AI Insight
This study presents a shared unmanned aerial vehicle (UAV) route scheduling optimization model designed for multi-airport, multi-station urban air mobility environments. The model accounts for passenger order characteristics and UAV operational parameters, incorporating a quadratic soft time window mechanism to handle arrival time deviations while minimizing total navigation time. The proposed Self-Learning Ant-Lion Optimizer (SLALO) algorithm outperformed Genetic Algorithm, Particle Swarm Optimization, and standard Ant-Lion Optimizer benchmarks, achieving a 27.3% reduction in total navigation time and an average UAV utilization rate of 78.6% in simulation tests.
Why it matters
Shared UAV scheduling systems could provide practical air mobility solutions in areas with limited ground transportation infrastructure, potentially reducing operational costs and improving passenger service efficiency. The framework has particular relevance for remote or border regions where conventional transit networks are underdeveloped.
by Chao Hong, Yi Yan, Zhigang Lian, Xiangrong Li
Shared passenger-carrying unmanned aerial vehicle (UAV) systems offer a promising solution for urban air mobility, yet their real-time scheduling in multi-airport environments remains challenging due to heterogeneous passenger demands, model compatibility constraints, and the need to balance efficiency with service quality. This study addresses these challenges by proposing a shared UAV route scheduling optimization model for multi-airport, multi-station systems. The model integrates passenger order characteristics (origin, destination, party size, preferred UAV type, and desired arrival time) with UAV operational parameters (current location, remaining endurance, seating capacity, speed, and model), and incorporates a quadratic soft time window mechanism to flexibly manage arrival deviations while minimizing total system navigation time. To solve this discrete combinatorial optimization problem, we develop a self-learning Ant-Lion Optimizer (SLALO) with natural number encoding that dynamically tracks seat availability and updates route assignments in real-time. Simulation results demonstrate that the proposed approach achieves a 27.3% reduction in total navigation time compared to non-pooling baselines, with an average UAV utilization rate of 78.6%. Comparative analysis against Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and standard Ant-Lion Optimizer (ALO) shows that SLALO achieves superior convergence speed and solution quality. The proposed framework is particularly relevant for border regions with limited ground infrastructure, where intelligent shared mobility systems can serve as a critical driver for cultivating new quality productive forces and supporting regional economic integration. These findings highlight the potential of shared UAV systems to improve urban air mobility efficiency, reduce operational costs, and enhance passenger satisfaction.