks.
“Qin et al. proposed a deep-learning algorithm that combines CNN and LSTM networks [210].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [210]
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Cited by
Sorted by citations
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
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“proposed a deep-learning algorithm that combines CNN and LSTM networks [218].”
Background
- Authors
- Qin, Y., Luo, H., Zhao, F., Wang, C., Wang, J., & Zhang, Y.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- Chinese Academy of Sciences (CN)
- Beijing Information Science & Technology University (CN)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 142353-142367
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2977253239
- Cited by
- 46
- References
- 27
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 8 · 2023: 5 · 2022: 8 · 2021: 9 · 2020: 7
- FWCI
- 2.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- National Basic Research Program of China (2018YFB0505200)
- Beijing University of Posts and Telecommunications
- Northwestern Polytechnical University (2019XD-A06, 2019PTB-011)
- Special Project for Youth Research and Innovation, Beijing University of Posts and Telecommunications
- National Natural Science Foundation of China (61872046)
- Key Research and Development Project from Hebei Province (19210404D)
- Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device
- Blue Fire Plan (Huizhou) Industry-University Joint Innovation Project of Ministry of Education (CXZJHZ201729)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Keywords
- transportation mode recognition, smartphone sensing, convolutional neural networks, long short-term memory, CNN-LSTM hybrid model, temporal feature extraction, activity recognition
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