nepal air traffic demand

Capacity Development of Civil Aviation Authority of Nepal

AIR TRAFFIC DEMAND FORECAST DRAFT

SEPTEMBER 2012

Page Intentionally blank

____________________________________________________________________________________________________ 2A National Plan Civil Aviation Development Page 3 of 147 Air Traffic Demand Forecast Draft

Contents

1 INTRODUCTION ........................................................................................................................................ 5

2 SCOPE ....................................................................................................................................................... 5

3 NEPAL HISTORICAL AIR TRANSPORT BACKGROUND ...................................................................... 6

3.1 AIR TRAFFIC DEMAND HISTORICAL EVOLUTION ............................................................................. 73.2 AIR TRAFFIC SEASONAL BEHAVIOUR .............................................................................................. 103.3 AIR TRAFFIC TIPOLOGY ...................................................................................................................... 14

3.3.1 International ............................................................................................................................... 143.3.2 Domestic .................................................................................................................................... 19

3.4 OPERATING AIR TRANSPORT COMPANIES ................................................................................................ 213.5 AIR TRANSPORT INFRASTRUCTURES IN NEPAL .......................................................................................... 23

4 DATA BACKGROUND ............................................................................................................................ 28

5 METHODOLOGY ..................................................................................................................................... 30

6 IBM SOFTWARE SPSS: PREDICTIVE ANALYTICS SOFTWARE AND SOLUTIONS ........................ 31

7 REFERENCES ......................................................................................................................................... 31

8 NOMENCLATURE ................................................................................................................................... 31

9 DOMESTIC TRIBHUVAN SHORT TERM FORECASTING .................................................................... 32

9.1 AVAILABLE DATA .................................................................................................................................. 329.2 DOM. SHORT TERM FORECASTING HYPHOTESIS AND USED MODEL ...................................... 329.3 STD RESULTS ....................................................................................................................................... 38

9.3.1 Passengers ................................................................................................................................ 389.3.2 Aircraft Movements .................................................................................................................... 409.3.3 Cargo ......................................................................................................................................... 42

10 INTERNATIONAL TRIBHUVAN SHORT TERM FORECASTING ...................................................... 44

10.1 AVAILABLE DATA .............................................................................................................................. 4410.2 INT. SHORT TERM FORECASTING HYPHOTESIS AND USED MODEL ..................................... 4410.3 STI RESULTS ..................................................................................................................................... 45

10.3.1 Passengers ................................................................................................................................ 4510.3.2 Aircraft Movements .................................................................................................................... 4710.3.3 Cargo ......................................................................................................................................... 49

11 INTERNATIONAL TRIBHUVAN LONG TERM FORECASTING ........................................................ 51

11.1 AVAILABLE DATA .............................................................................................................................. 5111.2 INT. LONG TERM FORECASTING HYPHOTESIS AND USED MODEL ....................................... 5111.3 LTI RESULTS ...................................................................................................................................... 52

11.3.1 PESSIMISTIC ............................................................................................................................ 5411.3.2 OPTIMISTIC .............................................................................................................................. 6211.3.3 PROBABLE ............................................................................................................................... 68

12 DOMESTIC TRIBHUVAN LONG TERM FORECASTING .................................................................. 74

12.1 AVAILABLE DATA .............................................................................................................................. 7412.2 DOM. LONG TERM FORECASTING HYPHOTESIS AND USED MODEL .................................... 7412.3 LTD RESULTS .................................................................................................................................... 76

12.3.1 PESSIMISTIC ............................................................................................................................ 7712.3.2 OPTIMISTIC .............................................................................................................................. 85


12.3.3 PROBABLE ............................................................................................................................... 91

13 REGIONAL AIRPORT AIR TRAFFIC DEMAND FORECASTING ..................................................... 97

13.1 AVAILABLE DATA .............................................................................................................................. 9713.2 REGIONAL AIRPORT AIR TRAFFIC DEMAND HYPHOTESIS AND USED MODEL ................. 105

13.2.1 Passengers .............................................................................................................................. 11513.2.2 Aircraft Movements .................................................................................................................. 11713.2.3 Cargo ....................................................................................................................................... 119

14 TRIBHUVAN PEAK HOUR FORECASTING .................................................................................... 121

14.1 AVAILABLE DATA ............................................................................................................................ 12114.2 TRIBHUVAN PEAK HOUR HYPHOTESIS AND USED MODEL ................................................... 121

14.2.1 TRIBHUVAN PEAK DAY RESULTS ....................................................................................... 12314.2.2 TRIBHUVAN PEAK HOUR RESULTS .................................................................................... 12914.2.3 TRIBHUVAN DESIGN DAY ..................................................................................................... 13514.2.4 TRIBHUVAN DESIGN DAY PEAK HOUR .............................................................................. 136

15 NEPAL INFRASTRUCTURE IMPROVEMENTS EFFECTS IN AIR TRAFFIC FORECAST. ........... 137

16 ANNEX 1: SPSS MAIN RESULTS EXTRACTS ................................................................................ 138

16.1 STD .................................................................................................................................................... 13816.2 STI ..................................................................................................................................................... 13916.3 LTI ...................................................................................................................................................... 14016.4 LTD .................................................................................................................................................... 146


1 INTRODUCTION As introduction to this document, following, an ICAO 9184 Airport Planning Documents extract, Chapter 3 Forecasting for Planning, 3.2.1: Forecasting is the heart of planning and control processes. Forecasts are necessary to define the facilities that will be required, the scale of such facilities, and the time at which they will be required.

The objective of forecasting is not to predict the future with precision, but to provide information that can be used to evaluate effects of uncertainty about the future. Thus, both for physical planning and for financial assessment purposes, consideration should be given not only to the implications of the forecasts themselves but also to the implications of lack of precision in the forecasts and in the conversion of the forecasts into planning criteria.

So, this document pretends to be a starting point document for the future Nepal Air Transport Infrastructures and Management Planning.

