A Survey of Machine Learning Algorithms and Techniques for Air Mobility Under Emergency Situations

Abstract

Emergency situations in aviation pose serious risks to life and result in huge negative impacts on air mobility, causing a significant economic and reputation loss to airlines and airports. However, the decisions to deal with emergencies are usually made by flight dispatchers according to their experience, and they merely consider local-view optimization. Therefore, there is an urgent need to design a decision-making assistant system to alleviate the negative impact of perturbations on aviation air mobility from a global-view perspective. In this paper, we focus on using machine learning techniques and algorithms to solve emergency situations and improve air mobility. Through various techniques, researchers and computer scientists have studied how different reasons and factors affect air mobility. This paper will review previous research and provides a better view of using machine learning techniques and algorithms to improve air mobility in emergency situations.