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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.19199 |
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| _version_ | 1866915840566231040 |
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| author | Xu, Ming Xiang, Jinrong Xie, Zilong Meng, Xiangfu |
| author_facet | Xu, Ming Xiang, Jinrong Xie, Zilong Meng, Xiangfu |
| contents | 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 importance. Experiments on three SUMO-generated simulated networks of different scales show that, against state-of-the-art methods, HetGL2R achieves average improvements of approximately 7.52%, 4.40% and 3.57% in ranking performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19199 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Rank Critical Road Segments via Heterogeneous Graphs with Origin-Destination Flow Integration Xu, Ming Xiang, Jinrong Xie, Zilong Meng, Xiangfu Machine Learning 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 importance. Experiments on three SUMO-generated simulated networks of different scales show that, against state-of-the-art methods, HetGL2R achieves average improvements of approximately 7.52%, 4.40% and 3.57% in ranking performance. |
| title | Learning to Rank Critical Road Segments via Heterogeneous Graphs with Origin-Destination Flow Integration |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.19199 |