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Paper FR-LM-T39.3

YANG, Huijuan (Ecole Nationale de l'Aviation Civile), Delahaye, Daniel (Ecole Nationale de l'Aviation Civile), Ma, Ji (Civil Aviation University of China)

Enhancing Airport Ground Operations Efficiency through Speed-Controlled Aircraft Taxiing and Future Guidance Readiness

Scheduled for presentation during the Regular Session "S39a-Data-Driven Optimization in Intelligent Transportation Systems" (FR-LM-T39), Friday, November 21, 2025, 11:10−11:30, Coolangata 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords AI, Machine Learning for Dynamic Traffic Signal Control and Optimization, Data Analytics and Real-time Decision Making for Autonomous Traffic Management, AI, Machine Learning for Real-time Traffic Flow Prediction and Management

Abstract

This paper presents a speed-controlled aircraft taxiing optimisation approach designed to improve airport surface operations by strategically managing ground movements. The proposed method integrates a sliding window-based decomposition strategy to dynamically resolve conflicts, enabling real-time adaptability to fluctuating traffic conditions. Operationally, the approach prioritises minimising holding point delays over pushback delays to promote smoother flow and better gate utilisation. Simulation results at Paris Charles de Gaulle (CDG) Airport demonstrate that the algorithm significantly reduces average delays, with many flights requiring no holding or pushback adjustments. Even during peak periods, the system maintains low average taxiing, holding, and pushback times, highlighting its robustness under congested conditions. In addition to enhancing current operational efficiency, the proposed speed control framework establishes a foundation for future innovations, such as the implementation of follow-me vehicles to guide aircraft during taxiing, further improving safety, predictability, and sustainability in airport ground operations.

 

 

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