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Comparison of entry control point and arrivals concourse instantaneous utilization for the simulation models with and without a retail opportunity.
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Simulating passenger flows within airports is very important as it can provide an indication of queue lengths, bottlenecks, system capacity and overall level of service. To date, visual simulation tools such as agent-based models have focused on processing formalities such as check-in, and not incorporate discretionary activities such as duty-free...
Citations
... These models simulate the behaviour and movement of individual passengers (i.e. the 'agents') given the spatial layout of the terminal building, where the agents are assigned 'goals' according to the steps in the passenger facilitation process. (Cheng et al., 2014;Enciso et al., 2016;Kleinschmidt et al., 2011;Ma et al., 2011;Schultz and Fricke, 2011;Verma et al., 2020). An alternate approach to modelling passenger facilitation is that of queuing theory and queue networks. ...
Passengers go through different handling processes inside airport terminal buildings. The quality of these processes
is usually measured by the time passengers require and by the level of comfort experienced by them. We
present an analysis of behavioural patterns in queues at check-in desks and security controls, which are two of
the most critical processes regarding passenger service. The passengers’ flow is simulated to obtain queue lengths
at one busy European airport between 2014 and 2016, supported by real flight data. Simulation is designed as a
store-and forward cell-based system, whose parameters have been tuned and validated with real data from
observations and empirical capacity and demand studies within the airport. Random Forest algorithms are then
implemented to develop different models for each parameter prediction, after a data analysis stage based on
statistical and visualization methods. Feature analysis techniques between dependent variables and the target
outputs (queue lengths) determine which are the fundamental elements to explain queue behaviour and to
predict target variables. We provide a method to forecast behavioural patterns at check-in desks and security
controls, to help airport operators to implement adequate response policies. Queue behavioural patterns are
captured by Machine Learning models, which can be used to offer improved passenger services (such as real-time
predictions for expected waiting time at queues), or can be considered in a dynamic approach for terminal
services design (as the entire progress of terminal handling depends on the stochastic behaviour of passengers).
This could be a key tool for managing passengers demand and optimise the infrastructure’s capacity through
resource allocation.
... Moreover, existing literature from fields of transportation research has focused on passenger requirements, flow models, and passenger experience on airports [e.g. 5,14,15,18], but neglected the associated (information) behavior of passengers. ...
In public transitional spaces, such as airports, users are faced with diverse challenges regarding information interaction and use. These challenges arise due to the scheduled and/or location-dependent procedures users are required to perform. Understanding what these users need or desire in the context of such spaces, what information is on offer, both online and in situ, and how these aspects interrelate is important to facilitate the design of systems that are accepted by the users concerned. However, very little is known about human information behavior (HIB) in public transitional spaces. As a starting point to understand how behavior in such spaces relates to or differs from information behavior in other contexts, holistically, I will create an explanatory model of airport information behavior by conducting an exploratory grounded theory based field study and relating my findings to those of existing models.
... The intelligent agents perceive information and perform actions to achieve specific goals (Zhang, Peeta, & Friesz, 2005). Several studies used ABM approach mainly to represent the physical and financial interdependencies (Amin, 2001;Kleinschmidt, Guo, Ma, & Yarlagadda, 2011;Macal & Sallach, 2000;Ma, Fookes, Kleinschmidt, & Yarlagadda, 2012;North, 2000;Sallach & Wolsko, 2001). ...
This paper provides a comprehensive review of literature on integrated infrastructure systems with an emphasis on interdependency types and definitions, complex network abstractions, and on different modeling and simulation techniques. An infrastructure interdependency matrix is presented to provide a better mapping of implementation, utilization, and in some cases a lack of integration among infrastructure systems. It is expected that this review will contribute to the advancement of the growing area of integrated infrastructure systems. Assessment of the research on integrated infrastructure modeling literature showed that the focus of most modeling efforts is mainly on short term operational purposes and extreme events. Long-term efforts to examine the impacts and assessment of future policies and scenarios are needed. Although ‘integration’ is being used in many studies on civil infrastructure, the level of detail or type of integration is not consistent and in some cases almost trivial.
Airport retail revenue has long been recognized as a critical revenue stream to ensure an airport's financial sustainability and stability. However, there is a lack of simulation model on how airport terminal could be better designed to facilitate this vital revenue stream. This paper presents a conceptual agent-based simulation model on passengers shopping behavior in the airport context. This model attempts to investigate the relationship between terminal design and retail performance through different scenarios studies. Results show that finger pier terminal shape can have a negative impact on retail revenue if shops are decentralized. Terminal with centralized shopping areas also performed better than a terminal with decentralized shopping area. Future research directions were proposed at the end to improve the existing simulation model with the aim of making it an essential evaluation tool for future terminal design.
This paper presents a layered framework for the purposes of integrating different socio-technical systems (STS) models and perspectives into a whole-of-systems model. Holistic modelling plays a critical role in the engineering of STS due to the interplay between social and technical elements within these systems and resulting emergent behaviour.
The framework decomposes STS models into components, where each component is either a static object, dynamic object or behavioural object. Based on existing literature, a classification of the different elements that make up STS, whether it be a social, technical or a natural environment element, is developed; each object can in turn be classified according to the STS elements it represents. Using the proposed framework, it is possible to systematically decompose models to an extent such that points of interface can be identified and the contextual factors required in transforming the component of one model to interface into another are obtained.
Using an airport inbound passenger facilitation process as a case study socio-technical system, three different models are analysed: a Business Process Modelling Notation (BPMN) model, Hybrid Queue-based Bayesian Network (HQBN) model and an Agent Based Model (ABM). It is found that the framework enables the modeller to identify non-trivial interface points such as between the spatial interactions of an ABM and the causal reasoning of a HQBN, and between the process activity representation of a BPMN and simulated behavioural performance in a HQBN. Such a framework is a necessary enabler in order to integrate different modelling approaches in understanding and managing STS.
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.
A method to integrate a BN complex systems model with stochastic queuing theory is developed based on the properties of the Poisson and exponential distributions. The resultant Hybrid Queue-based Bayesian Network (HQBN) framework enables the simulation of arbitrary factors, their relationships, and their effects on passenger flow and vice versa.
A case study implementation of the framework is demonstrated on the inbound passenger facilitation process at Brisbane International Airport. The predicted outputs of the model, in terms of cumulative passenger flow at intermediary and end points in the inbound process, are found to have an R2R2 goodness of fit of 0.9994 and 0.9982 respectively over a 10 h test period. The utility of the framework is demonstrated on a number of usage scenarios including causal analysis and ‘what-if’ analysis. This framework provides the ability to analyse and simulate a dynamic complex system, and can be applied to other socio-technical systems such as hospitals.
Todaytextquoterights software-intensive systems are among the most complex artifacts created by men. This is due to ever increasing requirements and functionality of the software on the one hand, and to rising structural complexity with respect to size, interconnectedness, and distribution on the other hand. Engineering and controlling these systems pushes existing software engineering approaches to (and beyond) their limits. This paper describes the concept of IT ecosystems as a new approach for addressing this challenge from the perspective of software engineering. The concept and approaches described were developed in a large interdisciplinary research project (www.it-oekosysteme.org); we present first results including a validation scenario of a smart airport, which has been devised and implemented in the project, aiming at a comprehensive approach to IT ecosystems engineering.
