Redirigiendo al acceso original de articulo en 21 segundos...
Inicio  /  Aerospace  /  Vol: 8 Par: 6 (2021)  /  Artículo
ARTÍCULO
TITULO

Systemic Agent-Based Modeling and Analysis of Passenger Discretionary Activities in Airport Terminals

Adin Mekic    
Seyed Sahand Mohammadi Ziabari and Alexei Sharpanskykh    

Resumen

Discretionary activities such as retail, food, and beverages generate a significant amount of non-aeronautical revenue within the aviation industry. However, they are rarely taken into account in computational airport terminal models. Since discretionary activities affect passenger flow and global airport terminal performance, discretionary activities need to be studied in detail. Additionally, discretionary activities are influenced by other airport terminal processes, such as check-in and security. Thus, discretionary activities need to be studied in relation to other airport terminal processes. The aim of this study is to analyze discretionary activities in a systemic way, taking into account interdependencies with other airport terminal processes and operational strategies used to manage these processes. An agent-based simulation model for airport terminal operations was developed, which covers the main handling processes and passenger decision-making with discretionary activities. The obtained simulation results show that operational strategies that reduce passenger queue time or increase passenger free time can significantly improve global airport terminal performance through efficiency, revenue, and cost.

 Artículos similares

       
 
Tong Chen and Shinya Hanaoka    
Congestion and delays occur on airport surfaces as a result of a rapid increase in the demand for air transport. The aim of this study is to determine the differences between optimized and observed operations to improve airport surface operation at Tokyo... ver más
Revista: Aerospace

 
Ligang Yuan, Yang Zeng, Haiyan Chen and Jiazhi Jin    
In order to quantify the degree of influence of weather on traffic situations in real time, this paper proposes a terminal traffic situation prediction model under the influence of weather (TSPM-W) based on deep learning approaches. First, a feature set ... ver más
Revista: Aerospace

 
Xinglong Wang, Ziyan Chen and Kenan Li    
The increased number of severe weather events caused by global warming in recent years is a major turbulence factor for airport operation and results in more irregular flights. Quantifying the system response status towards turbulence is critical, in ord... ver más
Revista: Aerospace

 
Xiao Chu, Xianghua Tan and Weili Zeng    
Performing clustering analysis on a large amount of historical trajectory data can obtain information such as frequent flight patterns of aircraft and air traffic flow distribution, which can provide a reference for the revision of standard flight proced... ver más
Revista: Aerospace

 
Koki Higasa and Eri Itoh    
Despite the importance of controlling the inter-arrival times of flights to propose strategies for efficient arrival management by the Arrival Manager (AMAN), the specific guidelines of such adjustments and their effect on reducing delays have not been e... ver más
Revista: Aerospace