Malandri, Caterina
(2020)
How to cope with air transport disruptions: airport airside resilience and vulnerability, [Dissertation thesis], Alma Mater Studiorum Università di Bologna.
Dottorato di ricerca in
Ingegneria civile, chimica, ambientale e dei materiali, 32 Ciclo. DOI 10.48676/unibo/amsdottorato/9480.
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Abstract
The efficiency of airport airside operations is often compromised by unplanned disruptive events of different kinds, such as bad weather, strikes or technical failures, which negatively influence the punctuality and regularity of operations, causing serious delays and unexpected congestion. They may provoke important impacts and economic losses on passengers, airlines and airport operators, and consequences may propagate in the air network throughout different airports. In order to identify strategies to cope with such events and minimize their impacts, it is crucial to understand how disruptive events affect airports’ performance. The research field related with the risk of severe air transport network disruptions and their impact on society is related to the concepts of vulnerability and resilience. The main objective of this project is to provide a framework that allows to evaluate performance losses and consequences due to unexpected disruptions affecting airport airside operations, supporting the development of a methodology for estimating vulnerability and resilience indicators for airport airside operations. The methodology proposed comprises three phases. In the first phase, airside operations are modelled in both the baseline and disrupted scenarios. The model includes all main airside processes and takes into consideration the uncertainties and dynamics of the system. In the second phase, the model is implemented by using a generic simulation software, AnyLogic. Vulnerability is evaluated by taking into consideration the costs related to flight delays, cancellations and diversions; resilience is determined as a function of the loss of capacity during the entire period of disruption. In the third phase, a Bayesian Network is built in which uncertain variables refer to airport characteristics and disruption type. The Bayesian Network expresses the conditional dependence among these variables and allows to predict the impacts of disruptions on an airside system, determining the elements which influence the system resilience the most.
Abstract
The efficiency of airport airside operations is often compromised by unplanned disruptive events of different kinds, such as bad weather, strikes or technical failures, which negatively influence the punctuality and regularity of operations, causing serious delays and unexpected congestion. They may provoke important impacts and economic losses on passengers, airlines and airport operators, and consequences may propagate in the air network throughout different airports. In order to identify strategies to cope with such events and minimize their impacts, it is crucial to understand how disruptive events affect airports’ performance. The research field related with the risk of severe air transport network disruptions and their impact on society is related to the concepts of vulnerability and resilience. The main objective of this project is to provide a framework that allows to evaluate performance losses and consequences due to unexpected disruptions affecting airport airside operations, supporting the development of a methodology for estimating vulnerability and resilience indicators for airport airside operations. The methodology proposed comprises three phases. In the first phase, airside operations are modelled in both the baseline and disrupted scenarios. The model includes all main airside processes and takes into consideration the uncertainties and dynamics of the system. In the second phase, the model is implemented by using a generic simulation software, AnyLogic. Vulnerability is evaluated by taking into consideration the costs related to flight delays, cancellations and diversions; resilience is determined as a function of the loss of capacity during the entire period of disruption. In the third phase, a Bayesian Network is built in which uncertain variables refer to airport characteristics and disruption type. The Bayesian Network expresses the conditional dependence among these variables and allows to predict the impacts of disruptions on an airside system, determining the elements which influence the system resilience the most.
Tipologia del documento
Tesi di dottorato
Autore
Malandri, Caterina
Supervisore
Dottorato di ricerca
Ciclo
32
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
Air transport; Disruption; Airside operations; Airport operations; Resilience; Vulnerability; Discrete Event simulation; Bayesian Networks; Delays; Turnaround operations; Transportation; AnyLogic; Capacity.
URN:NBN
DOI
10.48676/unibo/amsdottorato/9480
Data di discussione
24 Marzo 2020
URI
Altri metadati
Tipologia del documento
Tesi di dottorato
Autore
Malandri, Caterina
Supervisore
Dottorato di ricerca
Ciclo
32
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
Air transport; Disruption; Airside operations; Airport operations; Resilience; Vulnerability; Discrete Event simulation; Bayesian Networks; Delays; Turnaround operations; Transportation; AnyLogic; Capacity.
URN:NBN
DOI
10.48676/unibo/amsdottorato/9480
Data di discussione
24 Marzo 2020
URI
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