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Title:
Context-Based Moving Object Trajectory Uncertainty Reduction and Ranking in Road Network
Author: Dai, J ; Ding, ZM ; Xu, JJ
Keyword: moving object trajectory ; uncertainty reduction ; road network ; context-aware information
Source: JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
Issued Date: 2016
Volume: 31, Issue:1, Pages:167-184
Indexed Type: SCI
Department: Beijing Univ Technol, Sch Comp Sci, Beijing 100124, Peoples R China. Univ Chinese Acad Sci, Beijing 100049, Peoples R China. Chinese Acad Sci, Inst Software, Beijing 100190, Peoples R China. Soochow Univ, Sch Comp Sci & Technol, Suzhou 215000, Peoples R China.
Abstract: To support a large amount of GPS data generated from various moving objects, the back-end servers usually store low-sampling-rate trajectories. Therefore, no precise position information can be obtained directly from the back-end servers and uncertainty is an inherent characteristic of the spatio-temporal data. How to deal with the uncertainty thus becomes a basic and challenging problem. A lot of researches have been rigidly conducted on the uncertainty of a moving object itself and isolated from the context where it is derived. However, we discover that the uncertainty of moving objects can be efficiently reduced and effectively ranked using the context-aware information. In this paper, we focus on context-aware information and propose an integrated framework, Context-Based Uncertainty Reduction and Ranking (CURR), to reduce and rank the uncertainty of trajectories. Specifically, given two consecutive samplings, we aim to infer and rank the possible trajectories in accordance with the information extracted from context. Since some context-aware information can be used to reduce the uncertainty while some context-aware information can be used to rank the uncertainty, to leverage them accordingly, CURR naturally consists of two stages: reduction stage and ranking stage which complement each other. We also implement a prototype system to validate the effectiveness of our solution. Extensive experiments are conducted and the evaluation results demonstrate the efficiency and high accuracy of CURR.
English Abstract: To support a large amount of GPS data generated from various moving objects, the back-end servers usually store low-sampling-rate trajectories. Therefore, no precise position information can be obtained directly from the back-end servers and uncertainty is an inherent characteristic of the spatio-temporal data. How to deal with the uncertainty thus becomes a basic and challenging problem. A lot of researches have been rigidly conducted on the uncertainty of a moving object itself and isolated from the context where it is derived. However, we discover that the uncertainty of moving objects can be efficiently reduced and effectively ranked using the context-aware information. In this paper, we focus on context-aware information and propose an integrated framework, Context-Based Uncertainty Reduction and Ranking (CURR), to reduce and rank the uncertainty of trajectories. Specifically, given two consecutive samplings, we aim to infer and rank the possible trajectories in accordance with the information extracted from context. Since some context-aware information can be used to reduce the uncertainty while some context-aware information can be used to rank the uncertainty, to leverage them accordingly, CURR naturally consists of two stages: reduction stage and ranking stage which complement each other. We also implement a prototype system to validate the effectiveness of our solution. Extensive experiments are conducted and the evaluation results demonstrate the efficiency and high accuracy of CURR.
Language: 英语
WOS ID: WOS:000370489700013
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/17415
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Dai, J,Ding, ZM,Xu, JJ. Context-Based Moving Object Trajectory Uncertainty Reduction and Ranking in Road Network[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2016-01-01,31(1):167-184.
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