Assessing airport terminal performance using a system dynamics model

https://doi.org/10.1016/j.jairtraman.2009.10.007Get rights and content

Abstract

Current models for airport terminal performance assessment require substantial modeling effort to be customized to the configuration of a particular airport terminal and to reflect adequately the alternative airport operational policies adopted in a user-friendly way. Therefore, there is a need to develop a generic, yet flexible decision-support tool that will facilitate high-level decision-making related to fundamental changes in the structure and operation of the airport terminal system. This paper presents a generic and easily customizable system dynamics based tool for assessing the performance of the Athens International Airport passenger terminal under different demand and resource deployment scenaria.

Introduction

Strategic decision-making related to airport terminal is a process of high complexity, as it demands the effective planning and coordination of multiple complex dynamic processes. The airport terminal is a highly complex large-scale system, as it involves a large number of entities, a large variety of types of services, complex interrelations between processes. In addition, airport terminal performance assessment involves multiple and sometimes conflicting objectives of the various stakeholder groups involved in and affected by airport terminal operations. In this context, to support effective airport terminal decision-making, several models, both analytical and simulation, have been developed. Most of the existing models require substantial modeling effort to be customized to the configuration and operational environment of any airport terminal in a user-friendly way. Therefore, there is an urgent need for developing a generic, yet flexible decision-support tool that will facilitate airport terminal strategic analysis.

Recently, a generic system dynamics based airport performance tool has been developed (Manataki and Zografos, 2009, Manataki and Zografos, 2009b) that can assist decision makers to examine, in an efficient way, airport terminal performance by generating alternative demand and resource deployment scenaria. The model is customized to reflect the operational environment of the Athens International Airport (AIA) and is applied to assess the performance of the airport's passenger terminal.

Section snippets

Problem context

The airport terminal constitutes a major element of the airport landside, as it is the boundary of the airport towards the airside. It is associated with the processes and the facilities that airport customer groups visit while at the airport. Airport terminal strategic planning and operations exhibits some distinct characteristics:

  • It involves multiple stakeholders, representing multiple and sometimes conflicting operational objectives.

  • Most airport decision makers are interested in addressing a

Modeling approach and design

The selection of the modeling approach for the development of the airport terminal model is based on the need to address the requirements for a generic tool, so as to deal with the basic needs of any airport, for a tool capable to support strategic level performance assessment, and for flexibility, so as to capture easily any airport terminal configuration and operational environment.

These requirements influence the adoption of a holistic perspective for the design and development of a

Model application: the Athens International Airport

The proposed model has been customized and demonstrated using real data of the Athens International Airport. Specifically, the model has been configured to account for both the topological and the operational dimensions of the Athens International Airport, in terms of the physical airport layout and the operational policies followed. In this paper, the outcomes of the model application concerning departing passenger processing and associated flows through the various airport functional areas

Concluding remarks

A system dynamics simulation model for airport terminal analysis has been presented. The proposed tool supports effective strategic decision-making, providing the capability of conducting impact analyses with respect to a variety of airport terminal performance measures, i.e., capacity, delays/waiting times, level of service, resource utilization, passenger accumulations at various facilities and time frames, etc. The major strengths of the proposed model is that it is generic, yet flexible,

References (8)

There are more references available in the full text version of this article.

Cited by (51)

  • Design of passengers’ circulation areas at the transfer station: An automated hybrid simulation-differential evolution framework

    2018, Simulation Modelling Practice and Theory
    Citation Excerpt :

    Given the limitation TCRP Report-165, several researchers have tried to develop and improve the queuing model as well as simulation models for the analysis and design of both passengers and vehicular facilities. Researchers have employed queuing analytical modeling and simulation approach to carry out studies on: passengers’ facilities in the airport terminals [5–9], pedestrian flow in residential and commercial buildings [10–13], for the performance evaluation and design of urban rail transit stations [14–21], for the passengers’ flow at the BRT stations or bus terminals [22–24]. Similarly, some researchers focused on the vehicular traffic facilities on the highways [25–27].

  • Performance measurement practices in airports: Multidimensionality and utilization patterns

    2018, Journal of Air Transport Management
    Citation Excerpt :

    Airport efficiency/productivity, including their determinants (e.g. Adler and Liebert, 2014; Assaf et al., 2012; Barros and Weber, 2009; Chang et al., 2013; Chi-Lok and Zhang, 2009; Fan et al., 2014; Gillen and Lall, 1997; Merkert and Mangia, 2014; Oum et al., 2006; Oum et al., 2008; Sarkis and Talluri, 2004; Voltes-Dorta and Pagliarib, 2012; Yang and Fu, 2015; Yang and Zhang, 2011); the methods used (e.g. Abrate and Erbetta, 2010; Assaf et al., 2012; Assaf et al., 2014; Barros, 2009; Barros and Dieke, 2008; Jessop, 2009; Lai et al., 2015; Martín-Cejas, 2005; Suzuki et al., 2010); and benchmarking studies (e.g. Graham, 2005; Lai et al., 2012; Merkert et al., 2012; Morrison, 2009; Vogel and Graham, 2013); Service quality, including passenger's perceptions and satisfaction (e.g. Bezerra and Gomes, 2015; Perng et al., 2010; Bogicevic et al., 2013; Prebezac et al., 2010; Chen, 2007; Lupo, 2015; Janic, 2003; Fodness and Murray, 2007); level of service assessment (e.g. Borille and Correia, 2013; Correia and Wirasinghe, 2007; Correia et al., 2008; De Barros, Somasundaraswaran and Wirasinghe, 2007; Omer and Khan, 1988); and simulation models of airport operations (e.g. Andreatta et al., 2007; Ignaccolo, 2003; Manataki and Zografos, 2010; Zografos et al., 2013; Zografos and Madas, 2006); Safety performance (e.g. Chang et al., 2015; Enoma and Allen, 2007; Enoma et al., 2009; Leva et al., 2015; Pacheco et al., 2014; Roelen and Blom, 2013);

View all citing articles on Scopus
View full text