Multi-View Spatial–Temporal Graph Convolutional Network for Traffic Prediction-Reference-Cited by-同舟云学术

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Multi-View Spatial–Temporal Graph Convolutional Network for Traffic Prediction

Published:2024-08 Issue:8 Volume:25 Page:9572-9586
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Wei Shuqing¹^ORCID,Feng Siyuan²^ORCID,Yang Hai³^ORCID

Affiliation:

1. Division of Emerging Interdisciplinary Area, The Hong Kong University of Science and Technology, Hong Kong, China

2. Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong, China

3. Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China

Funder

Hong Kong Research Grants Council (RGC) Theme-based Research Scheme

Hong Kong Research Grants Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6979/10621861/10443648.pdf?arnumber=10443648

Reference65 articles.

1. Data-Driven Intelligent Transportation Systems: A Survey

2. Urban Computing

3. Dynamic route planning with real-time traffic predictions

4. Spatiotemporal traffic forecasting: review and proposed directions

5. Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. PI-STGnet: Physics-integrated spatiotemporal graph neural network with fundamental diagram learner for highway traffic flow prediction;Expert Systems with Applications;2024-12

2. Koopman theory meets graph convolutional network: Learning the complex dynamics of non-stationary highway traffic flow for spatiotemporal prediction;Chaos, Solitons & Fractals;2024-10

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