Building an assessment model for measuring airport resilience

Abstract

Airports are some of the most important facilities in any country's transportation system. It is important to protect such critical infrastructure from natural and manmade risks. However, it is difficult to build a risk prediction model based only on past statistical data. An experienced expert-based Multiple Criteria Decision-Making (MCDM) approach could be used to measure an airport's resilience and develop feasible protection strategies. This study proposes a novel assessment model for evaluating airport resilience. The model uses the Bayesian Best Worst Method (Bayesian BWM) to determine the optimal group weights of the criteria, and the modified Preference Ranking Organization Method for Enrichment Evaluations (modified PROMETHEE) technique to calculate the gap between each alternative and the aspiration level. The practicality and effectiveness of the model are demonstrated using Taiwan's airports as an example. In addition, sensitivity analysis and model comparisons are conducted to confirm the reliability of the proposed model. The results show that the proposed assessment system can effectively assist policy makers and airport security departments to formulate improvement strategies, thereby enhancing the resilience of this infrastructure.