Machine Learning for Improving Air Mobility Under Emergency Situations

Liu, Yongxin; Liu, Dahai; Zhou, Yujing

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Machine Learning for Improving Air Mobility Under Emergency Situations

2024-01-01
By Liu, Yongxin ; Liu, Dahai ; Zhou, Yujing

File Language:

English

Details

Creators:

Liu, Yongxin ; Liu, Dahai ; Zhou, Yujing
Corporate Creators:

Center for Advanced Transportation Mobility, North Carolina A&T State University ; Embry-Riddle Aeronautical University
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report: October 2021 - January 2024
Corporate Publisher:

Center for Advanced Transportation Mobility, North Carolina A&T State University
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.
Format:

PDF
Funding:

69A3551747125
Download URL:

https://rosap.ntl.bts.gov/view/dot/79338/dot_79338_DS1.pdf
File Type:

[PDF - 4.21 MB]
Collection(s):

Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
Main Document Checksum:

urn:sha-512:e61e476f072659ae8870977e332380fd6dabad7da83a0b9321c1177ef6ec641b6066b4e6f2fc7cee88e8b3b6d05ffdb403a839905871a68feef7ded08cae8009

File Language:

English

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