Shared UAV Route Scheduling Simulation Results for Multi-Airport Systems

Simulation results from a study proposing a shared unmanned aerial vehicle (UAV) route scheduling optimization model for multi-airport, multi-station systems. The dataset, shared by Chao Hong on figshare, was last updated on May 19, 2026. It likely contains results comparing the proposed self-learning Ant-Lion Optimizer (SLALO) against other algorithms, showing a 27.3% reduction in total navigation time and a 78.6% average UAV utilization rate.

Use Cases

• Benchmarking optimization algorithms based on reported metrics like total navigation time and UAV utilization rate.
• Analyzing shared mobility system performance based on integrated passenger order and UAV operational parameters described.
• Modeling real-time scheduling constraints for autonomous vehicles based on the described quadratic soft time window mechanism.

Strengths

• Includes specific performance metrics from the simulation, such as a 27.3% reduction in total navigation time.
• Reports a concrete average UAV utilization rate of 78.6%.
• Results are from a comparative analysis against established algorithms (GA, PSO, ALO).

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 (5.5 KB), indicating limited scope or summary-level results.

Provenance

Source

figshare, author Chao Hong.

Collection Method

Likely generated from simulation experiments described in the associated study.

Freshness

Last updated 2026-05-19 17:31:08; freshness should be verified.

Geography

The framework is described as relevant for border regions with limited ground infrastructure, but specific simulation geography is not stated.