Acharya, Kamal (University of Maryland, Baltimore County), Alvaro, Velasquez (University of Colorado), Liang, Sun (New Mexico State University), Liu, Yongxin (Embry-Riddle Aeronautical University), Liu, Dahai (Embry-Riddle Aeronautical University), Song, Houbing (University of Maryland, Baltimore County)

Improving Air Mobility for Pre-Disaster Planning with Neural Network Accelerated Genetic Algorithm

Scheduled for presentation during the Poster Session "Air Traffic Management" (WeBT14), Wednesday, September 25, 2024, 14:30−16:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 7, 2024

Abstract

Weather disaster related emergency operations pose a great challenge to air mobility in both aircraft and airport operations, especially when the impact is gradually approaching. We propose an optimized framework for adjusting airport operational schedules for such pre-disaster scenarios. We first, aggregate operational data from multiple airports and then determine the optimal count of evacuation flights to maximize the impacted airport’s outgoing capacity with- out impeding regular air traffic. We then propose a novel Neural Network (NN) accelerated Genetic Algorithm(GA) for evacuation planning. Our experiments show that integration yielded comparable results but with smaller computational overhead. We find that the utilization of a NN enhances the efficiency of a GA, facilitating more rapid convergence even when operating with a reduced population size. This effectiveness persists even when the model is trained on data from airports different from those under test. Data and code available at ”https://github.com/lotussavy/ITSC-2024”.