Shared UAV Mission Paths for Multi-Airport Scheduling Optimization

A simulation dataset for optimizing shared passenger-carrying unmanned aerial vehicle (UAV) routes in multi-airport environments. It was created by Chao Hong and last updated on May 19, 2026. The dataset likely contains operational parameters and passenger order characteristics used to model and minimize total system navigation time.

Use Cases

• Benchmarking route optimization algorithms based on simulated passenger orders and UAV parameters.
• Training or testing machine learning models for dynamic seat allocation and real-time scheduling updates.
• Analyzing the efficiency of shared mobility systems in regions with limited ground infrastructure.

Strengths

• The underlying model achieved a 27.3% reduction in total navigation time compared to non-pooling baselines.
• The simulation reported an average UAV utilization rate of 78.6%.
• The proposed SLALO optimizer demonstrated superior convergence speed and solution quality against GA, PSO, and standard ALO.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The 5.5 KB file size suggests a very limited scope, likely containing model parameters or summary results rather than extensive raw data.

Provenance

Source

figshare

Collection Method

Simulation results from a proposed self-learning Ant-Lion Optimizer (SLALO) model.

Freshness

Last updated 2026-05-19 17:31:13; freshness should be verified.

Geography

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