Moving destination prediction using sparse dataset: A mobility gradient descent approach
文献类型：期刊论文
| 作者 | Wang L(王亮); Wu ZW(於志文); Guo B(郭斌); Ku T(库涛) ; Yi, Fei
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| 刊名 | ACM Transactions on Knowledge Discovery from Data
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| 出版日期 | 2017 |
| 卷号 | 11期号:3页码:1-33 |
| 关键词 | Moving destination prediction sparse dataset space division gradient descent Markov transition model |
| ISSN号 | 1556-4681 |
| 产权排序 | 3 |
| 通讯作者 | 王亮 |
| 中文摘要 | Moving 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. |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000399725200012 |
| 源URL | [http://ir.sia.cn/handle/173321/20426]  |
| 专题 | 沈阳自动化研究所_数字工厂研究室 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：沈阳自动化研究所
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