RA6997498 Batmyagmar Myagmar

7/29/2019 RA6997498 Batmyagmar Myagmar

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National Cheng Kung University

Institute of International Management

Masters Thesis

An Alternative Pricing Framework for the


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ACKNOWLEDGEMENTS

Sincere thanks and appreciation go to my advisor Professor Alan J. Webb for his

guidance, patience and help during the process of this thesis. I would also like to

thank my committee members Professor Kevin P. Hwang and Professor Shao-Chi

Chang for their insight and critics that helped me gain a fuller understanding and

guide me on the correct path. To Odonchimeg Myagmarsuren for your great support

and friendship during this amazing experience in Taiwan, As well as great help during

the application for this scholarship. I would like to thank Delgersaikhan Nyamjav

economist of Authority of Mongolian Railway for providing such vital information

for this thesis. At the same time, I would like to express my sincere thanks to all the


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ABSTRACT

Keywords: Railway reform, Infrastructure vertical separation, Infrastructure

charging price.

This study examines the result of infrastructure vertical separation in Mongolian

railway. Mongolian railway started railway reform it include a lot of steps with the

aim of increase railway capacity and performance. One main step is to implement

infrastructure vertical separation and to find out optimal Infrastructure charging price.

We used secondary data from Ulaabaatar Railway and Authority of Mongolian

il f 2005 2011 I d h i f i l i l


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TABLE OF CONTENTS

ACKNOWLEDGEMENTS ........................................................................................... I

ABSTRACT .................................................................................................................. II

TABLE OF CONTENTS ............................................................................................. III

LIST OF TABLES ........................................................................................................ V

LIST OF FIGURES ................................................................................................... VII

CHAPTER ONE INTRODUCTION ............................................................................. 8

1.1 Research Background. ..................................................................................... 8

1.1.1 General Condition of Economic and Transport of Mongolia. .............. 8

1.1.2 Mongolian Railway.............................................................................. 11

1.1.3 Starting Rail Sector Reform. ............................................................... 13

1.1.4 Some Result of Reform. ....................................................................... 14


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CHAPTER FOUR ........................................................................................................ 42

DATA ANALYSIS ...................................................................................................... 42

4.1 Introduction. ................................................................................................... 42

4.2 Data Collection. ............................................................................................. 42

4.2.1 Transported Production Data.............................................................. 42

4.2.2 UBR Cost Statement Data ................................................................... 47

4.2.3 Pricing Data Collection. ..................................................................... 48

4.3 Forecasting ..................................................................................................... 50

4.3.1 Moving Average................................................................................... 52

4.3.2 Weighted Moving Average ................................................................... 53

4.3.3 Exponential Smoothing........................................................................ 54

4.3.4 Least Squares Method...................................................................... 56

4.4 Linear Programming ...................................................................................... 59


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LIST OF TABLES

Table 1-1Mongolian Industrial Output......................................................................... 9

Table 1-2Mongolian Freight Transport...................................................................... 10

Table 2-1 Freight European Union Model Share (%, in tons.km) .............................. 18

Table 2-2 UBR Investment from Mongolia .................................................................. 21

Table 2-3 UBR Rolling Stock Age................................................................................ 22

Table 2-4 Current Practice of Infrastructure Charging Price in European Union .... 28

Table 2-5Main Track/km Cost of Infrastructure Maintenance and Renewal............. 30

Table 4-1(a) Freight production data of UBR ............................................................. 44

Table 4-1(b) Freight production data of UBR by Percentage..................................... 45


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Table 4-13 Forecasting Results in 2014 ..................................................................... 58

Table 4-14 LP variables.............................................................................................. 59

Table 4-15 First case LP constraints.......................................................................... 61

Table 4-16 First Case LP Production mix of UBR ..................................................... 62

Table 4-17 Second Case LP Constraints .................................................................... 65

Table 4-18 Second Case LP Production mix of UBR and Demand of Private

Operators ................................................................................................. 66

Table 4-19 Private Operator companies LP Constraints for Production Mix.......... 67

