A Multi-Relational Graph Ranking Framework for Identifying Potential Short-Haul Air-Service Links in County-Level Transport Networks

The development of low-altitude passenger transport and emerging short-haul air services has made inter-county short-distance routes an important subject for regional aviation market research and preliminary planning. However, piloted eVTOL and low-altitude short-haul passenger services remain at an early stage, lacking continuous and stable historical operational data for supervised modelling. Moreover, unobserved county-level routes cannot be simply regarded as having no market potential. This study formulates county-level candidate route identification as a large-scale priority ranking problem under sparse proxy labels and develops a multi-relational graph ranking framework. The framework integrates county attributes, mobility, transport impedance, high-speed rail substitution conditions, and estimated air travel time variables through multiple relational graphs, and employs an MR-GAT ranking model to learn relative priorities among candidate routes using a pairwise ranking objective. Observed civil aviation route labels and flight frequencies are used as primary supervision signals, while airport catchment weak labels are introduced for auxiliary analysis. Experiments conducted over 240,350 county-level candidate ODs show that, across ten random seeds, the MR-GAT model achieves a mean internal Top-1000 recall of 16.33%, compared with 7.55% for XGBoost and 2.00% for the gravity-based heuristic. Paired bootstrap analysis further supports the higher internal top-ranked retrieval performance of MR-GAT over XGBoost. Ten-seed relational ablation shows relatively small differences among individual relation-removal variants, indicating that no single relational graph dominates the ranking performance under the current evaluation setting. Sensitivity analysis further shows that the resulting ranking is highly robust to uniform additions of up to 90 min to the estimated air travel time. Analysis of the resulting candidate set indicates that high-ranking ODs are typically associated with stronger inter-county interactions, higher ground-transport impedance, and limited direct high-speed rail connectivity. The framework therefore provides an analytical screening tool for reducing a large candidate space to a smaller set of links for subsequent market and operational feasibility assessment.