Context-Based Moving Object Trajectory Uncertainty Reduction and Ranking in Road Network

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	Context-Based Moving Object Trajectory Uncertainty Reduction and Ranking in Road Network
	Dai, J ; Ding, ZM ; Xu, JJ
刊名	JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
	2016
卷号	31 期号:1 页码:167-184
关键词	moving object trajectory uncertainty reduction road network context-aware information
ISSN号	1000-9000
中文摘要	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.
英文摘要	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.
收录类别	SCI
语种	英语
WOS记录号	WOS:000370489700013
公开日期	2016-12-13
内容类型	期刊论文
源URL	[http://ir.iscas.ac.cn/handle/311060/17415]
专题	软件研究所_软件所图书馆_期刊论文
推荐引用方式 GB/T 7714	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,2016,31(1):167-184.
APA	Dai, J,Ding, ZM,&Xu, JJ.(2016).Context-Based Moving Object Trajectory Uncertainty Reduction and Ranking in Road Network.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,31(1),167-184.
MLA	Dai, J,et al."Context-Based Moving Object Trajectory Uncertainty Reduction and Ranking in Road Network".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 31.1(2016):167-184.