Table 4-20 Second case LP Optimal Transportation Service Mix of Private Operator

Companies................................................................................................ 67

Table 5-1 Profit level of railway service (Revenue-Variable Cost)............................ 71


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LIST OF FIGURES

Figure 1-1. Mongolian gross domestic product ............................................................. 9

Figure 1-2. Mongolian industrial output ..................................................................... 10

Figure 1-3. Mongolian freight transport ...................................................................... 11

Figure 1-4. Mongolian railway lines (2010) ................................................................ 12

Figure1-5. Mongolian railway structure change ........................................................ 13

Figure 2-1. Railway infrastructure full cost ................................................................ 26

Figure 2-2. International main track/km cost of infrastructure maintenance and

renewal ..................................................................................................... 30

Figure 3-1. Conceptual model ..................................................................................... 35


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CHAPTER ONE

INTRODUCTION

1.1 Research Background.

The goal of this research is to investigate influence of partly vertical separation in

Ulaanbaatar Railway (UBR) Joint Venture Company using infrastructure charging

price for private operator transportation companies. This has been pushed by different

factors such as Mongolian economic growth, declining railway transport modal share,

international new trend to develop railway sector, attempts of railway sector reform of

Mongolia and the need to attract investment by private investors.


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Figure 1-1. Mongolian gross domestic product

Source: Adopted from the trade economics web page of World Bank (2010)

Table 1-1

Mongolian Industrial Output

2005 2006 2007 2008 2009 2010 2011

Percent of Mining

d i

66.30 64.09 58.31 56.83 58.57 58.65 58.53


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Figure 1-2. Mongolian industrial output

Source: Mongolian statistical yearbook (2008, 2011)

Table 1-2

M li F i h T

0.0

500.0

1000.0

1500.0

2000.0

2500.0

2005 2006 2007 2008 2009 2010 2011

Gross Industrial Output

Mining and quarrying bil.tug Total Gross Industrial output bil.tug


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Figure 1-3. Mongolian freight transport

Source: Mongolian statistical yearbook (2008, 2011)

Although rail transport is environmentally safety and fuel efficient sector of

transport, the modal share is still declining in recent years. We can see railway

t t d l h f t t l t t f T bl 1 2 F l d l k d di

0.00

10.00

20.00

30.00

40.00

50.00

2005 2006 2007 2008 2009 2010 2011

Freigth Transportation


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Figure 1-4. Mongolian railway lines (2010)

Mongolian Government consider about problems of UBR encountered as

f ll


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Until in 2007

After 2007

Ulaanbaatar railway (UBR) 50% Mongolian Gov 50% Russia owned

Railway Authority of Mongolia

UBR

50% Mongolian Gov50% Russia owned

Mongolia railway

51% Mongolian Gov49% Public owned

Private railwaycompanies

100% Private

R

eform


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As defined by Mongolian government, reform goals include the following:

Reduce government expenditures and liabilities associated with providing railwayservices

Improve railway financial performance and sustainability Attract private capital to the rail sector to alleviate government investment

requirements

Eliminate transport capacity constraints to economic growth Increase customer responsiveness and improve services, including efficiency gains

so transport charges can be reduced

Adopt requirements to increase competition, provide access to strategic nationalinfrastructure, or introduce new rail transport laws and regulations

1.1.4 Some Result of Reform.

N P i d S il i bli h d d h


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been declining in spite of a rapid increase in economic growth for the country. After

the 2007 reform conditions seemed to improve a little and the rate of decline

decreased but still rail freight growth is far behind the economic growth of the

country. For a country whose economy depends on a rapidly expanding mining

industry, rail freight is a major impediment to the countrys economic growth if

Mongolia cannot solve the problems faced by the Mongolian railway sector.

Why infrastructure charge pricing is the one of the solutions?

Until 2010 Mongolian private rolling stock owners increased their purchase of

the number of rolling stocks. For example one of the private companies had over 1000

freight car and 8 locomotives. If the Railway Authority and UBR give them chance to

use the rail network infrastructure and just transport as an operator company, they

have potential to provide transportation services. Most other countries with modern

rail networks use a pricing model that allows separated infrastructure owner and


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1.2 Research Objectives.

