融合出行流与路径信息，用图模型更准评估道路重要性。
Learning to Rank Critical Road Segments via Heterogeneous Graphs with Origin-Destination Flow Integration
- 构建三元异构图，统一出行流、路径和路网拓扑
- 在三个模拟网络上排名性能提升3.57%~7.52%
- 适合交通规划、智能导航系统研究者参考
现有道路网络学习排序方法常忽略起讫点（OD）流量与路径信息，难以建模长程空间依赖。为此，我们提出HetGL2R框架，通过构建包含OD流、路径与网络拓扑的三元异构图，并引入属性引导图将节点属性显式化为节点以建模功能相似性。采用异构联合随机游走算法（HetGWalk）同时采样两类图生成上下文丰富的节点序列，再用Transformer编码器学习蕴含OD流与路径配置引起的长程结构依赖及属性相似性带来的功能关联的嵌入表示。最后基于列表式排序策略与KL散度损失评估并排序路段重要性。在三个不同规模的SUMO仿真网络上的实验表明，相较于现有最优方法，HetGL2R在排名性能上平均提升约7.52%、4.40%和3.57%。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。