Regularized Spatial–Temporal Graph Convolutional Networks for Metro Passenger Flow Prediction-Reference-Cited by-同舟云学术

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Regularized Spatial–Temporal Graph Convolutional Networks for Metro Passenger Flow Prediction

Published:2024-09 Issue:9 Volume:25 Page:11241-11255
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Gao Chao¹^ORCID,Liu Hao²,Huang Jiajin³^ORCID,Wang Zhen¹^ORCID,Li Xianghua¹^ORCID,Li Xuelong⁴^ORCID

Affiliation:

1. School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an, China

2. College of Computer and Information Science, Southwest University, Chongqing, China

3. Faculty of Information Technology, Beijing University of Technology, Beijing, China

4. Institute of Artificial Intelligence (TeleAI), China Telecom Corporation Ltd., Beijing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Fok Ying Tong Education Foundation

Natural Science Basic Research Plan in Shaanxi Province of China

Tencent Foundation

Xplorer Prize

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6979/10659279/10452257.pdf?arnumber=10452257

Reference59 articles.

1. Metro Passenger Flow Prediction via Dynamic Hypergraph Convolution Networks

2. A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges

3. Subway Passenger Flow Prediction for Special Events Using Smart Card Data

4. Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting

5. Gallat: A Spatiotemporal Graph Attention Network for Passenger Demand Prediction

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