Improved Ant Colony Algorithm Parameters for Airport Taxiway Optimization

Featuring parameters for an Improved Ant Colony Algorithm (IACA) designed to optimize airport taxiway operations and departure schedules. It is associated with a study demonstrating a 17.8% reduction in taxiing costs and an optimized total cost of USD 8,163.44. The dataset is 5.5 KB in size and was authored by Jiyu Tang.

Use Cases

• Calibrate the IACA's negative feedback mechanism using the provided parameters to replicate the reported USD 1,412.71 average cost saving.
• Validate the algorithm's performance in generating multiple taxiing configurations for different departure intervals as described in the source study.
• Use the documented parameters to test the two-stage optimization framework for minimizing taxiing costs and mitigating ground conflicts.

Strengths

• The associated study reports concrete performance metrics, including a 17.8% reduction in taxiing costs.
• The dataset is explicitly linked to a published research framework for airport surface operation optimization.
• It is shared under a permissive CC BY 4.0 license, allowing for reuse and modification.

Limitations

• The dataset is very small at 5.5 KB, indicating it likely contains only algorithm parameters, not operational data.
• No sample data or column descriptions are available, making the exact data structure and content unknown.
• The experimental results are tied to a specific study context, and generalizability to other airports is unverified.

Provenance

Source

figshare, authored by Jiyu Tang.

Collection Method

Generated as part of a research study proposing a two-stage optimization framework for airport surface operations.

Freshness

Last updated on 2026-03-20.