C2.5 DEMANDS AND TRENDS

C2.5.1 Annual to Peak Period Demand

For the purpose of facilities planning it is essential to know the likely requirements on an hour-by- hour basis. Annual or even weekly forecast figures can be almost meaningless in this respect. The relationship of annual traffic to peak period will depend on seasonal variations and passenger characteristics. This relationship is projected separately for domestic and international traffic and within each category for each route area.

C2.5.2 Seasonal Trends

Seasonal variation affects the relationship of peak month to annual traffic. Common influencing factors in this regard include:

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Effect of economic growth on business or holiday market sectors (leisure traffic usually creates peaks at certain periods of the year different from the peak created by business traffic).

•

Whether airlines increase capacity during peak periods.

C2.5.3 Special Events

Peaks associated with special occurrences such as national holidays, religious festivals, and sporting events should be excluded from forecasts. Plan to accommodate this above planning peak demand at a lower level of service, by means of contingency plans, schedule coordination and other sound demand/capacity management practices.

C2.5.4 Assessment Methods

Having established the magnitude and frequency of the forecasted data, it will be necessary to assess it using proven assessment rules which will be used for the sizing of airport facilities. One approach is to use a proportion (85th percentile) of the forecast profile as the basis to plan airport infrastructure. Another approach is to select frequently occurring peak days or busy hour periods which are chosen as the basis on which to plan airport facilities. These approaches can be summarised as follows:

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85th percentile.

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40th busy hour or day of the year (see CDG example of this method in Table C2-2 below).

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30th busy hour or day of the year.

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The second busiest day in an average week during the peak month — an average weekly pattern of traffic is then calculated for that month.

It is important that one the above techniques is used as it is inappropriate to plan the design of airport infrastructure on the occurrence of either an isolated peak day forecast or an isolated peak hour rate.

Busy Day Schedule: Determining airport capacity largely depends on predicting the impact of projected airline schedules on the various airport facilities. Capacity and level of service are based on operating conditions and rules, but also upon the particular demand profiles created by the mix of flights and flight sector for a typical busy day. The amalgamated airline schedules for a typical busy day reflects the airlines strategy for an airport and how an airport is connected to the world.

The production of a single day forecast requires a detailed assessment of all the operational parameters that underlie airline schedules: the operational suitability of aircraft types for given route structures; reasonable aircraft roistering compatible with a high level of aircraft utilisation; and use of commercially feasible arrival and departure timings throughout a route structure. This assessment is then incorporated to form the amalgamated airline forecast schedule.

Selection of a 'Busy' Day: A typical 'busy' day is the second busiest day in an average week during the peak month. An average weekly pattern of passenger traffic is calculated for that month, and

peaks associated with special events such as religious festivals, trade fairs, conventions and sport events are excluded. This single day analysis should assess:

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Operational suitability of an aircraft type for a given route structure.

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Aircraft rotations compatible with a high level of utilisation.

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Use of commercially feasible arrival and departure timings throughout the route structure.

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Airport curfews and other limitations.

The 'busy day' data for the base year is 'actual' and should come from the airport control tower (ATC) log. It should cover each aircraft movement during the 'busy' day with indication of the following attributes:

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_{Airline Name.}

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_{Flight Number.}

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_{Aircraft Type.}

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_{Aircraft Registration.}

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_{Seating Capacity.}

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_{Origin Of Flight.}

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_{Arrival Time.}

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_{Terminal Used.}

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_{Passengers Disembarked.}

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_{Direct Transit Passengers (If Applicable).}

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_{Departure Time.}

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_{Destination Of Flight.}

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_{Embarking Passengers.}

The busy day should be more than just a single witnessed statistical hour or a day within an operational

calendar. The busy day should be representative of a frequently occurring 'model' busy period, representative of a realistic day within a weekly schedule.