2 SCOPE The present document studies the future air traffic demand in Nepal. As required at: TASK (14): Review and validate the air traffic forecast for Tribhuvan International Airport created under TA 7031 NEP. Develop this forecast further to account for traffic anticipated to use the domestic airports during the timeframe of the NCADP. A previous short review of the TA 7031 NEP document has been done, but regarding to its conclusions sources and methods are not described in detail, it is more appropriate a new air traffic forecast departing from actual scenario. Following, a TIA complete air traffic demand analysis have been done, as well as the following regional 14 airports air traffic study:


STUDIED 14 REGIONAL AIRPORTS

ICAO IATA NEPAL NAME CITY

1 VNNG KEP NG Nepalgunj Airport Nepalgunj 2 VNVT BIR BR/VT Biratnagar Airport Biratnagar 3 VNPK PKR PK Pokhara Airport Pokhara 4 VNLK LUA LK Lukla Airport Lukla 5 VNSK SKH SK Surkhet Airport Surkhet 6 VNDH DHI DH Dhangarhi Airport Dhangarhi 7 VNSI SIF SI Simara Airport Simara 8 VNBW BWA BW Bhairahawa Airport Bhairahawa 9 VNJP JKR JP Janakpur Airport Janakpur

10 VNBP BHR BP Bharatpur Airport Bharatpur

11 VNCG BDP BDP Bhadrapur Airport (chandragadhi

Airport) Bhadrapur / Chandragadhi 12 VNJL JUM JUM Jumla Airport Jumla 13 VNJS JMO JMO Jomsom Airport Jomsom 14 VNST IMK IMK Simikot Airport Simikot

Regarding to domestic airports, the information and data compilation has been hard to complete, so consultant team travelled to these airports, Biratnagar, Nepalgunj, Pokhara, Surkhet, in order to achieve more complete and accurate data. Finally, Ermc-Ineco-Prointec consultant considers that a peak day and peak hour analysis is an interesting and essential study for future airport plans, so a complete analysis has been added in addition. 3 NEPAL HISTORICAL AIR TRANSPORT BACKGROUND

Aviation sector in Nepal has shown a tremendous growth in air travel demand and in the number of flights after the country adopted a liberal sky policy since 1992. The upsurge in Nepalese immigrant traffic to Malaysia and the Gulf region especially from 2003 also contributed to the high growth in air travel demand. International air traffic has crossed 2,7 million passengers in 2011, and the recent growth trend has been most impressive. This mentioned growth has been occurred both in international and domestic traffic, as reflected in the following graphics. But international traffic shows more


regular growing, instead the domestic passenger evolution has more variation, depending on local circumstances. Following data have been extracted from several archives and paper documents provided by CAAN authority and other airport staff. During this study, some incoherence and uncertainty have been detected depending on different information sources. This study has synthesized all received documents according to a logical criteria, in order to complete a good departing data for an as accurate as possible air traffic demand forecast. These departing data is the showed following, in tables and graphics, resumed in this document and completed in attached annexes. 3.1 AIR TRAFFIC DEMAND HISTORICAL EVOLUTION

Source: CAAN

0

500000

1000000

1500000

2000000

2500000

3000000

1975 1980 1985 1990 1995 2000 2005 2010 2015

Pax

Year

INTERNATIONAL PAX EVOLUTION

DEPARTURES

ARRIVALS

TOTAL


Source: CAAN

Source: CAAN

Growth rates:

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

1800000

1975 1980 1985 1990 1995 2000 2005 2010 2015

Pax

Year

DOMESTIC PAX EVOLUTION

DEPARTURES

ARRIVALS

TOTAL

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

4000000

4500000

5000000

1975 1980 1985 1990 1995 2000 2005 2010 2015

Pax

Year

TOTAL PAX EVOLUTION

TOTAL


Source: CAAN

The same evolution has been observed in movement terms, directly related to passengers.

Source: CAAN

-0,2

-0,1

0

0,1

0,2

0,3

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

Gro

wth

Rat

e

PAX GROWTH RATE

0

20000

40000

60000

80000

100000

120000

1975 1980 1985 1990 1995 2000 2005 2010 2015

Pax

Year

TOTAL AIRCRAFT MOVEMENT EVOLUTION

TOTAL MOV.

Domestic Movements

International Movements


So, as reflected, an obvious increasing growth has been observed in last years. This means that Nepal CAAN authority planning must be ambitious and adjusted to this positive evolution. The complementary factor to be studied is the cargo air transport evolution, due to its importance in the countrys socio economic evolution.

Source: CAAN

In this case an irregular behavior is observed. The domestic cargo air transport has been growing regular, except some years between 1995-2000. However, international cargo air transport evolution has experienced an irregular behaviour but tending to be more constant in last years. 3.2 AIR TRAFFIC SEASONAL BEHAVIOUR Besides the historical air traffic evolution, in order to a global vision of the NEPAL air transport, other facts must be observed. Air traffic seasonal behavior is essential. An annual global data does not reflect each airport monthly, weekly and daily reality, the most important factor for its design and management.

-1000000

0

1000000

2000000

3000000

4000000

5000000

6000000

0

5000000

10000000

15000000

20000000

25000000

1975 1980 1985 1990 1995 2000 2005 2010 2015

Carg

o (

Tn)

Year

TOTAL CARGO EVOLUTION

INTERNATIONAL

TOTAL MOV.

DOMESTIC


This analysis must begin with a seasonal behavior study. Regarding to these, some 1995 and 1996 seasonal data has been received, but a continuous series is necessary. This continuous series are available from 2006 to 2011:

Source: CAAN

Source: CAAN

In these graphics can be observed that, both, in domestic and international passenger traffic, a constant peak month exists every year. A constant seasonal behavior is observed, but it not too sharp.

0

50000

100000

150000

200000

JAN FEB MAR ABR MAY JUN JUL AGO SEP OCT NOV DIC

Pax

Year

DOMESTIC SEASONAL PAX EVOLUTION

2006

2007

2008

2009

2010

2011

0

50000

100000

150000

200000

250000

300000


Pax

Year

INTERNATIONAL SEASONAL PAX EVOLUTION

2006

2007

2008

2009

2010

2011


Following, the corresponding data for aircraft movement and cargo air transport:

Source: CAAN

Source: CAAN

0

2000

4000

6000

8000

10000


Mov

emen

t

Year

DOMESTIC SEASONAL AIRCRAFT MOVEMENT EVOLUTION

2006

2007

2008

2009

2010

2011

0

500

1000

1500

2000

2500


Mov

emen

t

Year

INTERNATIONAL SEASONAL AIRCRAFT MOVEMENT EVOLUTION

2006

2007

2008

2009

2010

2011


As observed, aircraft movement seasonal evolution agrees with passengers.

Source: CAAN

Source: CAAN

0

200000

400000

600000

800000

1000000

1200000


Carg

o

Year

DOMESTIC SEASONAL CARGO EVOLUTION

2006

2007

2008

2009

2010

2011

0

500000

1000000

1500000

2000000


Carg

o

Year

INTERNATIONAL SEASONAL CARGO EVOLUTION

2006

2007

2008

2009

2010

2011


3.3 AIR TRAFFIC TIPOLOGY

The air traffic typology is essential for an air traffic forecast, because each type of passenger, his origin, nationality, travel purpose may vary depending on several factors, affecting to the air traffic evolution.

