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Machine Learning for Improving Air Mobility Under Emergency Situations
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2024-01-01
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[PDF-4.21 MB]
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Edition:Final Report: October 2021 – January 2024
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Abstract:This project investigates the integration of advanced machine learning and optimization techniques to improve air traffic management during emergencies. Over three phases, the study utilized AI to predict flight delays, optimize evacuation processes, and plan efficient evacuation flight paths, enhancing the resilience of air mobility systems in crises. The first phase developed an explainable GRU neural network to predict weather-related airport capacity constraints using historical data. The second phase employed Particle Swarm Optimization (PSO) to optimize air travel for emergency evacuations, demonstrating cost-effective and rapid mobilization of resources. The final phase introduced a hybrid model combining a genetic algorithm with a neural network to enhance evacuation flight planning. The study's findings highlight the potential of AI in emergency air mobility and offer recommendations for policymakers, airline operators, and researchers to advance these technologies and their practical applications.
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