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[2504.19199] Learning to Rank Critical Road Segments via Heterogeneous Graphs with Origin-Destination Flow Integration
Summary
The paper presents HetGL2R, a novel framework for ranking critical road segments by integrating heterogeneous graphs and origin-destination flows, enhancing long-range spatial dependency modeling.
Why It Matters
This research addresses limitations in existing road segment ranking methods by incorporating origin-destination flows and route information, which are crucial for urban planning and traffic management. Improved ranking of road segments can lead to better infrastructure development and traffic optimization.
Key Takeaways
- HetGL2R integrates heterogeneous graphs to improve road segment ranking.
- The framework incorporates origin-destination flows for better modeling.
- Experiments show significant performance improvements over existing methods.
- A novel listwise ranking strategy using KL-divergence loss is introduced.
- The approach enhances understanding of long-range spatial dependencies in road networks.
Computer Science > Machine Learning arXiv:2504.19199 (cs) [Submitted on 27 Apr 2025 (v1), last revised 21 Feb 2026 (this version, v3)] Title:Learning to Rank Critical Road Segments via Heterogeneous Graphs with Origin-Destination Flow Integration Authors:Ming Xu, Jinrong Xiang, Zilong Xie, Xiangfu Meng View a PDF of the paper titled Learning to Rank Critical Road Segments via Heterogeneous Graphs with Origin-Destination Flow Integration, by Ming Xu and 3 other authors View PDF HTML (experimental) Abstract:Existing learning-to-rank methods for road networks often fail to incorporate origin-destination (OD) flows and route information, limiting their ability to model long-range spatial dependencies. To address this gap, we propose HetGL2R, a heterogeneous graph learning framework for ranking road-segment importance. HetGL2R builds a tripartite graph that unifies OD flows, routes, and network topology, and further introduces attribute-guided graphs that elevate node attributes into explicit nodes to model functional similarity. A heterogeneous joint random walk algorithm (HetGWalk) jointly samples both graph types to generate context-rich node sequences. These sequences are encoded using a Transformer to learn embeddings that capture long-range structural dependencies induced by OD flows and route configurations, as well as functional associations derived from attribute similarity. Finally, a listwise ranking strategy with a KL-divergence loss evaluates and ranks segment impo...