3.3.1 International

The international passenger and cargo distribution has been observed in 2011 daily detailed air traffic data, and its as following:

INTERNATIONAL PASSENGER AND CARGO DISTRIBUTION

TOTAL PAX TOTAL CARGO TOTAL PAX % TOTAL CARGO % SOUTH ASIA 1071718 1381763,6 39,70% 15,69% MIDDLE EAST 1047639 3662982,1 38,80% 41,60% SOUTH EAST ASIA 305054 2591451,7 11,30% 29,43% EASTERN ASIA 275423 1168216,82 10,20% 13,27%

Source: CAAN

The photograph results:

Source: CAAN

TOTAL PAX

SOUTH ASIA MIDDLE EAST SOUTH EAST ASIA EASTERN ASIA


Source: CAAN

Nepal adopted a liberal sky policy since 1992, and since then the government has signed air agreements with four countries (Japan, UK, Qatar and Malaysia) and MOU with 16 countries (Italy, Egypt, Luxemburg, Austria, Bahrain, Oman, Macao, The Netherlands, Saudi Arabia, Jordan, Kuwait, UAE, Croatia, Israel, Philippines and Turkey. Five more new countries are willing to sign the air agreement with Nepal, namely, USA, Vietnam, Indonesia, Russia and Australia. But the main gateways for air travel from Nepal are still the airports in India (Delhi mainly), Gulf region (Doha, Dubai, Bahrain), and Thailand. Emergence of new budget airlines from India and the Gulf region has contributed to their dominance in the market. As reflected in new data received, some of these Asian gateways are hubs connecting Nepal with Europe region, North and South America and other sources. For forecasting exercises, these traffics have been not included, because the principal origins, are enough for a precise statistical analysis.

TOTAL CARGO

SOUTH ASIA MIDDLE EAST SOUTH EAST ASIA EASTERN ASIA


For more details, see Section 2.4.

DETAILED INTERNATIONAL PASSENGER AND CARGO DISTRIBUTION

TOTAL PAX TOTAL CARGO TOTAL PAX % TOTAL CARGO %

SOUTH ASIA 1071718 1381763,6 39,70% 15,69% AFGHANISTAN 0 0 0,00% 0,00% BANGLADESH 155469 301432,6 5,76% 3,42% BHUTAN 23943 62603 0,89% 0,71% INDIA 852532 719088 31,58% 8,17% PAKISTAN 39771 298640 1,47% 3,39% SRI LANKA 3 0 0,00% 0,00%

MIDDLE EAST 1047639 3662982,1 38,80% 41,60% BAHRAIN 189469 439080,8 7,02% 4,99% ISRAEL 163 0 0,01% 0,00% OMAN 67947 133047 2,52% 1,51% QATAR 298285 1206215,6 11,05% 13,70% UAE 491775 1884638,7 18,22% 21,41%

SOUTH EAST ASIA 305054 2591451,7 11,30% 29,43% CAMBODIA 10 0 0,00% 0,00% INDONESIA 0 0 0,00% 0,00% LAOS 20 0 0,00% 0,00% MALAYSIA 126186 348260,7 4,67% 3,96% MYANMAR 173 0 0,01% 0,00% PHILIPPINES 0 0 0,00% 0,00% THAILAND 178665 2243191 6,62% 25,48%

EASTERN ASIA 275423 1168216,82 10,20% 13,27% CHINA 238687 911247,52 8,84% 10,35% SOUTH KOREA 36736 256969,3 1,36% 2,92%

Source: CAAN

Filtering data from different sources, a general photograph about different international airlines operating in Nepal has been obtained. As observed, airlines operating in Nepal have been varying, and growing:


DETAILED INTERNATIONAL AIRLINE PASSENGERS DISTRIBUTION

2006 2007 2008 2009 2010 2011 TOTAL ABY Air Arabia 6928 54907 79383 131386 156147 187051 615802 5,26%

AIA 152451 152451 1,30% AUA AUA 11675 8072 103 19850 0,17% BAB 32877 46726 36279 115882 0,99% BHA 1314 7218 8532 0,07%

BBC

Biman Bangladesh

Airlines 46371 58171 100394 70727 105971 92258 473892 4,05% CCA Air China 19713 31192 16001 29620 36536 43489 176551 1,51% CES 5133 18543 26828 50504 0,43% COZ COZ 82265 1968 42694 126927 1,08%

CSN China Suthern

Airlines 16731 25971 21859 23368 46858 134787 1,15%

DRK Royal Buthan

Airlines 24523 26433 21992 21868 21942 30332 147090 1,26% ETD Etihad Airways 1061 80419 77981 116496 127125 403082 3,44% FDB FDB 1822 81446 152274 235542 2,01% GFA Gulf Air 207875 167699 264180 238527 178887 149566 1206734 10,31% GMG GMG Airlines 4272 22394 15476 37077 60425 37389 177033 1,51% HAD Dragon Air 41375 47658 68977 82854 240864 2,06% IGO 18913 18913 0,16% HKE HKE 4873 4873 0,04%

IAC

National Aviation

Company India 109055 143835 166479 199770 619139 5,29% JAI Jet Airways 85963 88668 143796 167849 255161 272560 1013997 8,66% JLL 107271 112060 105087 324418 2,77% KAL Korean Air 2535 15992 21053 33099 31528 36571 140778 1,20% KFR 55470 76586 132056 1,13% NAC NAC 174154 237751 232577 202041 846523 7,23%

NACIL 175022 175022 1,50% NPL NPL 758 758 0,01% NYT NYT 60105 60105 0,51% OEA OEA 3759 3759 0,03% OMA 15409 68964 84373 0,72%

PIA

Pakistan International

Airlines 25979 28133 53435 58161 47610 39668 252986 2,16% QTR Quatar Airways 183786 183651 195153 251214 300184 277796 1391784 11,89% RNA Nepal Airlines 276381 207552 483933 4,13% RSH Jet Lite 86475 87631 93157 267263 2,28% SEJ 22667 183594 206261 1,76% SLK Silk Air 255 44689 52372 52906 49914 200136 1,71% TFL 2660 6668 7289 16617 0,14% THA Thai Airways 196865 170133 169669 186466 175422 154070 1052625 8,99% UBD 24155 22274 46429 0,40%

OTHER OTHER 11622 14984 12320 13999 12941 12728 78594 0,67%

Source: CAAN


This general vision about the operators could be very helpful for the future management planning.

Source: CAAN

INTERNATIONAL AIRLINES

QTR Quatar Airways

GFA Gulf Air

THA Thai Airways

JAI Jet Airways

IAC National Aviation Company India

ABY Air Arabia

RNA Nepal Airlines

BBC Biman Bangladesh Airlines

ETD Etihad Airways

JLL

RSH Jet Lite

PIA Pakistan International Airlines

HAD Dragon Air

FDB FDB

SEJ

SLK Silk Air

GMG GMG Airlines

CCA Air China

NACIL

AIA

DRK Royal Buthan Airlines

KAL Korean Air

CSN China Suthern Airlines

KFR

COZ COZ

BAB

OMA

OTHER

NYT NYT

CES

UBD

AUA AUA

IGO

TFL

BHA

HKE HKE

OEA OEA

NPL NPL


3.3.2 Domestic On the domestic front, there has been equally significant and consistent growth in air traffic demand, especially in the increasing number of flights of small aircrafts. The leading carriers are Buddha Air and Yeti Airline about 75% of domestic air passenger traffic.

