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.
TopBackground
Airport operational management and planning assessments are the subject of extensive studies in the field of both airside and landside modeling and optimization, thereby a huge number of existing models and simulations have been made available to both research domains and industrial applications. Existing studies and practices cover a broad range of predictive models for different aspects of decision-making process and multiple categories of airport management operations, both airside and landside, throughout elements and entities involved in the airport flow processes (Zografos et al., 2013; Ravizza et al., 2014; Bruno et al., 2019). However, existing studies about the decision support system are limited in airport surrounding traffic management.
Key Terms in this Chapter
Traffic Engineering: A subset of transportation engineering that covers a wide range of the traffic planning, road system designs, land uses, and terminal operations.
Predictive Analysis: An analytical technique that makes simulations and forecasting in regards to uncertainties and unknown events using a variety of mathematical processes, such as statistical modeling, data mining, machine learning, etc.
Data Mining: A process of extracting information and recognizing patterns in large datasets by combining statistical learning and database management.
Deep Learning: A subset of machine learning based on artificial neural networks with more layers, which can improve the model performance significantly.
Decision Support System: A computer-based framework that can process and analyze the large scale of data for extracting useful knowledges and information, which can be applied to solve problems in decision-making.
Smart Airport: A modern airport ideology that enables to perform planning and operational tasks in digitalized forms using hyper-connected technologies, including IoT, GPS, and sensors.
Machine Learning: A subject of artificial intelligence that aims at the task of computational algorithms, which allow machines to learning objects automatically through historical data.