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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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Subject Classification

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.

Cite As Get BibTex

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.

References

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