DETAILED DOMESTIC AIRLINES PASSENGER DISTRIBUTION

2006 2007 2008 2009 2010 2011 TOTAL

BA Buddha

Air 396599 420963 403753 556917 615567 691023 3084822 41,96%

YA Yeti

Airlines 240059 456217 457615 480856 478225 449450 2562422 34,85% AG Aigni Air 18254 28378 56094 174244 193313 197523 667806 9,08%

GA Guna

Airlines 38987 36728 17074 40834 96122 95542 325287 4,42% TA TARA AIR 42724 103224 78241 224189 3,05%

NAC 45973 25490 52220 53406 47081 224170 3,05% ST Sita Air 34010 26691 25042 25194 10983 15543 137463 1,87% CA Air China 107153 1426 19760 0 128339 1,75%

RA Nepal

Airlines 38575 6321 44896 0,61%

AD Air

Dinasty 971 1740 3181 1916 2509 2311 12628 0,17% SA 3659 6456 437 10552 0,14%

FDA FDA 204 586 657 1395 3066 2813 8721 0,12% MH 4136 3449 7585 0,10% MA Manag Air 3112 373 264 84 3833 0,05%

SH Shree

Airlines 437 236 341 61 734 168 1977 0,03% OTHERS OTHERS 1927 41 1968 0,03%

SM SM 27 56 116 196 428 1122 1945 0,03%

KA Korean

Air 415 0 415 0,01% IMPRO IMPRO 3 174 32 209 0,00%

HH HH 161 161 0,00% NSBS NSBS 4 40 0 0 44 0,00% VVIP VVIP 32 6 0 38 0,00%

Asian

Helicopter 0 0,00%

Source: CAAN


Source: CAAN

DOMESTIC AIRLINES BA Buddha Air

YA Yeti Airlines

AG Aigni Air

GA Guna Airlines NAC

ST Sita Air

CA Air China

RA Nepal Airlines


3.4 Operating Air Transport Companies Bilateral: Nepal has signed bilateral air service agreements with 36 countries. This provides for 5.7 million seats per annum to and from Nepal. However, not even 40% of this capacity is being utilized by the operating airlines so far. The signing of a letter agreement with India multiplied seat provision five times to 30,000 seats per week and unlimited air cargo flights between India and Nepal has been a milestone in Nepalese civil aviation. Airline Operators: Twenty-nine international airlines are presently operating 284 schedule air services from Kathmandu to some 22 cities in Asia and Europe. Five new international airlines started their air services to Kathmandu in 2010 (Kingfisher, Oman Air, Buddha Air, Spice Jet and Indigo Air). Buddha Air started flights to Lucknow (India) from January 2011 and to Varanashi (India) from March 1, 2012. RAK Airways is the newest airline operating services to Kathmandu from February 15, 2012. Among the other new airlines proposing services to Nepal are Turkish Airline from Istanbul (October 2012), Air Lankan from Colombo. Thus, the number of international airlines operating air services from Kathmandu has reached 29, and the number of flights they operate increased to 284 flights per week, thereby producing some 47,523 seats for passengers and 914 ton capacity for cargo movement from Kathmandu each week. In the last years, five new airlines (namely, Buddha Air International, Shree Airlines International, Akashbhairav Air International, Goma Air and Skytech Heli Services were licensed to operate air services and Air Operator Certificates (AOC) have been issued. Three of them are fixed wing operators and two are helicopter companies. Under the policy to access remote areas, single engine fixed wing aircraft and helicopter operations have been facilitated to operate passenger and cargo services in remote mountain areas of Nepal. Out of the 86 aircrafts registered in Nepal, 69 aircrafts are in operational condition. This includes 46 fixed wing aircraft and 18 helicopters. On the aviation sports side, five of the eight companies who have acquired Air Operator Certificate (AOC) are operational. For the promotion of tourism through aerial sports and entertainment activities, aviation sports activities have been extended beyond Pokhara to Kathmandu, Bandipur and other touristic areas of Nepal.


NEW AIRLINES OPERATION SERVICE START

Year New Airlines operating services Service start 2006 GMG from Dhaka Bangladesh

Air Arabia from Sarjah, United Arab Emirates Korean Air from Seoul, South Korea

October 23 November 7 November 13

2007 China Southern from Guangzhou, China Etihad from Abu Dhabi Silk Air from Singapore Dragon Air from Hong Kong Hong Kong Express from Hong Kong Orient Thai from Bangkok, Thailand

February 5 October 26 October 30 December 2 December 13 December 30

2008 Fly Yeti.com/ Air Arabia to Dubai, UAE July 8 2009 Bahrain Air from Bahrain

China Eastern from Kunming, China TUI/ Arkfly from Amsterdam, The Netherlands Fly Dubai/ Dubai Aviation from Dubai, UAE

March 17 September 8 October December 15

2010 Kingfisher Airline from Delhi, India Oman Air from Muscat, Buddha Air to Paro (Bhutan), Lucknow and Varanasi, India Spice Jet from Delhi, India Indigo Airlines from Delhi, India United Airlines from Dhaka,Bangladesh

January 15 September 2 September 23 October 7 October 28 December

2012 RAK Airways from Dubai February 12

Source: CAAN


3.5 Air Transport Infrastructures in Nepal At present there are 54 airports including six under construction. Among these airports, 34 airports are operational with regular air services:

International Airport. Tribhuvan International Airport in Kathmandu. Domestic Hub Airports. There are 4 regional Airports: Biratnagar Airport,

Pokhara Airport, Bhairawa Airport and Nepalgunj Airport. Other Domestic Airports. There are 36 airports, only 29 in operation.

There is a new project for a second international Airport at Nijgadh. Nepal is a mountainous country and most of the areas in western side are not linked by road, so the air transport is the only mean of transportation. The Government has been demanded to build new airports because of social connectivity. Maintenance of many domestic airports is problematic and new airports are being built under political pressure. Some strict criteria are needed for adding new airports. In this aspect, CAAN Board has decided handing over the operation and management of some 7 remote area airports in western Nepal to the local community (district development committee) and the concept is in the process of government approval.


Tribhuvan International Airport Significant progress has been made in the airport recently: Now, under a loan and grant contract between Nepal government and Asian Development Bank signed on May 25, 2010, a loan of US$70 million and grant of US $ 10 million will be available for undertaking Priority 1A works in the first phase of TIA Master Plan 2026 in order to improve the capacity of air transport and flight safety in TIA. In addition to the above, the loan and grant program for TIA will also cover some works at Simikot airport, Rara airport at Mugu and Lukla airport.

