Knowledge Commons of Institute of Automation,CAS
MLRNN: Taxi Demand Prediction Based on Multi-Level Deep Learning and Regional Heterogeneity Analysis | |
Chizhan Zhang1,2; Fenghua Zhu1; Yisheng Lv1; Peijun Ye1; Feiyue Wang1 | |
发表期刊 | IEEE Transactions on Intelligent Transportation Systems |
ISSN | 1524-9050 |
2021 | |
卷号 | 0期号:0页码:0 |
通讯作者 | Zhu, Fenghua(fenghua.zhu@ia.ac.cn) ; Lv, Yisheng(yisheng.lv@ia.ac.cn) |
摘要 | Taxi demand prediction is valuable for the decision-making of online taxi-hailing platforms. Data-driven deep learning approaches have been widely utilized in this area, and many complex spatiotemporal characteristics of taxi demand have been studied. However, the heterogeneity of demand patterns among different taxi zones has not been taken into account. To this end, this paper explores zone clustering and how to utilize the inter-zone heterogeneity to improve the prediction. First, based on the pairwise clustering theory, a taxi zone clustering algorithm is designed by considering the correlations among different taxi zones. Then, both the cluster-level and the global-level prediction modules are developed to extract intra- and inter-cluster characteristics, respectively. Finally, a Multi-Level Recurrent Neural Networks (MLRNN) model is proposed by combining the two modules. Experiments on two taxi trip records datasets from New York City demonstrate that our model improves the prediction accuracy compared with other state-of-the-art methods. |
关键词 | Taxi demand prediction taxi zone clustering heterogeneity analysis deep learning |
DOI | 10.1109/TITS.2021.3080511 |
关键词[WOS] | MODEL |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2020YFB2104000] ; National Natural Science Foundation of China (NSFC)[U1811463] ; National Natural Science Foundation of China (NSFC)[U1909204] ; National Natural Science Foundation of China (NSFC)[62076237] ; National Natural Science Foundation of China (NSFC)[61876011] ; China Railway[N2019G020] ; China Railway[2019B1515120030] ; Youth Innovation Promotion Association of Chinese Academy of Sciences[2021130] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China (NSFC) ; China Railway ; Youth Innovation Promotion Association of Chinese Academy of Sciences |
WOS研究方向 | Engineering ; Transportation |
WOS类目 | Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology |
WOS记录号 | WOS:000732136500001 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 人工智能+交通 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44725 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Fenghua Zhu; Yisheng Lv |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Chizhan Zhang,Fenghua Zhu,Yisheng Lv,et al. MLRNN: Taxi Demand Prediction Based on Multi-Level Deep Learning and Regional Heterogeneity Analysis[J]. IEEE Transactions on Intelligent Transportation Systems,2021,0(0):0. |
APA | Chizhan Zhang,Fenghua Zhu,Yisheng Lv,Peijun Ye,&Feiyue Wang.(2021).MLRNN: Taxi Demand Prediction Based on Multi-Level Deep Learning and Regional Heterogeneity Analysis.IEEE Transactions on Intelligent Transportation Systems,0(0),0. |
MLA | Chizhan Zhang,et al."MLRNN: Taxi Demand Prediction Based on Multi-Level Deep Learning and Regional Heterogeneity Analysis".IEEE Transactions on Intelligent Transportation Systems 0.0(2021):0. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
09439926.pdf(4431KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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