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A Semisupervised End-to-End Framework for Transportation Mode Detection by Using GPS-Enabled Sensing Devices

文献类型：期刊论文

作者Li, Zhishuai3,4; Xiong, Gang1,2; Wei, Zebing3,4; Lv, Yisheng3,4; Anwar, Noreen3,4; Wang, Fei-Yue3,4
刊名IEEE INTERNET OF THINGS JOURNAL
出版日期2022-05-15
卷号9期号:10页码:7842-7852
ISSN号2327-4662
关键词GPS trajectory human mobility semi-supervised learning transportation mode detection (TMD)
DOI10.1109/JIOT.2021.3115239
通讯作者Lv, Yisheng(yisheng.lv@ia.ac.cn)
英文摘要As 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.
WOS关键词TRAVEL
资助项目National 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]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000803121100059
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Chinese Guangdong's ST Project
源URL[http://ir.ia.ac.cn/handle/173211/49575]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Lv, Yisheng
作者单位1.Chinese Acad Sci, Guangdong Engn Res Ctr 3D Printing & Intelligent, Cloud Comp Ctr, Dongguan 523808, Peoples R China
2.Chinese Acad Sci, Beijing Engn Res Ctr Intelligent Syst & Technol, Inst Automat, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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