Tribhuvan International Airport

Pokhara Airport Pokhara Regional International airport is one of the national high priority projects in Nepal. 34 years ago Nepal Government acquired land of 158 hector for regional International Airport. This site is 4 KM far (east) from existing Pokhara airport and


potential for 2500 meter length runway and can be constructed to ICAO 4D requirement. Government of Nepal is willing to construct this airport from Chinese Exim Bank Loan. For that CAAN has published a notice for Engineering, procurement and Construction (EPC) contract Tender from Chinese eligible bidders on February 9, 2011. The last date of tender submission is extended to April 24, 2012. The aim of these projects to allow direct flights to Lucknow in India at existing Pokhara Airport:

Pokhara Airport

Gautam Buddha Regional International Airport An agreement has been signed between Nepal Government and Asian Development Bank for US$28 million loan and US$6.41 million grant for the development of Gautam Buddha International airport under South Asian Tourism Infrastructure Development project to help develop tourism in Lumbini area and to diversify congestion at TIA.


Currently Gautam Buddha Airport (GBA) is operated as a domestic hub airport. The length of existing runway is 1524m and is 30m wide. Government of Nepal has received a US$28 million loan and US$6.41 million grant from ADB through South Asia Tourism Infrastructure Development Project (SATIDP) for upgrading the GBA to Regional International Airport. Financing agreement between ADB and Finance Ministry was signed on April 25, 2010. Design and Supervision consultant was mobilized from September, 2011 and right now they are engaged on design works. Procurement process also has started on January 2012 for prequalification of contractor for civil works. Submitted prequalification applications are under the evaluation. Probably the civil works contractor will be mobilized on October, 2012. Project Completion date will be 2015.

Gautam Buddha Airport

Second International Airport In 2007, Government of Nepal decided to build full flagged Second International Airport (SIA) in Bara, Nijgadgh through BOOT Scheme. One Korean private company conducted detail feasibility study and submitted a proposal to Nepal Government through BOOT scheme. This report is under study in the Ministry of Tourism and Civil Aviation.


Decisions on this project will affect severely to the Capacity Development Plan of the Civil Aviation Authority of Nepal.

Bara Region Nepal


4 DATA BACKGROUND

ERM-PROINTEC-INECO joint venture has been named to, as part of the Nepal Airport Development project, study the Nepal Air Traffic configuration and its forecast for the next 20 years. This forecast must observe as much as factors as possible, including determining and historical data,. This makes possible a complete short term, long term, peak hour and other air traffic analysis for Nepal in different scenarios for the next years. For that purpose, Nepal TIA CAAN authorities provide ERM-PROINTEC-INECO JV the following documentation folders:

2006 data CAAN Report 2010 RAJ Data 2007 DATA 2012 Flight permission airport data INTERNATIONAL DATA 2010 2011

Containing more than 1400 archives. As well as:

Paper Documents: o Civil Aviation Report 2009-2010 o Civil Aviation Report 2008

On the other hand, MOTCA provides:

Paper Documents: o Nepal Tourism Statistics 2010 o Nepal Tourism Statistics 2008

Electronic Documents:

o Nepal Tourism Politics o Nepal Economic information


Starting from this initial point, an exhaustive leaking and analysis have been done in order to clean and organize the necessary data for each kind of forecast. As a result of this analysis, the following archives have been generated:

DATA SUMMARY 31.08.12 2011_DAILY PEAK HOUR REGIONAL AIRPORTS FORECAST STD-STI NATCONSUMEPRICE LTD-LTI INDIA NEPAL GDP

Instead the documentation is wide, is not continuous and has low detail for a more precise forecast. Due to this, some hypothesis have been done during the study, and these hypothesis are described in each following section.


5 METHODOLOGY

REQUIRED

PROVIDED

AVAILABLETEMPORAL

SERIES

?

HYPOTHESIS STI

STD

LTI

LTD

AIR TRAFFICCHARACTERIZATION

FORECAST

OTHER INPUTS

VARIABLES


6 IBM SOFTWARE SPSS: Predictive analytics software and solutions SPSS Predictive Analytics software and solutions has been the selected

software for this Nepal Air Traffic forecasting and data analysis. SPSS, developed by IBM, is among the most widely used programs for

statistical analysis in social science. It is used by market researchers, health researchers, survey companies, government, education researchers, marketing organizations and others.

The original SPSS manual (Nie, Bent & Hull, 1970) has been described as

one of "sociology's most influential books". In addition to statistical analysis, data management (case selection, file reshaping, creating derived data) and data documentation (a metadata dictionary is stored in the datafile) are features of the base software. This software has been supported by excel statistical tools, and guided by ICAO and FAA manuals and recommendation for Air Traffic Forecasting. Each followed criteria is fully described in every section, as well as each formulated hyphotesis. 7 REFERENCES

Doc. 8991 Manual on Air Traffic Forecasting. ICAO FAA Forecasting Aviation Activitiy by Airports IATA Airport Development Reference Manual ICAO Doc. 9184 Airport Planning Manual

8 NOMENCLATURE

STD: Short Term Domestic Forecasting STI: Short Term International Forecasting LTD: Long Term Domestic Forecasting LTI: Long Term International Forecasting CARGO: Freight, in Kgs. PAX: Passengers MOV: Aircraft operation, movement, landing or take off DOM: Domestic INT: International GDP: Gross Domestic Product


9 DOMESTIC TRIBHUVAN SHORT TERM FORECASTING 9.1 AVAILABLE DATA The following historical data are needed fore an international medium term forecasting:

Medium-Term Forecast, focused on monthly data for the past 5 years (60 months).

The present data analysis has obtained the following series:

Monthly data from 2006 to 2011 for aircraft, passengers and cargo. Data by airline from 2006 to 2011 for aircraft, passengers and cargo. Daily domestic traffic by region/country/airport destination/origin 2011 10 Domestic (regional) airports traffic annual data, from 2003 to 2009 Tourists arrival information 2008-2010

9.2 DOM. SHORT TERM FORECASTING HYPHOTESIS AND USED MODEL Regarding to the available data, the initial approach has been the following: The short term domestic traffic evolution, the next years, has been considerate that will be in accordance to the National Consume Price evolution in these years. Regarding to this hypothesis, this National Consume Price has been obtained from the available documentation, from 01.07.2007 to 01.09.2011.


