Shared UAV Passenger Demand and Route Scheduling Simulation Data

Simulation data for a shared unmanned aerial vehicle (UAV) route scheduling optimization model in multi-airport environments. The dataset, created by Chao Hong and last updated in May 2026, models passenger order characteristics and UAV operational parameters to minimize total system navigation time. Results indicate the proposed SLALO optimizer achieved a 27.3% reduction in navigation time compared to non-pooling baselines.

Use Cases

• Benchmarking route optimization algorithms based on simulated passenger orders and UAV parameters.
• Analyzing the impact of passenger party size and preferred UAV type on scheduling efficiency.
• Evaluating the performance of soft time window mechanisms for managing arrival time deviations.
• Studying UAV utilization rates and system efficiency in multi-airport, multi-station environments.

Strengths

• Includes specific performance metrics from the simulation, such as a 27.3% reduction in total navigation time.
• Models a detailed set of parameters including passenger origin/destination, party size, UAV seating capacity, and remaining endurance.
• Comparative analysis results against Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and standard Ant-Lion Optimizer (ALO) are provided.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is simulation-based and may not reflect real-world operational conditions.

Provenance

Source

figshare, author Chao Hong.

Collection Method

Simulation results from a proposed shared UAV route scheduling optimization model.

Freshness

Last updated 2026-05-19 17:31:17; 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.