This study presents a real-time framework for aircraft conflict detection and trajectory optimisation in Terminal Manoeuvring Areas (TMAs) during emergency weather events. The proposed approach integrates wake turbulence separation, node and link conflict evaluation, and dynamic operational constraints to ensure safe and efficient traffic flow under severe weather disruptions. Departing from conventional offline resolution methods, an enhanced Selective Simulated Annealing (SSA) algorithm is introduced to enable online conflict detection and mitigation in rapidly changing conditions. The framework is validated through a case study at Chengdu Shuangliu International Airport (CTU) TMA. A historical dataset of 451 arrival flights over an 8-hour period is duplicated to create a high-density scenario with 902 flights, allowing robust evaluation of scalability and responsiveness under significant traffic and weather stress. Results show that the SSA algorithm achieves minimal route extensions (0.11% on average) in thunderstorm conditions compared to clear weather, while maintaining delay reductions and operational stability. Sensitivity analyses on slot shift ranges and decision strategies confirm the framework’s resilience, and simulated runway blockages demonstrate the role of holding stacks in mitigating conflicts during single-runway closures. By integrating conflict detection with real-time trajectory optimisation, this research offers Air Traffic Control Officers (ATCOs) a practical decision-support tool to enhance safety, efficiency, and resilience in emergency weather operations within TMAs.
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