As part of the financial feasibility study for the terminal extension at Hartford's Bradley International Airport (BDL), The Louis Berger Group (Berger) carried out a detailed forecast of passenger and cargo activity for the Connecticut Department of Transportation. The forecasting exercise included a classic "top-down" econometric modeling approach, where passenger and cargo volumes are derived from forecasts of regional economic activity and income. This approach was further extended to include the construction of separate forecasting models for the various segments of demand: air carrier, commuter and charter passengers, and freight and mail cargo. As these different market segments tend to exhibit distinct patterns over time, creating these separate models significantly improves the statistical precision of the forecasts.

The forecasts for passengers and cargo were used as a basis for calculating the expected revenues for financial feasibility analysis. The variability of this "base case" scenario was rigorously assessed with a risk evaluation procedure. The process involved defining estimates of variability for all the relevant factors that could be expected to play a large role in the financial outcome of the terminal extension. On the "demand" side, this included all the various factors influencing passenger and cargo volumes, such as regional economic performance and trade, deviation to alternative modes, and consumer behavior itself. On the "supply" side, this included the trends in the price of passenger travel and cargo shipping, the expansion of existing markets, or even the probability of a low-cost carrier entering the market at BDL. Supplementing these factors was an analysis of the expected variability of estimated construction and capital maintenance costs. These various estimates were then incorporated into a statistical model that was used to evaluate the effects of all possible outcomes on financial profitability.

The measures of variability used in the risk analysis were defined in terms of probability distributions, rather than simply high and low endpoints. In order to define meaningful estimates of these distributions, analysis of existing relevant data was carried out, supplemented by the extensive input of experts in commercial aviation, airport management, and construction industries was generated during a series of workshops and interviews.

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