Identifying Critical Links in Urban Transportation Networks Based on Spatio-Temporal Dependency Learning-Reference-Cited by-同舟云学术

Identifying Critical Links in Urban Transportation Networks Based on Spatio-Temporal Dependency Learning

Published:2023 Issue: Volume: Page:1-15
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Huang Xinlong¹^ORCID,Hu Simon¹^ORCID,Wang Wei²^ORCID,Kaparias Ioannis³,Zhong Shaopeng⁴,Na Xiaoxiang⁵,Bell Michael GH⁶,Lee Der-Horng¹

Affiliation:

1. ZJU-UIUC Institute, Zhejiang University, Haining, China

2. Artificial Intelligence Research Institute, Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing, Shenzhen MSU-BIT University, Shenzhen, China

3. Faculty of Engineering and Physical Sciences, Transportation Research Group, University of Southampton, Boldrewood Innovation Campus, Southampton, U.K.

4. School of Transportation and Logistics, Dalian University of Technology, Dalian, China

5. Department of Engineering, University of Cambridge, Cambridge, U.K.

6. Ports and Maritime Logistics, Institute of Transport and Logistics, The University of Sydney, Sydney, NSW, Australia

Funder

“Pioneer” and “Leading Goose” Research and Development Program of Zhejiang

National Natural Science Foundation of China

Smart Urban Future (SURF) Laboratory, Zhejiang Province, Zhejiang University Global Partnership Fund, Zhejiang University Sustainable Smart Livable Cities Alliance

National Science Fund for Young Scholars of China

Zhejiang University (ZJU)-University of Illinois Urbana-Champaign (UIUC) Joint Research Center Project of Zhejiang University led by Principal Supervisor

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/4358928/10368209.pdf?arnumber=10368209