NATIONAL CONSUME PRICE RATE

National Consume Price 01.07.2007 108,15 01.08.2007 111,15 01.09.2007 112,10 01.10.2007 112,15 01.11.2007 111,35 01.12.2007 110,15 01.01.2008 110,10 01.02.2008 111,15 01.03.2008 112,85 01.04.2008 114,70 01.05.2008 116,45 01.06.2008 118,50 01.07.2008 121,50 01.08.2008 124,70 01.09.2008 126,55 01.10.2008 127,30 01.11.2008 126,45 01.12.2008 125,10 01.01.2009 124,95 01.02.2009 125,65 01.03.2009 126,65 01.04.2009 128,50 01.05.2009 130,70 01.06.2009 132,15 01.07.2009 134,35 01.08.2009 136,70 01.09.2009 137,75 01.10.2009 138,55 01.11.2009 138,75 01.12.2009 138,30 01.01.2010 138,55 01.02.2010 138,80 01.03.2010 139,10 01.04.2010 140,45 01.05.2010 141,85 01.06.2010 143,55 01.07.2010 146,80 01.08.2010 149,05 01.09.2010 149,70 01.10.2010 150,45 01.11.2010 151,15 01.12.2010 152,60 01.01.2011 153,30


01.02.2011 153,15 01.03.2011 153,85 01.04.2011 154,45 01.05.2011 154,65 01.06.2011 156,70 01.07.2011 159,45 01.08.2011 161,10 01.09.2011 162,75

Source: CAAN

Departing from this initial data, a simple regression for this National Consume Price and Domestic Short Term Forecasting has been set out, and the following predictions and forecastings have been estimated. For this porpoise, SPSS software program has been utilized. Through SPSS, and for each case of series data, the best statistical forecast or prediction has been chosen: ARIMAS, Holt, Medias, regression, exponential softening, etc. As the short term National Consume Price factor is cyclic, seasonal, a previous seasonal factorization have been carried out, in order to avoid this influence in the short term forecasting. Once the forecasting is estimated, this seasonal characteristic has been replaced.


Source: Ermc-Ineco-Prointec

Obtaining the following forecast:

NATIONAL CONSUME PRICE RATE FORECAST

National Consume Price 01.10.2011 161,24 01.11.2011 162,85 01.12.2011 164,63 01.01.2012 166,11 01.02.2012 167,27 01.03.2012 168,03 01.04.2012 168,45 01.05.2012 168,89 01.06.2012 169,38 01.07.2012 169,98 01.08.2012 170,78 01.09.2012 171,64 01.10.2012 172,69 01.11.2012 173,98

0,00

50,00

100,00

150,00

200,00

250,00

0 20 40 60 80 100 120 140

Nat

iona

l Con

sum

e Pr

ice

Months

NATIONAL CONSUM PRICE EVOLUTION

Seasonally fitted series NatConsumPrice

ARIMA


01.12.2012 175,27 01.01.2013 176,49 01.02.2013 177,55 01.03.2013 178,31 01.04.2013 178,87 01.05.2013 179,36 01.06.2013 179,83 01.07.2013 180,36 01.08.2013 180,98 01.09.2013 181,69 01.10.2013 182,51 01.11.2013 183,43 01.12.2013 184,39 01.01.2014 185,32 01.02.2014 186,15 01.03.2014 186,82 01.04.2014 187,36 01.05.2014 187,81 01.06.2014 188,23 01.07.2014 188,65 01.08.2014 189,11 01.09.2014 189,63 01.10.2014 190,21 01.11.2014 190,85 01.12.2014 191,52 01.01.2015 192,18 01.02.2015 192,78 01.03.2015 193,29 01.04.2015 193,71 01.05.2015 194,05 01.06.2015 194,35 01.07.2015 194,63 01.08.2015 194,93 01.09.2015 195,25 01.10.2015 195,61 01.11.2015 196,00 01.12.2015 196,40 01.01.2016 196,80 01.02.2016 197,17 01.03.2016 197,48 01.04.2016 197,72 01.05.2016 197,91 01.06.2016 198,05 01.07.2016 198,18 01.08.2016 198,29 01.09.2016 198,41 01.10.2016 198,55


01.11.2016 198,70 01.12.2016 198,86 01.01.2017 199,02 01.02.2017 199,15 01.03.2017 199,24 01.04.2017 199,29 01.05.2017 199,29 01.06.2017 199,26 01.07.2017 199,20 01.08.2017 199,13 01.09.2017 199,05 01.10.2017 198,97 01.11.2017 198,90 01.12.2017 198,83



9.3 STD RESULTS 9.3.1 Passengers Available data about domestic passengers is the following:


Departing from the National Consume Price Evolution, a lineal correlation has been estimated, obtaining domestic passengers Short Term Forecast:

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000

Oct-06

Nov-07

Dec-08

Jan-10

Feb-11

Apr-12

Dom

esti

c Pa

x

Months

AVAILABLE DATA DOMESTIC MONTHLY PASSENGERS 2007-2011

Domestic Passengers



STD PASSENGERS RESULT TABLE

Years STD Passengers Growing Rates 2008 1.036.586 2009 1.377.868 33% 2010 1.542.393 12% 2011 1.645.763 7% 2012 1.948.000 18% 2013 2.120.600 9% 2014 2.259.600 7% 2015 2.360.800 4%

Source: Ermc-Ineco-Prointec It is very clarifying the resulting graphic correlation between domestic passengers evolution and the National Consume Price evolution. As it can be observed, enough for an econometrical model. The R2 is high, near 80%,.

0

50000

100000

150000

200000

250000

300000

Oct-06

Feb-08

Jul-09

Nov-10

Apr-12

Aug-13

Dec-14

May-16

Pax

Months

DOMESTIC SHORT TERM PASSENGERS FORECAST

Domestic Passengers Short Term Forecast



9.3.2 Aircraft Movements The Aircraft Movements forecast has been estimated departing from the passengers forecast. With the available data, the average aircraft occupation has been observed, and different trend and forecasting techniques have been proved over it: Trend lines (lineal, potential and logarithmic), as well as constant occupation and different ARIMAS trough SPSS. The observed conclusion with this study is that, with all techniques, this occupation is almost constant, so the most coherent ARIMA has been done for future occupation rate. This occupation rate has been developing from 13,5 aprox. in 2007 to 20 passengers per aircraft in 2011, so an estimation from 20 passengers to 22 has been considered for the short term future. Showed in the following graphic:

0,00

50,00

100,00

150,00

200,00

250,00

0

50000

100000

150000

200000

250000

300000

oct-06

feb-08

jul-09

nov-10

abr-12

ago-13

dic-14

may-16

Pax

Months

DOMESTIC SHORT TERM FORECAST VS.

