Decision-Making Approaches for Airport Surrounding Traffic Management

Abstract

This article is designed to investigate the automatic decision support system in terms of analyzing airport surrounding road networks. Several decision-making approaches are illustrated and examined based on a broad range of data-driven methods, including data mining, machine learning, and deep learning. Each method has been investigated by providing a survey study that involves the most recent and comprehensive understanding of traffic engineering. As a specific problem in urban traffic congestions, airport surrounding traffic management can be referenced from similar studies in urban traffic congestion. The study can be used in improving the airport services in terms of operational efficiency and airport landside management, further supporting the construction of the smart airport.