19 The assessment of the market share of the port of Antwerp is then needed before coming up with its container traffic forecast measured in Twenty Feet Equivalent Units (TEU), the standard measurement unit for container traffic. For the 1996 Master plan, the working assumption was that the market share of the port of Antwerp would remain the same at 18.4%. This results in a forecast of traffic increase from 25.8 million in 1995 to 33.9 in 2000 and 41.9 in 2005.23 To most purist, this is a very subjective approach to forecasting. But it is worth recognizing that this is how it works in many places. Most port projects which these authors have had to review followed the line described here. It is obvious that the analytical part of the project is weak. The quantitative methods used did not work but it is very likely that they could be improved with recent development in the analysis of time series. In the short run however, the proof is in the success of the forecast. The forecast reported here were made in 1996. They resulted in an underestimation since actual total container traffic had reached 35.4 million tons at the end of 1998 already.

# _5.4._ **_The_** _forecasting of Traffic in Spain's Airports_ **_24_**

In Spain, the forecasting of air traffic is done by a public entity, Aeropuertos Espafioles y Navegaci6n Aerea (AENA). The process is itself revealing and involves many of the approaches discussed earlier. Demand is forecasted quite technically at the airport level but the aggregate demand is the result of meetings of all the airport managers who decide collectively on the future investment needs and allocation. In general, the demand forecast distinguish between short run, medium run and peak traffic. Peak traffic forecast is needed to know when and what to do with prices in the short run to adjust demand to capacity. These estimates are compared with estimates made by users of the airport services to test for their robusteness.

Short run forecast is based on time-series techniques-moving averages, exponential smoothing and ARIMA models. They are designed to generate forecasts learning from past forecasting mistakes. They generate monthly estimates for a 12 months period for national and international traffic for all airports. Medium and long run forecasts are based on econometric models which reflect the complex interactions between mutliple variables. These are used to make forecast to 5, 10 and 15 years horizons. Figure 2 shows how AENA proceeds to come up with these estimates. This is done every year during the first quarter of the year.

Figure 2 shows that the forecast is based on past traffic trends but also on regional, national and international determinants of business cycles but also on the tourist traffic, the possibilities of substitutes offered by other transport modes and airport specific characteristics including the number of airlines present in each airport, their capacity, the size of the planes reaching each airport and their degree of occupancy. It also shows that the specific forecasting process is actually done in two stages.

> 23 This is equivalent to about 3.1 million containers measured in TEUs (divide the volume expressed in 1000 tons by the average tonnage of TEUS (twenty feet equivalent unit) that were loanded and unloaded) 24 This section is based on Trujillo and Jacob (1999)