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

Autor:	Acharya, Kamal, Velasquez, Alvaro, Liu, Yongxin, Liu, Dahai, Sun, Liang, Song, Houbing
Rok vydání:	2024
Předmět:	Computer Science - Neural and Evolutionary Computing Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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 without 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. Comment: 7 pages, 8 figures, ITSC 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.00790 Zobrazit plný text záznamu View this record from Arxiv