2026-07-22T01:57:11Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:23838 2025-11-12T13:20:23Z ddc:004 open_access

Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach Wagner, Christopher Dodge, Somayeh Alizadeh, Danial road networks resilience analysis machine learning graph neural networks 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Christopher Wagner and Somayeh Dodge and Danial Alizadeh 2025 Is Part Of LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GIScience.2025.9 urn:nbn:de:0030-drops-238380 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.9 eng https://creativecommons.org/licenses/by/4.0/legalcode