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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Published in:PloS one Vol. 18; no. 12; p. e0296045
Main Authors: Jana, Debasish, Malama, Sven, Narasimhan, Sriram, Taciroglu, Ertugrul
Format: Journal Article
Language:English
Published: United States Public Library of Science 21.12.2023
Public Library of Science (PLoS)
Subjects:
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
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Abstract 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 planners and emergency managers to enhance the network’s efficiency, robustness, and resilience to such stressors. We propose a novel approach to rapidly identify critical and vital network components (road segments in a transportation network) for resilience improvement or post-disaster recovery. We pose the transportation network as a graph with roads as edges and intersections as nodes and deploy a Graph Neural Network (GNN) trained on a broad range of network parameter changes and disruption events to rank the importance of road segments. The trained GNN model can rapidly estimate the criticality rank of individual road segments in the modified network resulting from an interruption. We address two main limitations in the existing literature that can arise in capital planning or during emergencies: ranking a complete network after changes to components and addressing situations in post-disaster recovery sequencing where some critical segments cannot be recovered. Importantly, our approach overcomes the computational overhead associated with the repeated calculation of network performance metrics, which can limit its use in large networks. To highlight scenarios where our method can prove beneficial, we present examples of synthetic graphs and two real-world transportation networks. Through these examples, we show how our method can support planners and emergency managers in undertaking rapid decisions for planning infrastructure hardening measures in large networks or during emergencies, which otherwise would require repeated ranking calculations for the entire network.
AbstractList 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 planners and emergency managers to enhance the network's efficiency, robustness, and resilience to such stressors. We propose a novel approach to rapidly identify critical and vital network components (road segments in a transportation network) for resilience improvement or post-disaster recovery. We pose the transportation network as a graph with roads as edges and intersections as nodes and deploy a Graph Neural Network (GNN) trained on a broad range of network parameter changes and disruption events to rank the importance of road segments. The trained GNN model can rapidly estimate the criticality rank of individual road segments in the modified network resulting from an interruption. We address two main limitations in the existing literature that can arise in capital planning or during emergencies: ranking a complete network after changes to components and addressing situations in post-disaster recovery sequencing where some critical segments cannot be recovered. Importantly, our approach overcomes the computational overhead associated with the repeated calculation of network performance metrics, which can limit its use in large networks. To highlight scenarios where our method can prove beneficial, we present examples of synthetic graphs and two real-world transportation networks. Through these examples, we show how our method can support planners and emergency managers in undertaking rapid decisions for planning infrastructure hardening measures in large networks or during emergencies, which otherwise would require repeated ranking calculations for the entire 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 planners and emergency managers to enhance the network's efficiency, robustness, and resilience to such stressors. We propose a novel approach to rapidly identify critical and vital network components (road segments in a transportation network) for resilience improvement or post-disaster recovery. We pose the transportation network as a graph with roads as edges and intersections as nodes and deploy a Graph Neural Network (GNN) trained on a broad range of network parameter changes and disruption events to rank the importance of road segments. The trained GNN model can rapidly estimate the criticality rank of individual road segments in the modified network resulting from an interruption. We address two main limitations in the existing literature that can arise in capital planning or during emergencies: ranking a complete network after changes to components and addressing situations in post-disaster recovery sequencing where some critical segments cannot be recovered. Importantly, our approach overcomes the computational overhead associated with the repeated calculation of network performance metrics, which can limit its use in large networks. To highlight scenarios where our method can prove beneficial, we present examples of synthetic graphs and two real-world transportation networks. Through these examples, we show how our method can support planners and emergency managers in undertaking rapid decisions for planning infrastructure hardening measures in large networks or during emergencies, which otherwise would require repeated ranking calculations for the entire 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 planners and emergency managers to enhance the network's efficiency, robustness, and resilience to such stressors. We propose a novel approach to rapidly identify critical and vital network components (road segments in a transportation network) for resilience improvement or post-disaster recovery. We pose the transportation network as a graph with roads as edges and intersections as nodes and deploy a Graph Neural Network (GNN) trained on a broad range of network parameter changes and disruption events to rank the importance of road segments. The trained GNN model can rapidly estimate the criticality rank of individual road segments in the modified network resulting from an interruption. We address two main limitations in the existing literature that can arise in capital planning or during emergencies: ranking a complete network after changes to components and addressing situations in post-disaster recovery sequencing where some critical segments cannot be recovered. Importantly, our approach overcomes the computational overhead associated with the repeated calculation of network performance metrics, which can limit its use in large networks. To highlight scenarios where our method can prove beneficial, we present examples of synthetic graphs and two real-world transportation networks. Through these examples, we show how our method can support planners and emergency managers in undertaking rapid decisions for planning infrastructure hardening measures in large networks or during emergencies, which otherwise would require repeated ranking calculations for the entire network.
Audience Academic
Author Jana, Debasish
Malama, Sven
Narasimhan, Sriram
Taciroglu, Ertugrul
AuthorAffiliation 2 Samueli Mechanical and Aerospace Engineering, University of California Los Angeles, Los Angeles, California, United States of America
1 Samueli Civil and Environmental Engineering, University of California Los Angeles, Los Angeles, California, United States of America
TU Wien: Technische Universitat Wien, AUSTRIA
AuthorAffiliation_xml – name: 1 Samueli Civil and Environmental Engineering, University of California Los Angeles, Los Angeles, California, United States of America
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/38127943$$D View this record in MEDLINE/PubMed
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2023 Jana et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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PublicationDate 2023-12-21
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– ident: pone.0296045.ref003
– volume: 9
  start-page: 260
  issue: 3
  year: 2013
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  article-title: Hierarchical infrastructure network representation methods for risk-based decision-making
  publication-title: Structure and infrastructure engineering
  doi: 10.1080/15732479.2010.546415
– ident: pone.0296045.ref055
  doi: 10.1016/j.neunet.2024.106207
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Snippet 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...
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SubjectTerms Analysis
Approximation
Arteries
Biology and Life Sciences
Business metrics
Computer and Information Sciences
Computer software industry
Decision making
Deep learning
Disaster recovery
Disasters
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Engineering and Technology
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Iran
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Natural Disasters
Neural networks
Neural Networks, Computer
Optimization techniques
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Simulation
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United States
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Title Edge-based graph neural network for ranking critical road segments in a network
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