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

Author

Listed:
  • Debasish Jana
  • Sven Malama
  • Sriram Narasimhan
  • Ertugrul Taciroglu

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.

Suggested Citation

  • Debasish Jana & Sven Malama & Sriram Narasimhan & Ertugrul Taciroglu, 2023. "Edge-based graph neural network for ranking critical road segments in a network," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-36, December.
  • Handle: RePEc:plo:pone00:0296045
    DOI: 10.1371/journal.pone.0296045
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    References listed on IDEAS

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    1. B. Berche & C. von Ferber & T. Holovatch & Yu. Holovatch, 2009. "Resilience of public transport networks against attacks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(1), pages 125-137, September.
    2. Jawad-ur-Rehman Chughtai & Irfan Ul Haq & Muhammad Muneeb, 2022. "An attention-based recurrent learning model for short-term travel time prediction," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-20, December.
    3. Lichun Chen & Elise Miller-Hooks, 2012. "Resilience: An Indicator of Recovery Capability in Intermodal Freight Transport," Transportation Science, INFORMS, vol. 46(1), pages 109-123, February.
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