AI-Based Smart Air Traffic Control Decision Support System

This paper presented the comprehensive design, algorithmic development, and simulation-based validation of an AI-Based Smart Air Traffic Control Decision Support System (AI-ATCDSS). The system integrates six functional modules within a cohesive service-oriented architecture, operating on a 1-second processing cycle and providing controllers with conflict alerts (STCA and MTCA), congestion forecasts, emergency management support, weather impact advisories, and ranked, confidence-scored resolution recommendations. Simulation validation across 120 experimental scenarios demonstrated: a 42.1% reduction in Loss of Separation events; a 28% improvement in departure on-time performance; a 34.4% reduction in average taxi delay; a 19.4% increase in runway throughput; a 23% reduction in overall controller workload (NASA-TLX); and a 49.7% improvement in emergency response time. These results provide strong evidence for the operational efficacy and safety value of the proposed system. The system is designed in accordance with ICAO Doc 4444 standards and EUROCONTROL HMI design principles, preserving the human controller's absolute authority over all ATC clearances. Future development should explore federated learning for multi-airport deployment, integration with UAS Traffic Management (UTM) / U-space frameworks, digital twin airspace modelling, Explainable AI (XAI) enhancements, and natural language voice interface integration. The AI-ATCDSS represents a technically sound, operationally realistic, and regulatory-compliant framework for the next-generation intelligent air traffic management system.  

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