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Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach

Authors Christopher Wagner , Somayeh Dodge , Danial Alizadeh

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ACM Subject Classification
  • Information systems → Geographic information systems
Keywords
  • road networks
  • resilience analysis
  • machine learning
  • graph neural networks

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Abstract

Effective resilience analysis of road networks is fundamental to building sustainable and disaster prepared cities. Identifying which road segments share similar vulnerabilities is important for pinpointing high-risk areas within the network and implementing measures to safeguard them against future disruptions. Graph-based community detection can be applied to group together areas of the network sharing similar structural vulnerabilities. However, current graph-based community detection methods either struggle with integrating node features during partitioning or do not account for the path-based dependencies in road networks. This paper introduces the Path-based Community Embedding (PCE) model, an approach that leverages path-based embeddings to overcome these limitations. PCE combines the strengths of graph attention networks and Long Short-Term Memory models (LSTMs) to learn representations that incorporate both local neighborhood information and long-range path dependencies. Our results on the Santa Barbara road network show that PCE improves community detection performance for resilience analysis, thus offering a powerful tool for urban planners and transportation engineers to preemptively identify vulnerabilities in road networks.

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Christopher Wagner, Somayeh Dodge, and Danial Alizadeh. Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 9:1-9:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.GIScience.2025.9

Author Details

Christopher Wagner
  • Department of Statistics and Applied Probability, University of California, Santa Barbara, CA, USA
Somayeh Dodge
  • Department of Geography, University of California, Santa Barbara, CA, USA
Danial Alizadeh
  • Department of Geography, University of California, Santa Barbara, CA, USA

Funding

The authors gratefully acknowledge the financial support from the U.S. National Science Foundation through award #BCS-2043202.

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