Modelling the resilience,friability and costs of an air transport network affected by a large-scale disruptive event |
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Affiliation: | 1. School of Automation, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, PR China;2. Key Lab. for Image Processing and Intelligent Control, Huazhong University of Science and Technology, Wuhan 430074, PR China;1. Dept. of Economics, University of Bologna, Italy;2. Dept. of Spatial Economics, VU University Amsterdam, Netherlands;3. Illumia S.P.A, Bologna, Italy |
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Abstract: | This paper deals with developing a methodology for estimating the resilience, friability, and costs of an air transport network affected by a large-scale disruptive event. The network consists of airports and airspace/air routes between them where airlines operate their flights. Resilience is considered as the ability of the network to neutralize the impacts of disruptive event(s). Friability implies reducing the network’s existing resilience due to removing particular nodes/airports and/or links/air routes, and consequently cancelling the affected airline flights. The costs imply additional expenses imposed on airports, airlines, and air passengers as the potentially most affected actors/stakeholders due to mitigating actions such as delaying, cancelling and rerouting particular affected flights. These actions aim at maintaining both the network’s resilience and safety at the acceptable level under given conditions.Large scale disruptive events, which can compromise the resilience and friability of a given air transport network, include bad weather, failures of particular (crucial) network components, the industrial actions of the air transport staff, natural disasters, terrorist threats/attacks and traffic incidents/accidents.The methodology is applied to the selected real-life case under given conditions. In addition, this methodology could be used for pre-selecting the location of airline hub airport(s), assessing the resilience of planned airline schedules and the prospective consequences, and designing mitigating measures before, during, and in the aftermath of a disruptive event. As such, it could, with slight modifications, be applied to transport networks operated by other transport modes. |
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Keywords: | Air transport network Resilience Friability Large-scale disruptive event Costs Modelling |
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