An airport passenger terminal simulator: A planning and design tool
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Ratio-based design hour determination for airport passenger terminal facilities
2021, Journal of Air Transport ManagementSimulation model of security control lane operation in the state of the COVID-19 epidemic
2020, Journal of Air Transport ManagementProcessing passengers efficiently: An analysis of airport processing times for international passengers
2015, Journal of Air Transport ManagementCitation 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.
An alternative methodology for planning baggage carousel capacity expansion: A case study of Incheon International Airport
2015, Journal of Air Transport ManagementCitation Excerpt :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.
A Hybrid Queue-based Bayesian Network framework for passenger facilitation modelling
2014, Transportation Research Part C: Emerging TechnologiesCitation 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.
A review of models and model usage scenarios for an airport complex system
2013, Transportation Research Part A: Policy and PracticeCitation 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.