Short-Term Metro Origin-Destination Passenger Flow Prediction via Spatio-Temporal Dynamic Attentive Multi-Hypergraph Network-Reference-Cited by-同舟云学术

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Short-Term Metro Origin-Destination Passenger Flow Prediction via Spatio-Temporal Dynamic Attentive Multi-Hypergraph Network

Published:2024-08 Issue:8 Volume:25 Page:9945-9957
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Shen Loutao¹,Li Junyi²^ORCID,Chen Yong²^ORCID,Li Chuanjia³,Chen Xiqun²^ORCID,Lee Der-Horng⁴^ORCID

Affiliation:

1. China Mobile Group Zhejiang Company Ltd., Hangzhou, China

2. Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China

3. Polytechnic Institute, Institute of Intelligent Transportation Systems, Zhejiang University, Hangzhou, China

4. Zhejiang University—University of Illinois Urbana-Champaign Institute, Zhejiang University, Haining, China

Funder

National Natural Science Foundation of China

Zhejiang Provincial Natural Science Foundation of China

Hainan Provincial Natural Science Foundation of China

Scientific Research Fund of Zhejiang Provincial Education Department

Research Fund of Zhejiang Provincial Xinmiao Talent Program

Smart Urban Future Laboratory, Zhejiang University, Zhejiang Province

Center for Balance Architecture, Zhejiang University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6979/10621861/10423900.pdf?arnumber=10423900

Reference34 articles.

1. Joint optimization of carriage arrangement and flow control in a metro-based underground logistics system

2. Demand-responsive passenger flow control strategies for metro networks considering service fairness and passengers’ behavioural responses

3. Deep reinforcement learning based train door adaptive control in metro tunnel evacuation optimization

4. Short-term traffic forecasting: Where we are and where we’re going

5. Transferability improvement in short-term traffic prediction using stacked LSTM network

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