A hybrid model towards moving route prediction under data sparsity

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 数字工厂研究室

	A hybrid model towards moving route prediction under data sparsity
	Wang L(王亮); Wang, Mei ; Ku T(库涛); Cheng, Yong ; Guo, Xinying
	2017
会议名称	20th International Conference on Information Fusion, Fusion 2017
会议日期	July 10-13, 2017
会议地点	Xi'an, China
页码	1-8
通讯作者	Wang L(王亮)
中文摘要	Moving route prediction offers important benefits for many emerging location-aware applications such as target advertising and urban traffic management. A common approach to route prediction is to match similar trace recordings from a larger volume of historical trajectories, and return the targeted recorded path as desired answer. However, due to privacy concerns, incentive mechanism and other reasons, especially in small business environment, a limited dataset with sparse trajectories is only available. Actually, the existing sparse dataset cannot cover sufficient query routes, and then the match-based approach may return no results at all. Moreover, the existing sparse dataset may fail many trajectory mining approaches that work well on general environment. In this paper, we investigate moving route prediction from sparse trajectory dataset, and propose a novel hybrid model, namely HMRP, to address the above problem. To avoid sparse distribution over spatial semantic layer, a road network map reconstruction methods are proposed to accommodate the sparse trajectories in semantic transformation. And then, by training historical trajectories, the implicit mobility patterns and Markov transition model are constructed to support route prediction. When a query trajectory arrives, towards its derived potential destination, our proposed HMRP model integrates pattern matching strategy and Markov probability distribution to predict its future route gradually in a complementary way. Experiments on real-life taxicab GPS recorded dataset demonstrate that HMRP method can improve movement prediction precision significantly, comparing with the baseline prediction algorithms. And the response time for each query trajectory is acceptable for most application cases.
收录类别	EI ; CPCI（ISTP）
产权排序	2
会议主办者	China Gezhouba Group No.3 Engineering Co., Ltd (CGGC); Energy China; et al.; Hangzhou Dianzi Univeristy; LIFT; SATPRO
会议录	20th International Conference on Information Fusion, Fusion 2017 - Proceedings
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISBN号	978-0-9964-5270-0
WOS记录号	WOS:000410938300240
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/20997]
专题	沈阳自动化研究所_数字工厂研究室
作者单位	1.Xi'an University of Science and Technology, Xi'an, 710054, China 2.Shenyang Institute of Automation (SIA), Chinese Academy of Sciences, Shenyang, 110016, China
推荐引用方式 GB/T 7714	Wang L,Wang, Mei,Ku T,et al. A hybrid model towards moving route prediction under data sparsity[C]. 见:20th International Conference on Information Fusion, Fusion 2017. Xi'an, China. July 10-13, 2017.