Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach
University of California, Santa Barbara
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…
Citation impact
- FWCI
- 311.75
- Percentile
- 100%
- References
- 0
Authors
3- WCWagner, ChristopherCorresponding
University of California, Santa Barbara
- DSDodge, Somayeh
University of California, Santa Barbara
- ADAlizadeh, Danial
University of California, Santa Barbara
Topics & keywords
- Autoencoder
- Computer science
- Encoder
- Graph
- Latent variable
- Benchmark (surveying)
- Artificial intelligence
- Feature learning