Uncertainty Quantification of Spatiotemporal Travel Demand With Probabilistic Graph Neural Networks-Reference-Cited by-同舟云学术

Uncertainty Quantification of Spatiotemporal Travel Demand With Probabilistic Graph Neural Networks

Author:

Wang Qingyi¹^ORCID,Wang Shenhao²^ORCID,Zhuang Dingyi¹^ORCID,Koutsopoulos Haris³^ORCID,Zhao Jinhua⁴^ORCID

Affiliation:

1. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

2. Department of Urban and Regional Planning, University of Florida, Gainesville, FL, USA

3. Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA

4. Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA

Funder

U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Vehicle Technology Program

Research Opportunity Seed Fund (ROSF) 2023 with the University of Florida

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

Reference51 articles.

1. Homoscedasticity: An overlooked critical assumption for linear regression;Yang;Gen. Psychiatry,2019

5. Inductive graph neural networks for spatiotemporal kriging;Wu

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