NATIONAL CONSUME PRICE

Domestic Passengers Short Term Forecast

National Consum Price



According to these criteria, the results for domestic aircraft movement forecast are the following:


0

5

10

15

20

25

oct-06

feb-08

jul-09

nov-10

abr-12

ago-13

dic-14

may-16

Pax

Months

DOMESTIC AIRCRAFT OCCUPATION RATE FORECAST

Aircraft Occupation Rate Forecast

0

2000

4000

6000

8000

10000

12000

Oct-06

Feb-08

Jul-09

Nov-10

Apr-12

Aug-13

Dec-14

May-16

Mov

Months

DOMESTIC AIRCRAFT MOVEMENT FORECAST

Commercial Aircraft Movement Short Term Forecast


STD AIRCRAFT MOVEMENT RESULT TABLE

Years STD Commercial

Aircraft Movements

Growing Rates

2008 69.286 2009 76.191 10% 2010 79.892 5% 2011 81.594 2% 2012 95.825 17% 2013 102.100 7% 2014 106.825 5% 2015 109.875 3%


9.3.3 Cargo The Cargo forecast has been estimated departing from the aircraft movements forecast. With the available data, the average cargo occupation (kgs.) over aircraft has been observed, and different trend and forecasting techniques have been proved over it: Trend lines (lineal, potential and logarithmic), as well as constant occupation and different ARIMAS trough SPSS. The observed conclusion with this study is that, with all techniques, this occupation is almost constant, so the most coherent ARIMA has been done for future occupation rate, estimating a 56 kgs. per domestic movement approximately.



Following, results:


0

10

20

30

40

50

60

70

80

oct-06

feb-08

jul-09

nov-10

abr-12

ago-13

dic-14

may-16

Carg

o ( K

gs.)

Months

DOMESTIC AVERAGE CARGO OCCUPATION OVER AIRCRAFT

Domestic Average Cargo Occupation over aircraft

0

200000

400000

600000

800000

1000000

1200000

Oct-06

Feb-08

Jul-09

Nov-10

Apr-12

Aug-13

Dec-14

May-16

Carg

o (K

gs)

Months

DOMESTIC CARGO FORECAST

Domestic Cargo (Kgs.) Short Term Forecast


STD CARGO ( KGS) RESULT TABLE

Years STD Cargo Growing Rates 2008 4.803.533 2009 4.384.475 -9% 2010 3.731.083 -15% 2011 4.400.231 18% 2012 5.221.400 19% 2013 5.552.450 6% 2014 5.807.500 5% 2015 5.972.300 3%


10 INTERNATIONAL TRIBHUVAN SHORT TERM FORECASTING 10.1 AVAILABLE DATA The following historical data are needed for an international medium term forecasting:

Medium-Term Forecast, focused on monthly O/D data for the past 5 years (60 months).

The data analysis has obtained the following series:

Monthly data from 2006 to 2011 for aircraft, passengers and cargo. Data by airline from 2006 to 2011 for aircraft, passengers and cargo. Daily traffic by region/country/airport destination/origin 2011 Tourists arrival information 2008-2010

10.2 INT. SHORT TERM FORECASTING HYPHOTESIS AND USED MODEL The short term international traffic evolution,for the close years, has been considerate that will be in accordance to the domestic vs. international traffic historical relationship in the last past 5 year. This PAXINT/PAXDOM rate has been obtain from 2007 to 2011and statistically predicted for the short term forecast. The result is that this rate is approximately 170 % for the next close years.



As observed, except in specific months, is a very constant relationship and easily predictable in statistic terms. Departing from this initial data, STI, Short Term International Passenger forecast has been developed, and the same technique as for the domestic movement and cargo forecasting has been followed for the international movement and cargo prediction. 10.3 STI RESULTS 10.3.1 Passengers Available data about international passengers is the following:

0%

50%

100%

150%

200%

250%

300%

350%

oct-06

feb-08

jul-09

nov-10

abr-12

ago-13

dic-14

may-16

Rate

Months

INTERNATIONAL PASSENGERS OVER DOMESTIC PASSENGERS RATE

% International Passengers / Domestic Passengers



Departing from this data and its rate over domestic passengers, international passengers Short Term Forecast:


0

50000

100000

150000

200000

250000

300000

Oct-06

Apr-07

Nov-07

Jun-08

Dec-08

Jul-09

Jan-10

Aug-10

Feb-11

Sep-11

Apr-12

PaxI

nt

Months

AVAILABLE DATA INTERNATIONAL MONTHLY PASSENGERS 2007-2011

International Monthly Passengers

0 50000

100000 150000 200000 250000 300000 350000 400000 450000 500000

Oct-06

Feb-08

Jul-09

Nov-10

Apr-12

Aug-13

Dec-14

May-16

PaxI

nt

Months

INTERNATIONAL SHORT TERM PASSENGERS FORECAST

International Passengers Short Term Forecast


STI PASSENGERS RESULT TABLE

Years STI Passengers Growing Rates 2008 1.830.630 2009 2.027.147 11% 2010 2.429.130 20% 2011 2.705.577 11% 2012 3.286.500 21% 2013 3.582.300 9% 2014 3.817.100 7% 2015 3.988.100 4%


10.3.2 Aircraft Movements The Aircraft Movements forecast has been estimated departing from the passengers forecast. With the available data, the average aircraft occupation has been observed, and different trend and forecasting techniques have been proved over it, as well as constant occupation and different ARIMAS trough SPSS. The observed conclusion with this study is that, with all techniques, this occupation is almost constant, so the most coherent ARIMA has been done for future occupation rate.



This occupation rate has been varying between 102 and 144 passengers from 2007 to 2011, so an estimation of 124 passengers per aircraft has been considered for the short term future. According to this, the following results:

0

20

40

60

80

100

120

140

160

oct-06

feb-08

jul-09

nov-10

abr-12

ago-13

dic-14

may-16

Pax

Months

INTERNATIONAL AIRCRAFT OCCUPATION RATE FORECAST

Aircraft Occupation Rate Forecast



STI AIRCRAFT MOVEMENT RESULT TABLE

Years STI Aircraft Movements Growing

Rates 2008 14.276 2009 15.701 10% 2010 19.417 24% 2011 24.652 27% 2012 26.450 7% 2013 28.850 9% 2014 30.750 7% 2015 32.075 4%


10.3.3 Cargo The Cargo forecast has been estimated departing from the aircraft movements forecast. With the available data, the average cargo occupation (kgs.) over aircraft has been observed, and different trend and forecasting techniques have been proved over it trough SPSS. The observed conclusion with this study is that this rate has been very irregular, decreasing in last year. With all techniques the most coherent rate

0

500

1000

1500

2000

2500

3000

3500

4000

Oct-06

Feb-08

Jul-09

Nov-10

Apr-12

Aug-13

Dec-14

May-16

Mov

Months

INTERNATIONAL AIRCRAFT MOVEMENTS FORECAST

Commercial Aircraft Movement Short Term Forecast


estimation for the close future occupation rate, is 779 kgs. aprox. per international movement.


