(c) Current The analysis of the The more It be difficult to project meso-model factors and precise the results from the current with future temporal evolution is scenarios to the future influence done in two stages: is frameworks. This is diagram only used particularly important when A detailed for current there are changes in supply represents operations and demand. Results might current operations since it is become more qualitative as (supply and demand) easier to the forecast used becomes of the system. The model the less accurate. effects of the factors effect of are modelled here, Some of the factors that the factors generating an analysis affect operations might be for that of their impacts on easier to model in the timeframe the metrics. current timeframe but The factors that affect supply and From the output of calibration demand will still need to be the a of the modelled in the meso-model parameters’ model is to adapt the flows of the relationship model is easier as it flights and passengers in produced. This is done can be order to understand the by modelling the done impact of their evolution on relationship of the based on the metrics. outputs of the model, historical or as a direct might be a high number data. description of the of assumptions in terms of output of previous the influence diagram model. simulations. On this It might be difficult to model, the effect of establish the impact of more the factors is than one factor at the same projected to the time in the model. future timeframes (2035, 2050) based on The validation of the a higher-level influence diagram model influence diagram might be complex. model. (a) Agent-based micro-model In this possibility, a microscopic model is developed. In this solution, each individual stakeholder instantiation is an agent (each airline, each airport, etc.), and makes decisions within a single simulation. Each agent updates its belief periodically based on the state of the overall simulation, including the expected state of other agents.
(c) Current meso-model with future influence diagram The analysis of the factors and temporal evolution is done in two stages: A detailed represents current operations (supply and demand) of the system. The effects of the factors are modelled here, generating an analysis of their impacts on the metrics. From the output of the a parameters’ relationship model is produced. This is done by modelling the relationship of the outputs of the model, or as a direct description of the output of previous simulations. On this model, the effect of the factors is projected to the future timeframes (2035, 2050) based on a higher-level influence diagram model. The more precise is only used for current operations since it is easier to model the effect of the factors for that timeframe The calibration of the model is easier as it can be done based on historical data. It be difficult to project the results from the current scenarios to the future frameworks. This is particularly important when there are changes in supply and demand. Results might become more qualitative as the forecast used becomes less accurate. Some of the factors that affect operations might be easier to model in the current timeframe but factors that affect supply and demand will still need to be modelled in the meso-model to adapt the flows of the flights and passengers in order to understand the impact of their evolution on the metrics. might be a high number of assumptions in terms of the influence diagram model. It might be difficult to establish the impact of more than one factor at the same time in the model. The validation of the influence diagram model might be complex. (a) Agent-based micro-model In this possibility, a microscopic model is developed. In this solution, each individual stakeholder instantiation is an agent (each airline, each airport, etc.), and makes decisions within a single simulation. Each agent updates its belief periodically based on the state of the overall simulation, including the expected state of other agents.