This thesis first gives an overview about the railway reform, separating

infrastructure charger from rolling stock and other equipments usage charges. It will

include a review of Mongolian transport market trends. Infrastructure charging price

application of Mongolian railway sector can improve their performance compared to

without separation trend. Cases featuring such as private railway companies

improvement in condition of separated structure of railway sector would present them

long term safety business environment. This information should provide the

government of Mongolia as well as the Authority of Railway officials with first hand

investment decisions information on railway sector.

The importance of the study is to develop research that studies the separation of

infrastructure charges with the alternative of continuing to charge customers based on

a package of services that includes infrastructure access, rolling stock rental and


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Based on the foundation of these objectives, the research result can provide

information to suggest whether implementing a partly vertical separation of

infrastructure charging price in Mongolian railway is a viable economic alternative.

1.3 Research Contribution.

Identification of research objectives -> Collection and review of relevant

literature -> Development of research model -> Data analysis ->Conclusion and

Suggestions.


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CHAPTER TWO

LITERATURE REVIEW

2.1 Railway Reform and Vertical Separation.

At the end of the 1990s, Railroads were confronted in Europe with the

continuous decline of their modal share for passenger transport as well as goods

transport despite high levels of subsidies from central governments that could reach

close to 50% of the operational costs (Nash & Toner, 1998). It means railroad

h f b h f d f i h d li i d i h h


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government policy, investment strategy, or management structure that seeks to

improve railway performance. Railways are complex institutions with multiple

measures for performance- costs, transport charges, service levels, and investment

needs, among other factors. In the past, most interested parties sought industry

improvements that would reduce government subsidies, introduce competition,

improve capacity and reliability, and increase responsiveness to user needs to expand

the client base (World Bank, 2011).

Every country has their special circumstances. In addition there is a question

what reform of the railway institution is likely to increase the efficiency of railways?

Facing the different types of reforms chosen, it appears that a key objective of

government was to search for a better effectiveness of railway operator.

A railway can be divided into one or more entities that own and manage railway

infrastructure and one or more entities that operate train operating companies offering


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railways. Consequently, separating rail freight operations from infrastructure led to a a

20-40% loss of technical efficiency and an additional 70% loss of operational

efficiency if there were more than one rail transport operator.

Research in the European Union: In the European Union, research on the

advantages and disadvantages of vertical separation is also not convincing either way.

This is partly because few countries had until recently carried out vertical separation.

Also there are difficulties with obtaining comparable data, with measuring the extent

of reform and with isolating the impact of vertical separation from that of other

factors. Rivera-Trujillo (2004) analyzed European data and found that competition

increases efficiency but that vertical separation reduces it. However, if vertical

separation is necessary for introducing competition, he concluded that its overall

effect may be to increase efficiency. Merkert, Smith, and Nash (2010) found that

vertical separation did not have significant effect on technical efficiency although it


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2.2 Infrastructure Separation for Mongolian Railway.

Mongolian transport demand is growing dramatically last years as a result of

growing mining industry investment. UBR is only one Railway Company until 2007.

Lack of investment for UBR is one of main reasons to declining railway performance.

The 1949 restricted agreement between Soviet Union and Republic of Mongolia for

UBR is still valid until today in spite of intensive effort of Mongolia to renew it over

last 10 years. This old agreement influence badly UBR performance and prevents

future develop.

