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

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 ev

SUBMITTER: Jana D 

PROVIDER: S-EPMC10734987 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature