GENERAL DIRECTORATE OF

STATE AIRPORTS AUTHORITY

TURKEY

TABLE OF CONTENTS

STATISTICS

DHMI MAIN FUNCTION AND SERVICES

HUGE PROJECT

DHMI MAJOR PROJECTS

DHMI MAIN FUNCTION

AND SERVICES

AIRPORTS OPEN TO CIVIL AIR TRAFFIC IN TURKEY

OPEN TO CIVIL AIR TRAFFIC

UNDER CONSTRUCTION

PLANNED

Passenger (Thousand) 2003

1. UNITED STATES 1.268.895

2. UNITED KINGDOM 203.608

3.JAPAN 175.854

4.SPAIN 153.394

5.GERMANY 146.673

6.FRANCE 119.728

7.ITALY 100.364

8.CHINA 99.546

9.BRAZIL 76.640

10.CANADA 76.509

11.AUSTRALIA 74.722

12.SOUTH KOREA 64.937

13.MEXICO 61.522

14.NETHERLANDS 41.389

15.INDIA 40.105

16.INDONESIA 39.919

17.TAILAND 39.794

18.TURKEY 34.444

19.MALAYSIA 34.071

20.RUSSIAN FEDERATION 33.086

Passenger (Thousand) 2016

1. UNITED STATES 1.649.105

2.CHINA 1.019.737

3.JAPAN 295.621

4.UNITED KINGDOM 270.566

5.INDIA 255.193

6.SPAIN 229.635

7.GERMANY 225.044

8.BRAZIL 199.525

9. INDONESIA188.839

10.FRANCE 174.993

11.TURKEY 174.153

12.ITALY 165.009

13.RUSSIAN FEDERATION 160.724

14.AUSTRALIA 153.843

15.CANADA 140.360

16. TAILAND 138.732

17. SOUTH KOREA136.436

18.MEXICO 127.324

19.UNITED ARAB EMIRATES 120.132

20.MALAYSIA 91.390

RANKINGS BY PASSENGER TRAFFICS

TURKEY RANKINGS BY

EUROPE & WORLD

Years Europe World

TOTAL

PASSENGER

(MILLION)

2019 - - 223.433

2018 - - 205.402

2017 4 9 193.410

2016 5 11 174.153

2015 3 8 181.075

2014 3 9 166.181

2013 5 11 149.996

2012 6 12 131.030

2011 6 12 118.292

…

2005 6 16 56.119

2004 6 16 45.057

2003 7 18 34.443

Source: (2017) The Undisclosed Data Set Out By The ACI

Source:ACI

DHMI’s MAJOR PROJECTS

ISTANBUL NEW AIRPORT

ISTANBUL NEW AIRPORT

ON GOING, COMPLETED AND UNDER CONSTRUCTION

PHASE BOT PROJECTS

ESENBOGA AIRPORT NEW

INTERNATIONAL/DOMESTIC

TERMINAL AND SUPPLEMENTARY

BUILDINGS PROJECT

ZAFER AIRPORT

ANTALYA AIRPORT I. & II. PHASE

INTERNATIONAL LINES TERMINALS,

CIP BUILDING, DOMESTIC LINES

TERMINAL AND

SUPPLEMENTARY BUILDINGS

AIR TRANSPORT GENERAL ETUDE (HUGE)

HUGE Project

• Main Steps of HUGE Project

• Step1: Clustering

• Step2: Short Term Forecast (3 Years)

• Step3: Medium Term Forecast (10 Years)

• Step4: Long Term Forecast (20 Years)

• Step5: System Planning HUGE

CLUSTERING

Prior to conducting the survey, sampling studies were conducted to determine the number of

airports to be surveyed and the number of the surveys. Clustering analysis was used in the

sampling study. Factors considered in the cluster analysis:

• Number of Passenger

• Connectivity (By Domestic and International)

• Accessibility

QUESTIONNARIES

As a result of the analyses, the number of the

questionnaires were made at some airports:

Some Examples of Questionnaries Results at

Istanbul Atatürk Airport

FORECASTS

• Short –Term Forecast (3 Years)• Medium- Term Foracast (10 Years)• Long-Term Forecast (20 Years)

Forecasts and presentation files areprepared by R program.

Aircraft Movements

RESULTS OF

SHORT TERM FORECASTS (TURKEY )

METHOD

• Econometric methods have been used for medium-term estimates.

Model : ytdij = β0 + β1x1,tdij + β2x2,tdij + … + β4t + β5Dij + εtdij

• In the outline estimates, data were used at the country level (on landing-departure and summer-winter seperation).

Sample Model ( PAX for Domestic Commercial Flights)

MEDIUM TERM FORECASTS

Forecasts were made for three scenarios:

Scenario One (Base): IMF data were used to estimate the GDP of the countries, and GDP growth rates per capita. IMF foresees the 2017-2022 period will grow by an annual average of 4.2% for Turkey.

Scenario Two (Low): It is predicted that all countries in the world will grow 1.5 percentage points below IMF estimates. In this scenario, the outcome of adverse external shocks (increase in fuel prices, developments that adversely affect tourism, etc.) was assumed to be 2 percentage points lower for domestic and international routes than for the period 2011-2016.

Scenario Three (High): It is assumed that all countries in the world will grow 1.5 percentage point above the IMF estimates. It is also assumed that the outcome of positive external shocks (the real downward trend in fuel prices, positive political environment, etc.) will be 2 percentage point increase in the trend growth rate both in domestic and international lines compared to 2011-2016 period.

RESULTS OF FORECASTS FOR 10 YEARS

DATA SOURCES

• List of Airports in the World (+50,000 airport / heliport). Country, city, location information about airports. Source: http://ourairports.com/data/ (Last updated 14 November 2017)

• Country codes and classifications. Source: World Bank, World Development Indicators

• Turkey real GDP and current account data (2011-2016). The GDP deflator was calculated from current and real GDP. Source: TURKSTAT

• Turkey airports data. Source: DHMİ

• High speed rail connections. Source: MATPUM

• Turkey 2004-2014 GDP per capita at the provincial level (at current prices). Turkey has been calculated by increasing the level of data 2015-2016 average. Source: TURKSTAT

• Turkey's population by province (2007-2016 data). Source: TURKSTAT

• Coordinates of cities in Turkey. The province coordinates were used as the arithmetic average of the district centers coordinates.

• IMF country-based population and per capita GDP estimates (growth rates, 2016-2022 data). The average of the last three years was used for 2023-2026. Source: IMF, World Economic Outlook Database, October 2017

• Total population, adult population, population in the largest city, GDP per capita Source: World Bank, World Development Indicators (R WDI package)

Domestic Passenger

Scenario 1 Scenario 2 Scenario 3

Domestic Passenger

Domestic Flights

International Passenger

International Passenger

TOP 20 FOR COMMERCIAL FLIGHTS AND PASSENGER NUMBERS

LONG TERM FORECASTS AND SCENARIOSLONG TERM FORECASTS AND SCENARIOSLONG TERM FORECASTS AND SCENARIOSLONG TERM FORECASTS AND SCENARIOS

LONG TERM FORECASTSLONG TERM FORECASTSLONG TERM FORECASTSLONG TERM FORECASTS

The long-term forecasting study was conducted in 5 main steps:

Step 1 : Determination of Priorities

Step 2 : Determination of External Trends

Step 3: Creating a Matrix of Significant Variables

Step 4: Creating Scenarios

Step 5: Choosing the Best Scenario

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