Knowledge Commons of Institute of Automation,CAS
Trip Purposes Mining From Mobile Signaling Data | |
Li, Zhishuai1,2; Xiong, Gang2,3; Wei, Zebing1,2; Zhang, Yu4; Zheng, Meng4; Liu, Xiaoli5; Tarkoma, Sasu5; Huang, Min1; Lv, Yisheng1,2; Wu, Chuheng2 | |
发表期刊 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS |
ISSN | 1524-9050 |
2021 | |
卷号 | 99期号:99页码:13 |
摘要 | With the widespread application of mobile phones, it has become possible to study human mobility and travel behaviors based on cellular network data. Contrary to call detail records, the data is triggered by mobile cellular signaling and can provide fine-grained information about users' daily routines. However, it does not explicitly provide semantic details about traveling traces, e.g., trip purposes. In this paper, we propose a methodological framework to handle large-scale cellular network data and discover the underlying trip purposes in an unsupervised way. We first devise heuristic rules to identify home/work purposes. Then, a flexible latent Dirichlet allocation (LDA) model is presented to discover the activities for remaining trips, in which each trip is depicted by four attributes, i.e. arrival time, age group, stay duration, and the point of interest tag for the destination. Experimental results show that the proposed method can identify diverse trip purposes by explaining their structures over trip attributes and outperform baselines in terms of log-likelihood and perplexity. We also analyze the difference between the automatically discovered trip purposes and those estimated from household census, and the analyzed results demonstrate the feasibility of our proposed method. |
关键词 | Cellular networks Trajectory Semantics Unsupervised learning Supervised learning Resource management Public transportation Trip purpose inference cellular network data latent Dirichlet allocation travel behavior big data |
DOI | 10.1109/TITS.2021.3121551 |
关键词[WOS] | PREDICTION ; DISCOVERY ; PATTERNS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2020YFB2104001] ; National Natural Science Foundation of China[U1811463] ; National Natural Science Foundation of China[61903363] ; National Natural Science Foundation of China[61876011] ; National Natural Science Foundation of China[61603381] ; Chinese Guangdong's Science and Technology Project[2019B1515120030] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Chinese Guangdong's Science and Technology Project |
WOS研究方向 | Engineering ; Transportation |
WOS类目 | Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology |
WOS记录号 | WOS:000732146400001 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 数据挖掘 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47014 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Huang, Min; Lv, Yisheng |
作者单位 | 1.University of Chinese Academy Sciences 2.Institute Automatation, Chinese Academy Sciences 3.Chinese Acad Sci, Cloud Comp Ctr, Dongguan 523808, Peoples R China 4.Beijing Municipal Inst City Planning & Design, Beijing 100045, Peoples R China 5.Univ Helsinki, Dept Comp Sci, Helsinki 00014, Finland |
推荐引用方式 GB/T 7714 | Li, Zhishuai,Xiong, Gang,Wei, Zebing,et al. Trip Purposes Mining From Mobile Signaling Data[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2021,99(99):13. |
APA | Li, Zhishuai.,Xiong, Gang.,Wei, Zebing.,Zhang, Yu.,Zheng, Meng.,...&Wu, Chuheng.(2021).Trip Purposes Mining From Mobile Signaling Data.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,99(99),13. |
MLA | Li, Zhishuai,et al."Trip Purposes Mining From Mobile Signaling Data".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 99.99(2021):13. |
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