Table C2-2: CDG Peak Passenger Traffic Analysis

CDG Airport Passenger Traffic Analysis

Punngin 2000 1999 1998 1897 199t 1995 1994 TTL

Par Year 48,246,137 43.597,194 38,628,916 35,327,039 31.724,035 28,356.470 28,880,214 254,559,006

Per Peak Month 4,887,000

4.258

,00

3,877,000 3,487,000 3.057.000 2,798.000 2,778.807 24,940,807

0.

Peek Month to Year

0

.10

0

.10

0.10 0.10 0.10 0.10 0.10 0.10

Per Peek Day* 179,519 168,248 151,461 137,809 128.951 114,283 108274 988,545

.04;

Peak Day to Peak Month 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04

Per Peak Hour 16.791 16,474 12.927 12,699 12.085 8,915 9,148 89,039

M

Peak Hour to Peak Day 0.09 0.10 0.09 0.09 0.09 0.08 0.08 0.09

Per 40th Peak Hour 14,599 13,492 10,980 10,697 10,146 7,760 7,874 75,548

.08 0.08 0.08 0.07 0.08 0,08 0.07 0.07 0.08

101

Table C2-3: Estimate of Peak Passenger Traffic Based on MPPA Forecast

Passengers/Year 1,000,000 2,500,000 5,000,000 10,000,000 12,500,000 15,000,000 Passengers/Peak Month 100,000 250,000 500,000 1,000,000 1,250,000 1,500,000 Passengers/Peak Day 4,000 10,000 20,000 40,000 50,000 60,000 Passengsrs/Peak Hour 3S0 900 1,800 3,600 4,500 5,400 Passengers/Year 20,000,000 25,000,000 30,000,000 35,000,000 40,000,000 50,000,000 Passengers/Peak Month 2,000,000 2,500,000 3,000,000 3,500,000 4,000,000 5,000,000 Passengers/Peak Day 80,000 100,000 120,000 140,000 160,000 200,000 Passengers/Peak Hour 7,200 9,000 10,800 12,600 14,400 18,000 C2.6 FORECASTING METHODOLOGY C2.6.1 Study Objectives

The objectives of the forecast study should be clearly identified prior to the collation of data. Informed decisions should be made and forecasters should be focused on having the correct representative statistics rather than a convenient series of numbers which perhaps do not convey the true behavioural patterns of the airport and its traffic in the foreseeable future. Forecasters should aim to satisfy the following high level study objectives:

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There should be three sets of statistics provided by the airport facility forecaster, which should represent the low, medium and high magnitude data obtained and assessed. The forecaster must specify which influencing factors have the largest level of uncertainty in regard to their future evolution, in order to justify having both low and high projections.

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Operational and business assumptions should be clarified in every regard on forecasted information with qualifications as regard their impact on the forecasted data.

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Data should be auditable whereby the forecaster should be able to trace the history of the manipulation of data and to confirm the logic for the decisions made in every regard.

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Consultation groups should be identified along with their terms of reference. All of which should be clarified in the record and the presented data produced by forecasters.

C2.6.2 Data Availability

There are three main credible sources of data for forecasters to access. This includes but it is not exclusively limited to:

1. Historical Site Data

Historical Site data may originate from various sources within the airport organisation and or the airlines. Care should be observed with historical data because as the name suggests it is based on past trends and may not be representative of how the existing airport or airline may function based on a changing fleet or changes in business processes. Historical data is useful in the assessment of process times and historical processing trends.

2. IATA World Wide Survey

This data is sourced by IATA following extensive world wide surveys of key airline and airport infrastructures/organisations (see clause C2.6.3 Method 2 for further details).

3. User Forecasted New Data

This data is created by the airline or airport from first principles and may reflect a combination of historical data and new operational objectives on the use of newer aircraft or new airport processes.

C2.6.3 Methods Of Forecasting Passenger Traffic And Aircraft Movements

A combination of several methods forms the core of the traffic forecasting approach, these are defined as follows:

In document IATA - Airport Development Reference Manual - JAN 2004 (Page 100-103)