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Moving destination prediction using sparse dataset: A mobility gradient descent approach
Wang L(王亮); Wu ZW(於志文); Guo B(郭斌); Ku T(库涛); Yi, Fei
Department数字工厂研究室
Source PublicationACM Transactions on Knowledge Discovery from Data
ISSN1556-4681
2017
Volume11Issue:3Pages:1-33
Indexed BySCI ; EI
EI Accession number20171703602531
WOS IDWOS:000399725200012
Contribution Rank3
Funding OrganizationNational Basic Research Program of China (No. 2015CB352400), the National Natural Science Foundation of China (No. 61402360, 61373119, 61332005, 61402369).
KeywordMoving Destination Prediction Sparse Dataset Space Division Gradient Descent Markov Transition Model
AbstractMoving destination prediction offers an important category of location-based applications and provides essential intelligence to business and governments. In existing studies, a common approach to destination prediction is to match the given query trajectory with massive recorded trajectories by similarity calculation. Unfortunately, due to privacy concerns, budget constraints, and many other factors, in most circumstances, we can only obtain a sparse trajectory dataset. In sparse dataset, the available moving trajectories are far from enough to cover all possible query trajectories; thus the predictability of the matching-based approach will decrease remarkably. Toward destination prediction with sparse dataset, instead of searching similar trajectories over the sparse records, we alternatively examine the changes of distances from sampling locations to final destination on query trajectory. The underlying idea is intuitive: It is directly motivated by travel purpose, people always get closer to the final destination during the movement. By borrowing the conception of gradient descent in optimization theory, we propose a novel moving destination prediction approach, namely MGDPre. Building upon the mobility gradient descent, MGDPre only investigates the behavior characteristics of query trajectory itself without matching historical trajectories, and thus is applicable for sparse dataset. We evaluate our approach based on extensive experiments, using GPS trajectories generated by a sample of taxis over a 10-day period in Shenzhen city, China. The results demonstrate that the effectiveness, efficiency, and scalability of our approach outperform state-of-the-art baseline methods.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering
WOS KeywordMODEL ; OBJECTS
WOS Research AreaComputer Science
Citation statistics
Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/20426
Collection数字工厂研究室
Corresponding AuthorWang L(王亮)
Affiliation1.School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, 710072, China
2.Xi'an University of Science and Technology, China
3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, 110016, China
Recommended Citation
GB/T 7714
Wang L,Wu ZW,Guo B,et al. Moving destination prediction using sparse dataset: A mobility gradient descent approach[J]. ACM Transactions on Knowledge Discovery from Data,2017,11(3):1-33.
APA Wang L,Wu ZW,Guo B,Ku T,&Yi, Fei.(2017).Moving destination prediction using sparse dataset: A mobility gradient descent approach.ACM Transactions on Knowledge Discovery from Data,11(3),1-33.
MLA Wang L,et al."Moving destination prediction using sparse dataset: A mobility gradient descent approach".ACM Transactions on Knowledge Discovery from Data 11.3(2017):1-33.
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