Learning to Rank Critical Road Segments via Heterogeneous Graphs with OD Flow Integration

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) samples both graph types to generate context-rich node sequences. These sequences are encoded with a Transformer to learn embeddings that capture long-range structural dependencies driven by OD demand and route configuration, 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. Keywords: Learning to Rank, Heterogeneous Graph, Random Walk, Ranking, Road Networks1. Introduction Efficient and resilient road networks are essential for ensuring smooth urban mobility and public safety. When a single road segment becomes congested or blocked, the resulting disruption often propagates along multiple routes, leading to large-scale delays or even citywide paralysis. Therefore, identifying critical road segments--those whose failure would significantly degrade overall network performance--is of great importance for traffic management and infrastructure planning (Xu et al., 2018). These approaches are intuitive and easy to interpret but fail to incorporate the rich attribute features and dynamic traffic behaviors associated with each road segment. In reality, a segment's criticality depends on multiple factors such as traffic volume, number of lanes, and functional hierarchy, all of which are neglected in purely topological metrics.