Exploring the Side Information Fusion Method with Spatial-temporal Model for Taxi Demand Prediction
文献类型:会议论文
作者 | Jia Mou1,2; Yu Liu1,2; Dongchang Liu1 |
出版日期 | 2020-07 |
会议日期 | 2020-7-24 |
会议地点 | 中国 厦门 |
关键词 | SideInfo-STNet Spatial-temporal data Taxi demand prediction Deep learning |
DOI | 10.1145/3414274.3414510 |
英文摘要 | Taxi is one of the most common public transport, predicting taxi demand precisely within an area is of great signifcance for improving efciency of trafc. Taxi demand is spatialtemporal data, and highly influenced by many external factors, such as time, weather. So there are two main problems on taxi demand prediction: the one is that modeling both spatial and temporal non-linear correlations is not easy, the other is that real scenarios exist temporal but non-spatial side information, which is hard to be fused with spatial-temporal taxi demand. To handle the two problems, in this paper,we propose a novel Side information fused Spatial-Temporal |
语种 | 英语 |
URL标识 | 查看原文 |
源URL | [http://ir.ia.ac.cn/handle/173211/44316] |
专题 | 中国科学院自动化研究所 综合信息系统研究中心_脑机融合与认知评估 |
通讯作者 | Dongchang Liu |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences Beijing 100049, China 2.School of Artifcial Intelligence, University of Chinese Academy of Sciences Beijing 100049, China |
推荐引用方式 GB/T 7714 | Jia Mou,Yu Liu,Dongchang Liu. Exploring the Side Information Fusion Method with Spatial-temporal Model for Taxi Demand Prediction[C]. 见:. 中国 厦门. 2020-7-24. |
入库方式: OAI收割
来源:自动化研究所
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