As can be observed, 2011 cargo data falls significantly, but this last has not been highly considered, being them unusual.


0

200

400

600

800

1000

1200

1400

1600

oct-06

feb-08

jul-09

nov-10

abr-12

ago-13

dic-14

may-16

Carg

o ( K

gs.)

Months

INTERNATIONAL AVERAGE CARGO OCCUPATION OVER AIRCRAFT

International Average Cargo Occupation over aircraft

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

Oct-06

Feb-08

Jul-09

Nov-10

Apr-12

Aug-13

Dec-14

May-16

Carg

o (K

gs)

Months

INTERNATIONAL CARGO FORECAST

International Cargo (Kgs.) Short Term Forecast


STI CARGO ( KGS) RESULT TABLE

Years STI Cargo Growing Rates 2008 13.886.146 2009 15.349.037 11% 2010 14.003.661 -9% 2011 8.817.283 -37% 2012 20.617.875 134% 2013 22.475.125 9% 2014 23.948.050 7% 2015 25.020.525 4%


11 INTERNATIONAL TRIBHUVAN LONG TERM FORECASTING 11.1 AVAILABLE DATA The following historical data are needed fore an international long term forecasting:

O/D annual data for the last 15 years.

The available data analysis has obtained the following series:

Annual data from 1981 to 2011 for aircraft, passengers and cargo Monthly data from 2006 to 2011 for aircraft, passengers and cargo. Data by airline from 2006 to 2011 for aircraft, passengers and cargo. Daily traffic by region/country/airport destination/origin 2011

11.2 INT. LONG TERM FORECASTING HYPHOTESIS AND USED MODEL Regarding to the available data, the initial approach and hyphotesis is that the international long term air traffic is related to each origin/destination country. GDPs of all principal origin/destination countries (see section 2.3.1. above) have been observed, and India and Nepal GDPs have been finally selected for the different scenarios forecasting.


Depending of different countries GDPs and forecasting models, the scenario changes in an optimistic or pessimistic way, so, as ICAO recommendations indicate, this exercise is very useful and lighting for future long term different situations study. The present study observes as first step the pessimistic scenario, and second, the optimistic scenario. Departing from these to situation, a probable scenario is calculated statistically. 11.3 LTI RESULTS For a complete future vision of the Nepal air traffic demand, a several situation exercise have been done, in order to study the different factors influence in the future air traffic demand. Available data about international passengers is the following: GDP: Gross domestic product based on purchasing-power-parity (PPP) per capita. Source: International Monetary Fund ( IMF). IMFs real data, as well as IMFs prediction for GDPs until 2017 has been utilized, and a prediction has been done over this series with SPSS.



Regarding to international passengers historical evolution


0 1000 2000 3000 4000 5000 6000 7000 8000 9000

1970 1980 1990 2000 2010 2020 2030 2040

GD

P

Year

GDP EVOLUTION AND PREDICTION 1981-2035

India

Nepal

0

500000

1000000

1500000

2000000

2500000

3000000

1975 1980 1985 1990 1995 2000 2005 2010 2015

Pax

Year

INTERNATIONAL PAX EVOLUTION

DEPARTURES

ARRIVALS

TOTAL


11.3.1 PESSIMISTIC For a pessimistic situation, India and Nepal GDP evolution has been considered. Departing from this hypothesis, SPSS statistical prediction has been obtained with the following criteria:

Independents: India y Nepal. ARIMA ( 0,2,0). Transf Func..: NEPALGDP: num=1 Denom= 0 Dif.=0 Retardo=1 INDIA: None

And the same methods as in ST Forecasting have been utilized to extrapolate passengers predictions to aircraft movements and cargo predictions. 11.3.1.1 Passengers


0

1.000.000

2.000.000

3.000.000

4.000.000

5.000.000

6.000.000

7.000.000

8.000.000

9.000.000

10.000.000

2005 2010 2015 2020 2025 2030 2035 2040

Pax

Year

INTERNATIONAL PESSIMISTIC PASSENGER LONG TERM FORECAST


LTI PESSIMISTIC PASSENGER RESULT TABLE

Years LTI Pessimistic

Passengers Forecast

Growing Media Rate 2011-2035

2008 1.830.630 2009 2.027.147 2010 2.429.130 2011 2.705.577 2012 3.286.500 2013 3.582.300 2014 3.817.100 2015 4.062.000 2016 4.307.000 2017 4.548.000 2018 4.769.000 2019 4.969.000 2020 5.152.000 2021 5.321.000 2022 5.478.000 2023 5.628.000 2024 5.775.000 2025 5.923.000 2026 6.077.000 2027 6.243.000 2028 6.425.000 2029 6.630.000 2030 6.865.000 2031 7.137.000 2032 7.452.000 2033 7.820.000 2034 8.248.000 2035 8.746.000 5,01%



11.3.1.2 Aircraft Movements The Aircraft Movements forecast has been estimated departing from the passengers forecast. With the available data, the average aircraft occupation has been observed, and different trend and forecasting techniques have been proved over it.


This occupation rate has been growing irregularly from 62 and 124 passengers from 1981 to 2011, so a growing statistical estimation has been considered, from 124 passengers per aircraft in 2012 to 168 passengers per aircraft in 2035.

0

20

40

60

80

100

120

140

160

180

1970 1980 1990 2000 2010 2020 2030 2040

Pax

Year

INTERNATIONAL AIRCRAFT OCCUPATION RATE FORECAST

International aircraft Occupation Rate



0

10.000

20.000

30.000

40.000

50.000

60.000

2005 2010 2015 2020 2025 2030 2035 2040

Pax

Year

INTERNATIONAL PESSIMISTIC AIRCRAFT MOVEMENT LONG TERM FORECAST


LTI PESSIMISTIC AIRCRAFT MOVEMENTS RESULT TABLE

Years LTI Pessimistic

Aircraft Movements

Forecast

Growing Media Rate 2011-2035

2008 14.276 2009 15.701 2010 19.417 2011 24.652 2012 26.450 2013 28.850 2014 30.750 2015 31.350 2016 32.750 2017 34.050 2018 35.200 2019 36.150 2020 36.950 2021 37.650 2022 38.225 2023 38.750 2024 39.225 2025 39.700 2026 40.200 2027 40.775 2028 41.425 2029 42.225 2030 43.175 2031 44.350 2032 45.750 2033 47.425 2034 49.450 2035 51.825 3,14%



11.3.1.3 Cargo The Cargo forecast has been estimated departing from the aircraft movements forecast. With the available data, the average cargo occupation (kgs.) over aircraft has been observed, and d

nepal air traffic demand

Documents

air traffic tipology

air transport infrastructures

air traffic seasonal

data background

available data

predictive analytics

ibm software spss

used model