Risk-aware hierarchical multi-objective planning of RSU deployment and UAV scheduling for urban V2X networks

Urban Vehicle-to-Everything (V2X) services demand infrastructure plans that remain effective under pronounced spatiotemporal demand shifts and operational uncertainty. Existing studies often optimize roadside unit (RSU) deployment or unmanned aerial vehicle (UAV) operations in isolation, rely on scalar objectives, and simplify feasibility constraints, which limits their value for actionable planning. This paper develops a hierarchical multi-objective planning framework that jointly decides (i) where and how many RSUs to deploy and (ii) how to schedule a UAV fleet over time to complement the static backbone. The upper level searches for non-dominated infrastructure configurations that balance total cost, spatiotemporal coverage, and robustness, while the lower level evaluates each configuration through a state-aware greedy dispatch policy that explicitly models UAV operational modes and battery/charging dynamics. Robustness is optimized via a weighted composite of redundancy, coverage variability, and mission balance, and scenario-based Conditional Value-at-Risk (CVaR) is reported as a secondary tail-risk indicator under demand perturbations and component failures. Experiments on realistic urban traffic data, together with policy validation on reduced instances, baseline comparisons, and multi-seed stability analysis, demonstrate that the framework yields diverse and interpretable planning trade-offs.