The airport Network and Catchment area Competition Model - A comprehensive airport demand forecasting system using a partially observed database
AbstractFor airport capacity planning long term forecasts of aircraft movements are required. The classical approach to generate such forecasts has been the use of time series data together with econometric models, to extrapolate observed patterns of growth into the future. More recently, the dramatically increased competition between airports, airlines and alliances on the one hand, and serious capacity problems on the other, have made this approach no longer adequate. Airport demand forecasts now need to focus heavily on the many competitive elements in addition to the growth element. In our paper we describe a comprehensive, pragmatic air demand model system that has been implemented for Amsterdam’s Schiphol Airport. This model, called the Airport Network and Catchment area Competition Model (ACCM), provides forecasts of future air passenger volumes and aircraft movements explicitly taking account of choices of air passengers among competing airports in Europe. The model uses a straightforward nested logit structure to represent choices of air passengers among alternative departure airports, transport modes to the airport, airlines/alliances/low cost carriers, types of flight (direct versus transfer), air routes, and main modes of transport (for those distances where car and high-speed train may be an alternative option). Target year passenger forecasts are obtained by taking observed base year passenger numbers, and applying two factors to these: (1)Firstly a growth factor, to express the global impact of key drivers of passenger demand growth such as population size, income, trade volume; (2)Secondly a market share ratio factor, to express the increase (or decline) in attractiveness of the airport due to anticipated changes in its air network and landside-accessibility, relative to other (competing) airports. The target year passenger forecasts are then converted into aircraft movements to assess whether or not the available runway capacity is adequate. Key inputs to the model are data bases describing for base year and target year the level of service (travel times, costs, service frequencies) of the land-side accessibility of all departure airports considered, and the air-side networks of all departure and hub airports considered. The air-side networks (supply) are derived from a detailed OAG based flight simulation model developed elsewhere. A particular characteristic of the ACCM implementation for Schiphol Airport is that it had to be developed using only a partial data set describing existing demand: although detailed OD- information was available for air passengers using Schiphol Airport in 2003, no such data was available for other airports or other transport modes. As a consequence a synthetic modelling approach was adopted, where the unobserved passenger segments for the base year were synthesised using market shares ratios between unobserved and observed segments forecasts for the base year together with the observed base year passenger volumes. This process is elegant and appealing in principle, but is not without a number of problems when applied in a real case. In the paper we will first set out the objectives of the ACCM as it was developed, and the operational and practical constraints that were imposed. Then we will describe how the ACCM fits with model developments in the literature, and sketch the overall structure that was adopted. The following sections will describe the modelled alternatives and the utility structures, the level-of-service data bases used for land-side and air-side networks, for base year and target year. Then we will describe in some detail how we dealt with the partial data issue: the procedure to generate non-observed base year data, the validation, the problems encountered, the solutions chosen. Finally we shall show a number of the results obtained (subject to permission by the Dutch Ministry of Transport), and provide some conclusions and recommendations for further application of the methodology.
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Bibliographic InfoPaper provided by European Regional Science Association in its series ERSA conference papers with number ersa05p521.
Date of creation: Aug 2005
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-06-03 (All new papers)
- NEP-DCM-2006-06-03 (Discrete Choice Models)
- NEP-FOR-2006-06-03 (Forecasting)
- NEP-NET-2006-06-03 (Network Economics)
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