Shared UAV Passenger 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 in May 2026, likely contains outputs comparing the proposed self-learning Ant-Lion Optimizer (SLALO) against baseline algorithms like Genetic Algorithm and Particle Swarm Optimization. The 9.5 KB XLS file includes metrics such as total navigation time reduction and UAV utilization rate from the simulation.

Use Cases

• Benchmarking optimization algorithms for UAV scheduling based on reported reductions in total navigation time.
• Analyzing the impact of passenger order characteristics (e.g., party size, preferred UAV type) on system efficiency.
• Studying UAV operational parameters like seating capacity and endurance in a shared mobility simulation context.
• Evaluating the performance of a quadratic soft time window mechanism for managing arrival deviations in transport systems.

Strengths

• Includes specific performance metrics: a 27.3% reduction in total navigation time and an average UAV utilization rate of 78.6%.
• Comparative analysis provided against established algorithms (GA, PSO, ALO).
• Shared under a permissive CC-BY-4.0 license, allowing for reuse and modification.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The 9.5 KB size indicates a very small dataset, likely containing summary results rather than raw simulation logs.

Provenance

Source

figshare, author Chao Hong.

Collection Method

Likely generated from simulation experiments described in the associated study.

Freshness

Last updated 2026-05-19 17:31:18; freshness should be verified.

Geography

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