A review of airport passenger terminal operations analysis and modelling

Tosic, Vojin S.

doi:10.1016/0965-8564(92)90041-5

Cited by 28 publications

(19 citation statements)

References 32 publications

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“…IATA (2014), Correia and Wirasinghe (2013), Ashford et al (2013) and Tošić (1992) provide several detailed performance and operational models that deal with landside processes at airports. Wilke et al (2014), Norin et al (2012) and Fricke and Schultz (2009) focused their studies on surface operations and airside procedures (mainly on-ground turnaround).…”

Section: Conceptual Framework and Taxonomy For The Airport Operationsmentioning

confidence: 99%

Reduction of Uncertainty Propagation in the Airport Operations Network

Sanz

Comendador

Valdés

2016

Libro De Actas CIT2016. XII Congreso De Ingeniería Del Transporte

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Airport operations are a complex system involving multiple elements (ground access, landside, airside and airspace), stakeholders (ANS providers, airlines, airport managers, policy makers and ground handling companies) and interrelated processes. To ensure appropriate and safe operation it is necessary to understand these complex relationships and how the effects of potential incidents, failures and delays (due to unexpected events or capacity constraints) may propagate throughout the different stages of the system. An incident may easily ripple through the network and affect the operation of the airport as a whole, making the entire system vulnerable. A holistic view of the processes that also takes all of the parties (and the connections between them) into account would significantly reduce the risks associated with airport operations, while at the same time improving efficiency. Therefore, this paper proposes a framework to integrate all relevant stakeholders and reduce uncertainty in delay propagation, thereby lowering the cause-effect chain probability of the airport system (which is crucial for the operation and development of air transport). Firstly, we developed a model (map) to identify the functional relationships and interdependencies between the different stakeholders and processes that make up the airport operations network. This will act as a conceptual framework. Secondly, we reviewed and characterised the main causes of delay. Finally, we extended the system map to create a probabilistic graphical model, using a Bayesian Network approach and influence diagrams, in order to predict the propagation of unexpected delays across the airport operations network. This will enable us to learn how potential incidents may spread throughout the network creating unreliable, uncertain system states. Policy makers, regulators and airport managers may use this conceptual framework (and the associated indicators) to understand how delays propagate across the airport network, thereby enabling them to reduce system vulnerability, and increase its robustness and efficiency.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3484

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Section: Conceptual Framework and Taxonomy For The Airport Operationsmentioning

confidence: 99%

Reduction of Uncertainty Propagation in the Airport Operations Network

Sanz

Comendador

Valdés

2016

Libro De Actas CIT2016. XII Congreso De Ingeniería Del Transporte

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“…The problems related to airport terminal operations have received a considerable amount of attention from airport operators and researchers around the world. Both analytical and simulation models have been developed to support airport terminal decision-making [3,4,5]. In an early work, Ashford et al put forward the importance of understanding of passenger's behavior in design airport terminal [6].…”

Section: Introductionmentioning

confidence: 99%

“…Yuan and McCabe validated a computer model with service times and arrival rates at a check-in counter, a security checkpoint, and a boarding gate [7]. A review of the studies of airport passenger behaviors including both departing passengers and arrival passengers is presented in [3].…”

Section: Introductionmentioning

confidence: 99%

“…It was recognized that check-in as perhaps the major point of congestion in the terminal. Given the nature of passenger's activity-processing in an airport, the most prevalent analytical approach is using queuing theory or queuing network [3,9,10]. The purpose of using queuing theory is to encapsulate the essential stochastic nature of passenger's behavior as well as of the operators.…”

Section: Introductionmentioning

confidence: 99%

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Temporal Patterns Underlying Domestic Departure Passengers Behavior in the Airport

Wang¹,

Liu²,

Hu³

et al. 2020

IEEE Access

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Air travelers' behavior is closely related to the operational performance of any airport terminal. Much of previous research has focused on how airport operators balance the number of facilities in a terminal and the Level of Service (LOS), while the behavior of passengers is less considered. Not much is known, however, about passenger's behavior during the entire departure process in an airport. In this study, we analyze empirical departure passenger's data to gain an insight into the regular patterns of their activities in an airport. We find that there exist two distinguished temporal patterns during two discretionary periods-post check-in and pre-security check, post security check and pre-boarding. The time that departure passengers spend in these two periods is well approximated by a double power-law distribution and an exponential truncated power-law distribution respectively. The two distinguished distributions suggest that there may be different mechanisms underlying passengers' behavior as indicated by previous studies on human mobility. We introduce a stochastic model that considers traveling experience and time pressure to capture the decision dynamics of human behavior. Simulation results suggest that traveling experience and time pressure dominate passenger's decisions before and after security respectively. Our findings contribute to a better understanding of human dynamics, and also offer the potential for optimizing and simulation of airport terminal operation.

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“…A detailed description of these applications is provided in the survey by Tošić [216]. The approaches of Horonjeff [100], Paullin and Horonjeff [184], de Neufville and Grillot [55], Wirasinghe and Shehata [232], Wirasinghe and Bandara [231], and de Barros and Tomber [53] rely on the fluid approximation.…”

Section: Service Systemsmentioning

confidence: 99%