Table 2-2

UBR Investment from Mongolia

Mongolian investment to UBR 1986-2009

Investment sources

Implemented

year Amount


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numbers because of renting process seems temporary. They need long term

cooperation improvement due to difficulty for entry in the railway sector. Mongolian

Government is trying to provide them new circumstance which partly vertical

separation. Mongolian Government chose the potential modern way which encourages

private companies and provides them the opportunity instead of direct investment in

UBR rolling stocks. That partly vertical separation is the way to use fully

infrastructure of UBR because professionals told its capacity is around 20 million

tones production a year. The 2011 actual situation of shipped product of UBR has

never been in excess 15million tone each year because low demand and lack of rolling

stocks except recent years increasing demand. If private company start to operate

their transport using their rolling stocks as an operator company on the infrastructure

of UBR they should pay infrastructure using cost to UBR. In that case we have

questions which pricing is possible for them? How to calculate UBR infrastructure


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UBR ages of railway cars

Age Number Percentage Over aged percent

Over 31 years 704 26.6 61.7From 21 to 30 years 928 35.1

From 11 to 20 years 829 31.3

Until 10 184 7.0

Total 2645 100 61.7

Source: UBR statement (2009)

2.3 Railway Infrastructure Charging Price Related Literatures.

Estimating cost functions for railway organizations has a long history and can be

found as early as the 1960s (Borts, 1960). The focus of the early research was to

check for inefficiencies in the U.S. railroad industry and to regulate monopoly prices

in the presence of economies of scale (Keeler, 1974).

However, each country appears to treat wear and tear differently and there are

different definitions and ways of accounting for operating, maintenance and renewal


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Marginal Cost: The marginal cost pricing model recovers internal and some

external costs directly related to train operations. Since this method provides a direct

connection between usage and costs, it is ideal for managing demand and helps to

improve overall transport system efficiency. The main drawbacks are that it is difficult

to estimate MC, and it provides a low internal cost recovery around 10% of total

infrastructure costs (European Commission, 2003; Pittman, 2003). MC is applied in

Sweden, Switzerland, and Denmark (Calvo, Ona, & Nash, 2007). Recent European

studies have a different perspective as they are looking at the cost structure in

vertically separated rail infrastructure organizations to derive short run marginal costs.

Some studies have grown out of a sequence of research projects on transport

infrastructure pricing funded by the European Commission, such as Pricing European

Transport Systems (PETS) (Nash & Sansom, 2001), UNIfication of accounts and

marginal costs for Transport Efficiency (UNITE) (Nash, 2003)and Generalisation of


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comparable use of the infrastructure. The economic principles behind an appropriate

access regime are well established. Access charges should reflect the marginal cost

that each user imposes on the infrastructure provider. To these marginal costs should

be added the external costs (pollution, accidents, congestion, etc) that each user

generates. This is social marginal cost pricing and, if implemented correctly, will

result in the most efficient use of the rail infrastructure (Tsionas, Baltas, & Chionis,

2010).

Full Cost: The objective of a full cost pricing model is total cost recovery.

Therefore, charges are fixed above MC using Ramsey prices (RP), increasing

variable charges above MC for services that are less price sensitive), attending

operators willingness to pay, and/or using a two-part tariff (comprising fixed and

variable charges). FC methods do not actually pay the full infrastructure costs, since

these costs are so high that they would be impossible for operators to pay. In European


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Figure 2-1. Railway infrastructure full cost

Source: Adopted from World Bank (2011)

These infrastructure costs have a component that is essentially fixed or invariant

with the level of infrastructure usage and a component that is variable with traffic


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railway infrastructure networks financial sustainability depends critically on high

traffic volumes. Good railway network economics requires high infrastructure

utilization-the higher the utilization, the better the infrastructure economics. This is

true whether the infrastructure network is part of a vertically integrated railway, or

provided by a separate rail infrastructure authority or company. Vertical separation of

train operations from railway infrastructure is insufficient to improve railway financial

sustainability, although it may facilitate other policies that help. However, a vertically

separated track authority or company will face much higher fixed costs across its total

business than a vertically integrated railway company (World Bank, 2011).

The infrastructure cost curve is largely fixed in relation to traffic volume, but can

be shifted downwards by management actions that improve efficiency in infrastructure

provision and maintenance. A company exhibits economies of scale if its long run

average cost curve slopes downwards as the output of the company increases.


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revenue for the railway network. Will potential economic benefits from competition in

services outweigh the dilution of economic benefits from Ramsey price differentiation

and the transaction costs of separation? This remains to be seen (World Bank, 2011).

