A Semisupervised End-to-End Framework for Transportation Mode Detection by Using GPS-Enabled Sensing Devices
Li, Zhishuai1,2; Xiong, Gang3,4; Wei, Zebing1,2; Lv, Yisheng1,2; Anwar, Noreen1,2; Wang, Fei-Yue1,2
Source PublicationIEEE INTERNET OF THINGS JOURNAL
ISSN2327-4662
2022-05-15
Volume9Issue:10Pages:7842-7852
Corresponding AuthorLv, Yisheng(yisheng.lv@ia.ac.cn)
AbstractAs an essential component of Internet of Things, GPS-enabled devices record tremendous digital traces, which provide a great convenience for understanding human mobility. How to discover transportation modes efficiently from such valuable sources has come into the spotlight. In this article, the transportation mode detection is treated as a dense classification task, and a similarity entropy-based encoder-decoder (SEED) model is proposed. We first design an encoder-decoder backbone for end-to-end mode detection. Then, a semi-supervised learning module based on similarity entropy is proposed to exploit numerous unlabeled data. Specifically, we stack several convolutional layers as an encoder to capture hierarchical features from fixed-length trajectories, and then adopt transposed convolutional layers as a decoder. For a semi-supervised module, inspired by entropy regularization, we use the K-Means algorithm to cluster prototype vectors from the encoder's predictions. We then fine-tune the encoder by sharpening the similarity distribution between unlabeled predictions and prototypes, aiming to make the former close to one prototype only while staying away from others. A majority-voting post-processing method is used to alleviate jitter impact when inferring. The Experimental results show that SEED significantly outperforms segmentation-then-inference methods. Furthermore, the similarity entropy-based module can improve the generalization performance of the model, and the metrics such as intersection over union can be increased by 5% over baselines. All of these verify the superiority of our method.
KeywordGPS trajectory human mobility semi-supervised learning transportation mode detection (TMD)
DOI10.1109/JIOT.2021.3115239
WOS KeywordTRAVEL
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2020YFB2104001] ; National Natural Science Foundation of China[U1909204] ; National Natural Science Foundation of China[61773381] ; National Natural Science Foundation of China[U1811463] ; National Natural Science Foundation of China[61872365] ; National Natural Science Foundation of China[61773382] ; Chinese Guangdong's ST Project[2019B1515120030] ; Chinese Guangdong's ST Project[2020B0909050001]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Chinese Guangdong's ST Project
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000803121100059
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49576
Collection复杂系统管理与控制国家重点实验室_平行智能技术与系统团队
Corresponding AuthorLv, Yisheng
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Beijing Engn Res Ctr Intelligent Syst & Technol, Inst Automat, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Guangdong Engn Res Ctr 3D Printing & Intelligent, Cloud Comp Ctr, Dongguan 523808, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li, Zhishuai,Xiong, Gang,Wei, Zebing,et al. A Semisupervised End-to-End Framework for Transportation Mode Detection by Using GPS-Enabled Sensing Devices[J]. IEEE INTERNET OF THINGS JOURNAL,2022,9(10):7842-7852.
APA Li, Zhishuai,Xiong, Gang,Wei, Zebing,Lv, Yisheng,Anwar, Noreen,&Wang, Fei-Yue.(2022).A Semisupervised End-to-End Framework for Transportation Mode Detection by Using GPS-Enabled Sensing Devices.IEEE INTERNET OF THINGS JOURNAL,9(10),7842-7852.
MLA Li, Zhishuai,et al."A Semisupervised End-to-End Framework for Transportation Mode Detection by Using GPS-Enabled Sensing Devices".IEEE INTERNET OF THINGS JOURNAL 9.10(2022):7842-7852.
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