An airport passenger terminal simulator: A planning and design tool

doi:10.1016/S0928-4869(97)00018-9

Simulation Practice and Theory

Volume 6, Issue 4, 15 May 1998, Pages 387-396

https://doi.org/10.1016/S0928-4869(97)00018-9 Get rights and content

Abstract

Recent airport capacity studies have indicated that there is an imbalance in passenger terminal, airfield and airspace planning at many major airports. Traditionally, the emphasis has been on airfield and airspace development and analysis. Not much emphasis has been made on passenger terminal design. Therefore, there are many cases around the world exhibiting congestion problems at the airport passenger terminal as the number of air passengers continue to increase. This paper presents a generic simulation model for the final design of airport passenger terminal using SLAM II Simulation Language. The animation is presented on facility diagram which graphically portrays the layout of passenger terminal. Icons representing the entities of the system (both international and domestic passengers and baggage) are provided. The model has been verified and validated by data obtained from the Singapore Changi Airport.

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

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Processing passengers efficiently: An analysis of airport processing times for international passengers
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Citation Excerpt :
Simulation models have been applied to passenger movements with great success (Paullin, 1966; Ma, 2013; Ray and Claramunt, 2003; Kovacs et al., 2012). In particular, Agent based modelling (Bonabeau, 2002) is a very common type of simulation model for airport management, and as such has been applied to data from a range of terminals (Pendergraft et al., 2004; Jim and Chang, 1998; Gatersleben et al., 1999). However, while this approach gives an impression of the overall behaviour of the terminal based on known parameters, it neither gives managers insight into how passenger characteristics are reflected in the data nor does it give any estimation of the uncertainty around model predictions.
With immense and growing pressure on stakeholders in international airport terminals to process passengers faster than previously, there is a great benefit to understanding which factors affect passenger processing times and in which situations. In addition, storing and analysing the collected data in batch is itself a difficult and time consuming task that could be made much simpler with sequential analysis. We aim to present a method for airport managers to discover which variables are important to understanding passenger processing times and identifying problematic passenger profiles without the need for high computational capacity and full historical datasets.
In this paper we introduce Bayesian hierarchical models as a method of sequentially processing data, reducing computation time and obviating storage of large amounts of raw data. We use a range of exploratory models to identify which variables are important to predicting passenger processing time using a dataset from a day of operations at an international airport terminal, then compare a range of regression models. A Bayesian hierarchical regression model based on the model of best fit discovered through exploration is then applied to two subsets of data. We demonstrate that sequential updating based on daily data achieves similar results to batch processing based on full historical datasets and can therefore be used as an alternative in appropriate circumstances. Using the presented models, we find that the airline operating a flight is the most important variable to determining passenger processing time, followed by each passenger's age, sex and nationality. We demonstrate that in our dataset, the passenger profiles correlated with higher mean processing times overall were not the same as those passengers most problematic for meeting processing time targets.
An alternative methodology for planning baggage carousel capacity expansion: A case study of Incheon International Airport
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Most studies have either developed a generic model of the entire airport terminal or have narrowed their focus to an analysis of airport performance. Jim and Chang (1998) developed an airport passenger terminal simulation model using Simulation Language for Analogue Modeling II (SLAM II) and described passenger and baggage flows in the arrival and departure hall by considering a variety of input variables, such as aircraft type, domestic/international passengers and baggage, passenger group size, and passenger characteristics. Their model focused on an analysis of the general airport flow by including many elements of the entire terminal rather than analyzing details in a specific area.
Intensifying competition for air transportation passengers has led airports to research optimal designs and determine the infrastructure expansion capacities of their terminals. As a result, many researchers have studied this subject from a variety of perspectives. In this study, we propose an alternative methodology of determining the expansion of baggage carousel capacity over a series of steps that includes both a simulation and a cost-benefit analysis. The methodology consists of three stages. In the first stage, we forecast the volume of arriving passengers (excluding transfer passengers) and aircraft traffic with an autoregressive integrated moving average (ARIMA) model. Next, we conduct an elaborate analysis to estimate passenger delay using a discrete event simulation model in which we consider the conveyor load and the baggage carousel allocation to aircraft rates. Finally, we determine a plan to expand baggage carousel capacity that accounts for expansion costs and passenger benefits. Construction and conveyor costs were applied to expansion costs, and capacity expansion leads to passenger benefits due to reduced waiting time. Using a real case with 23 candidate baggage carousels at Incheon International Airport during 2013–2015, our experiments demonstrate the strength of the proposed methodology in planning appropriate capacity expansion that reflect the operational flow of passengers within the airport based on the future trend of passenger demand. In particular, our results show that carousel no. 18 should be expanded during the first quarter of 2013, carousels no. 17 and no. 19 should be expanded in 2014, and carousel no. 5 should be expanded in 2015 to obtain optimal benefit-cost ratios of 1.65, 1.79, and 1.76 for each year, respectively.
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Citation Excerpt :
Additionally, they can be extended to model other factors such as security risk (Wilson et al., 2006; Koch, 2004). Moreover, passenger facilitation ABMs have been applied to a range of ‘what-if’ scenarios relating to different (or new) airport configurations (Takakuwa and Oyama, 2003; Wilson et al., 2006), different flight schedules (Jim and Chang, 1998) and different resource assignment schedules (Eilon and Mathewson, 1973). However, ABMs do not provide an explicit representation or quantification of the causal relationships between different elements within the model.
This paper presents a novel framework for the modelling of passenger facilitation in a complex environment. The research is motivated by the challenges in the airport complex system, where there are multiple stakeholders, differing operational objectives and complex interactions and interdependencies between different parts of the airport system. Traditional methods for airport terminal modelling do not explicitly address the need for understanding causal relationships in a dynamic environment. Additionally, existing Bayesian Network (BN) models, which provide a means for capturing causal relationships, only present a static snapshot of a system.
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Citation Excerpt :
Spatial detail and passenger movement interactions – microscopic models simulate interactions between individual passengers and walls, facilities and other passengers whereas mesoscopic models tend to use predetermined paths and walking speed distributions to determine travel time between facilities. Hence, microscopic models are better suited to evaluating terminal layout as part of ‘final design’ (Jim and Chang, 1998). Microscopic models tend to capture passenger group interactions and their effect on passenger movement.
Airports represent the epitome of complex systems with multiple stakeholders, multiple jurisdictions and complex interactions between many actors. The large number of existing models that capture different aspects of the airport are a testament to this. However, these existing models do not consider in a systematic sense modelling requirements nor how stakeholders such as airport operators or airlines would make use of these models. This can detrimentally impact on the verification and validation of models and makes the development of extensible and reusable modelling tools difficult.
This paper develops from the Concept of Operations (CONOPS) framework a methodology to help structure the review and development of modelling capabilities and usage scenarios. The method is applied to the review of existing airport terminal passenger models. It is found that existing models can be broadly categorised according to four usage scenarios: capacity planning, operational planning and design, security policy and planning, and airport performance review. The models, the performance metrics that they evaluate and their usage scenarios are discussed. It is found that capacity and operational planning models predominantly focus on performance metrics such as waiting time, service time and congestion whereas performance review models attempt to link those to passenger satisfaction outcomes. Security policy models on the other hand focus on probabilistic risk assessment. However, there is an emerging focus on the need to be able to capture trade-offs between multiple criteria such as security and processing time. Based on the CONOPS framework and literature findings, guidance is provided for the development of future airport terminal models.

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An airport passenger terminal simulator: A planning and design tool

Abstract

Planning and Design of Airport

Land Use and Environmental Control

Discrete-Event System Simulation

Introduction to Simulation and SLAM II