Introduction to D3 Sample Clauses

Introduction to D3. 2 In line with the FlightPath 2050 goal of enabling 90 per cent of European travellers to complete their door-to-door journey within four hours (4hD2D), this report will set its focus on delivering insights into the future European air transport system and future passengers. To explore how the passenger of the future might look like, a first step is to examine the factors that drive air transport demand. As these demand drivers might change in the future, a better understanding of the resulting effects on demand is crucial to see how passenger profiles might change as well. Demand is driven by various external and internal factors. External factors refer to economic and demographic conditions and internal factors encapsulate drivers such as quality of service and ticket price (Xxxxxxx et al., 2010). Many have already been described and examined in D3.1 as well as within other studies, like Xxxxxxxx and Postorino (2006), Xxxxx and Xxxxxxxxxx (2011), Xxxxxx and Xxxxx (2001), Xxxxxx (2012) and Xxxxxx (2000). Different research methods such as econometric models and qualitative expert questionnaires have been used. Present studies tend to be a good basis to explain the drivers of passenger air transport demand, however, many of these only take a small number of explorative variables into account or solely set focus on specific developments (e.g. fuel price development or population growth). Within this report, we will take a more holistic view to predict passenger (pax) demand and to describe future passenger profiles. In order to do so, the report is structured the following way. In section 2 the main drivers for forecasting air transport passenger demand will be identified and presented. On the on hand, these drivers are the results of a high-level factor identification (section 2.2). On the other hand, more detailed future developments of demographical, geographical, socio-economic and behavioural aspects characterising passengers as well as mobility aspects will be examined (sections 2.3 to 2.8). In Part II of the report, the findings of Part I are applied using the multi-method modelling tool AnyLogic. Here, a simulation will be conducted to predict future passenger demand in 2035 and 2050 based on publicly available data (section 3). Finally, implications for future air transport passenger profiles will be derived, complemented by the modelling of respective passenger trips (section 4). The whole research process is depicted in Figure 1.
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