Graph-Informed Potential V2I Demand Forecasting for Overlap-Aware RSU Prioritization

Abstract

Roadside unit (RSU) planning requires evidence about when vehicles may encounter candidate sites and whether a shortlist repeatedly covers the same vehicles. This study treats potential vehicle–site contact opportunities—not observed communication sessions or network traffic—as short-horizon vehicle-to-infrastructure (V2I) demand. Seven days of Hohhot taxi and bus trajectories, comprising more than 40 million records, were aggregated into 15 min panels for 1317 bus-stop-derived candidate sites. Training-only vehicle-transition relations augmented LightGBM forecasting, and a permuted-graph control assessed the value of the mobility structure. Historical vehicle-population overlap then informed set-level site screening, which was evaluated by future unique-vehicle coverage. Graph-informed forecasts showed modest improvements over tabular and permuted-graph models, while a historical time-of-day baseline remained stronger at 60 min. At the primary 100 m spatial footprint and 30 min horizon, overlap-aware screening improved unique-vehicle coverage by approximately 10% across shortlist budgets. Temporal-gap sensitivity and dependence-aware inference qualify the forecasting evidence. These results provide a data-driven screening basis for prioritizing candidate RSU sites before detailed feasibility, communications, and engineering assessment.