At the domestic level, the decision as to which pricing system is applied has

depended on several factors: the government budget allocated to the railroad, degree

of network congestion, and importance of reducing transportations external costs as a

policy objective (Calvo et al., 2007).

2.4 Alternative Infrastructure Charging Price for Mongolian Railway.

Infrastructure charging price covers at least MC or in maximum case FC of

Infrastructure. Every country chooses their charging price depend on affordable level

of Operation Companys payment. This level exists in interval of MC and FC we can

see it from the Table 2-4.


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CountryPricing

PrincipleMain Variables Considered Country

Netherlands MC Poland FC Infrastructure, railway service, rolling stock, operation

Portugal MC+ Infrastructure, railway service, operation

Romania FC Railway service

Slovakia FC Infrastructure, railway service

Slovenia FC Infrastructure, rolling stock, operation

Spain MC+ Infrastructure, rolling stock, traffic, time

Sweden MC+ Railway service, rolling stockSwitzerland MC+ Railway service, time

United

Kingdom MC/MC+ Railway service, rolling stock, operation, traffic, time

Note: MC = marginal cost; FC = full cost; = dimensionless.

Source: Macrio, Teixeira, and Rothengatter (2010)

Any countrys defining affordable pricing process start from defining MC or FC.

After that they use approaching method which increase MC charging price or decrease

FC charging price. We did not prefer FC cost charging price in starting point because


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Table 2-5

Main Track/km Cost of Infrastructure Maintenance and Renewal

2006 2007 2008 2009 2010

Annual Infrastructure maintenance and renewal

costs bil.tug

42.96 35.66 46.68 41.58 52.97

UBR's Rail line length km 1810 1810 1810 1810 1810

Annual Infrastructure maintenance and renewal

costs tous.euro per track/km. 14.56 12.09 15.82 14.09 17.95

Source: UBR annual statement (2011)


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2.5 Influencing Variables of the result.

This research will more consider about revenue change and shipped production

change influence of partly vertical separation of UBR using infrastructure charging

price for private operation companies.

The GOM aim is to provide enough transport to meet market demand and

decrease subsidies for UBR. We will use liner programming with the aim of seeing

influence of using an alternative pricing structure that relies on an infrastructure

charging price. The following variables will considered.

Productions.

In real condition Mongolian railway is shipping 8 main activities as follows:

Domestic coal - From coal mining to main power station in city area. UBR is

obligated to this transport.

Export coal From coal mining to boarder of China


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UBR rolling stock capacity Actually in real condition railway production

constrained by UBR rolling stock capacity it was from 13 to 15 million tons per year

2005 to 2010.

Private company rolling stocks capacity We will assume it which transportation

demand excess UBR rolling stock constraint it would be shipped by private operator

companies rolling stocks. It depends on transportation demand.


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CHAPTER THREE

RESEARCH DESIGN AND METHODOLOGY

3.1 Introduction.

The purpose of this research is to show a comparative analysis of UBR

performance under two conditions:

(1) Partly vertical separation of UBR using infrastructure charging price for

private operation companies

(2) Without separation of the infrastructure charge.

Linear programming is useful to formulate our problem then we can see UBR


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3.2 Mathematical Development of the Conceptual Framework.

- To define all constraint for

linear programming

- To calculate Infrastructure

charging price

LP model specification for

UBR net revenue without

Infrastructure charge

LP model specification for

UBR net revenue with

Infrastructure charge

Comparison and

analyze


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1. Suppose we maximize the total net revenue of the UBR without infrastructure

charging price in 2014. So that means:

NR = TR TC, where,

TR is total revenue. And it is composed of the revenue generated by 5

services:

TRcd= total revenue from shipping domestic coal.

TRce = total revenue from shipping export coal.

TRir= total revenue from shipping iron ore

TRcu = total revenue from shipping copper concentration

TRoi = total revenue from shipping oil product (from Russia)

TRim = total revenue from shipping imported product (from China)

TRts = total revenue from transshipment (Russia to China or China to Russia)

TRpg = total revenue from passenger service.


