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
Source PublicationIEEE Transactions on Intelligent Transportation Systems
ISSN1524-9050
2021
Volume0Issue:0Pages:0
Corresponding AuthorZhu, Fenghua(fenghua.zhu@ia.ac.cn) ; Lv, Yisheng(yisheng.lv@ia.ac.cn)
Abstract

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.

KeywordTaxi demand prediction taxi zone clustering heterogeneity analysis deep learning
DOI10.1109/TITS.2021.3080511
WOS KeywordMODEL
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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 Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000732136500001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Sub direction classification人工智能+交通
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/44725
Collection复杂系统管理与控制国家重点实验室_平行智能技术与系统团队
Corresponding AuthorFenghua Zhu; Yisheng Lv
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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