Shared UAV Route Scheduling Simulation Results for Multi-Airport Systems

Simulation results demonstrate a 27.3% reduction in total navigation time compared to non-pooling baselines, with an average UAV utilization rate of 78.6%. This dataset, created by Chao Hong and last updated in May 2026, contains results from a model optimizing shared passenger-carrying unmanned aerial vehicle (UAV) route scheduling. The model integrates passenger order characteristics with UAV operational parameters and uses a self-learning Ant-Lion Optimizer (SLALO) for real-time assignment.

Use Cases

• Benchmarking optimization algorithms based on the reported 27.3% reduction in total navigation time
• Studying shared mobility system efficiency based on the 78.6% average UAV utilization rate
• Modeling passenger demand heterogeneity based on described order characteristics like party size and preferred UAV type
• Evaluating real-time scheduling frameworks for multi-airport environments based on the described dynamic seat availability tracking

Strengths

• Includes specific performance metrics: a 27.3% reduction in total navigation time and a 78.6% average UAV utilization rate
• Compares results against established algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and standard Ant-Lion Optimizer (ALO)
• Model integrates multiple data dimensions: passenger order characteristics and UAV operational parameters

Limitations

• Row count is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download
• The dataset is very small at 5.5 KB, indicating limited scope

Provenance

Source

Chao Hong

Collection Method

Likely generated via simulation of a proposed shared UAV route scheduling optimization model.

Freshness

Last updated 2026-05-19 17:31:11; freshness should be verified

Geography

The framework is described as particularly relevant for border regions with limited ground infrastructure.