Edge-based graph neural network for ranking critical road segments in a network

Transportation networks play a crucial role in society by enabling the smooth movement of people and goods during regular times and acting as arteries for evacuations during catastrophes and natural disasters. Identifying the critical road segments in a large and complex network is essential for pla...

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Veröffentlicht in:	PloS one Jg. 18; H. 12; S. e0296045
Hauptverfasser:	Jana, Debasish, Malama, Sven, Narasimhan, Sriram, Taciroglu, Ertugrul
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 21.12.2023 Public Library of Science (PLoS)
Schlagworte:	Analysis Approximation Arteries Biology and Life Sciences Business metrics Computer and Information Sciences Computer software industry Decision making Deep learning Disaster recovery Disasters Earthquakes Emergencies Emergency management Emergency plans Emergency preparedness Engineering and Technology Evacuation Evaluation Graph neural networks Graph theory Graphs Heuristic Humans Hurricanes Infrastructure (Economics) Iran Literature reviews Management Managers Natural Disasters Neural networks Neural Networks, Computer Optimization techniques Performance measurement Physical Sciences Ranking Rankings Recovery Research and Analysis Methods Resilience Roads & highways Segments Simulation Transportation Transportation networks Transportation planning United States United States Iran
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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