Generation parameters of the synthetic graphs.

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.

交通网络在社会中发挥着至关重要的作用：日常时段可保障人员与物资的顺畅流转，灾变与自然灾害发生时则作为疏散行动的动脉通道。识别大型复杂交通网络中的关键道路路段，是规划者与应急管理人员提升网络应对此类压力的效率、鲁棒性与恢复力的核心前提。我们提出一种全新方法，可快速识别交通网络中的关键核心路段，用于优化网络恢复力提升或开展灾后恢复工作。我们将交通网络建模为以道路为边、交叉口为节点的图结构，并部署经多类网络参数变化与中断事件训练的图神经网络（Graph Neural Network, GNN），对各道路路段的重要性进行排序。经训练的GNN模型可快速估算因中断事件产生修改后的网络中，单个道路路段的关键度排名。我们针对现有文献在资本规划或应急场景下存在的两大局限进行了改进：一是针对组件变更后的完整网络开展排名计算，二是应对灾后恢复排序中部分关键路段无法立即恢复的场景。尤为关键的是，本方法克服了重复计算网络性能指标所带来的计算开销问题——这类开销往往会限制其在大型网络中的实际应用。为展示本方法的适用场景，我们给出了合成图与两个真实世界交通网络的示例。通过这些示例，我们验证了本方法可辅助规划者与应急管理人员在大型网络的基础设施加固规划或应急场景中快速作出决策，而若采用传统方法，这类决策往往需要对整个网络进行多次重复的排名计算。