Page 187 - Planning and Design of Airports

154 Airp o r t Pl anning

successful in many instances principally due to the large number of
factors which may be considered in such a process. Though there is
often a lack of mathematical sophistication in the process, the knowl-
edge and consideration of the many diverse factors influencing avia-
tion forecasts usually improves the results. The disadvantages of this
forecasting technique include the absence of statistical measures on
which to base the results and the inability, except in the most obvious
cases, to gain a significant consensus relative to the expected perfor-
mance of the explanatory factors in the future.

Time Series Method
Time series analysis or extrapolation is based upon an examination of
the historical pattern of activity and assumes that those factors which
determine the variation of traffic in the past will continue to exhibit
similar relationships in the future. This technique utilizes times series
type data and seeks to analyze the growth and growth rates associ-
ated with a particular aviation activity. In practice, trends appear to
develop in situations in which the growth rate of a variable is stable
in either absolute or percentage terms, there is a gradual increase or
decrease in growth rate, or there is a clear indication of market satura-
tion trend over time [11]. Statistical techniques are used to assist in
defining the reliability and the expected range in the extrapolated
trend. The analysis of the pattern of demand generally requires that
upper and lower bounds be placed upon the forecast and statistics
are used to define the confidence levels within which specific projec-
tions may be expected to be valid. From the variation in the trends
and the upper and lower bounds placed on the forecast a preferred
forecast is usually developed. Quite often smoothing techniques are
incorporated into the forecast to eliminate short run, or seasonal, fluc-
tuations in a pattern of activity which otherwise demonstrates a trend
or cyclical pattern in the long run [16].
An illustration of the application of a trend line analysis to forecast
annual enplanements at an airport is shown in Example Problem 5-1.

Example Problem 5-1 The historical data shown in Table 5-1 have been collected
for the annual passenger enplanements in a region and one of the commercial
service airports in this region. It is necessary to prepare a forecast of the annual
passenger enplanements at the study airport in the design years 2010 and 2015
using a trend line analysis.
In applying the trend line analysis to these data, a forecast technique of the
annual enplanements at the study airport will be made by forecasting the his-
torical trend to the design years. A plot of the trend in the annual passengers
enplaned at the study airport is given in Fig. 5-1.
By extrapolating the trend into the future, an estimate of the annual enplaned
passengers at the study airport in 2010 is found to be 2,100,000 passengers and
in the year 2015 is found to be 2,900,000 passengers.