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3. those associated UBR rolling stock cost

4. those associated operation and financial department cost

Product specific costs:

These costs include the type and capacity of the rolling stock, any price premium

or discount for the type of usage of the infrastructure. For example, passenger traffic

commands a premium because it must be scheduled first for passenger convenience.

Transit shipping also commands a premium because it is given priority (and

commands a higher freight charge). Rolling stock, of course, differs based on the type

of product being shipped. So we can represent product specific costs for the ith

service as Ki such that:

Ki = (Ri + ki ) Li * Wi where

Ri is the cost of UBR rolling stock cost per tn.km for the i th service

ki is the surcharge for infrastructure usage and scheduling for the ith service and


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The alternative cost of shipping product i by the next best transport mode. (i.e.,

by truck/ bus for passengers)

Demands for products which includes:

Demand for Mongolian coal in China

Demand for coal in Mongolia (powerplant requirements)

Demand for iron ore in China

Demand for copper concentration in China

Demand for transshipment services (Shipments between China and Russia per

period)

Demand for passenger traffic

Demand for oil product in Mongolia (from Russia)

Demand for construction and food production in Mongolia (from China)

After this calculation we can find max net revenue of UBR and optimal


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- New Operator companies product. It would transport excess product of UBR

transported (UBR obligated to transport passenger and domestic coal for those

transport no excess). Formulate is following.

wce =export coal by other operator company .

wir= iron ore by other operator company

wcu = copper concentration by other operator company

woi = oil product (from Russia) by other operator company

wim = imported product (from China) by other operator company

Wce =export coal by UBR

Wir= iron ore by UBR

Wcu = copper concentration by UBR

Woi = oil product (from Russia) by UBR

Wim = imported product (from China) by UBR Dece =export coal.


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Subject to the constraints: (are same as before)

Railway network capacity (Number of cars that can travel on the network in a

period)

The maximum number of rail cars available in period t for shipping service i

The tariff rate schedule Pi

The maximum quantity of product for shipping by rail in period t

The alternative cost of shipping product i by the next best transport mode. (i.e.,

by truck/ bus for passengers)

Demands for products which includes:

Demand for Mongolian coal in China

Demand for coal in Mongolia (power plant requirements)

Demand for iron ore in China

Demand for copper concentration in China


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3.4 Data Collection.

The collection of data for this study was gathered from various sources and

includes different kinds of data. The data collected includes information and

characteristics from journal articles for railway transportation sector, annual report

from UBR, government publications and web pages from governments and agencies

related to railway or statistics and field notes also we run railway economist audit with

the help of the economist Delgersaikhan Jargalsaikhan who works for Authority of

Mongolian Railway.


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CHAPTER FOUR

DATA ANALYSIS

This chapter provides the data analysis which includes forecasting and,

mathematical methods of production mix using purely LP method. The presentation of

the forecasting is used to define the future production demand and some of fixed costs

based on the result of analyze of Mongolian railway performance. The Linear

Programming model presented here finds the optimal production mixes.

4.1 Introduction.

This chapter provides the data analysis which includes forecasting and Linear


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infrastructure separation price, it could be possible to implement this system starting

2014. Also we will investigate net revenue of UBR for both of the above two cases.

We collected Mongolian railway production transport data from UBR. These data

cover the 6 years from 2005 to 2010 (Freight data in appendix 1).

Freight data production is subcategorized into more than 70 types which we

sought to simplify for further analysis. We subtracted some of low weight products

compared with the total and reduced the subcategories to a total of 8 which accounts

for around 75 percent of total freight handled (tons) and around 80 percent of all

freight transport services (in ton/km) (table 4.1). This is important to define LP model.

(If we have a lot of types of product we have to design cumbersome model but these

low weighted products will not significantly influence our result.) Finally we will

assume that UBR have the following 8 categories freight transportation products for

the LP model.


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Iron ore: Mongolian iron ore companies annual production capacity is 8 mil.ton

but their mining output is heavily depending on railway capacity. Iron ore is one of the

most profitable products to transport by railway. Six years average it accounts for 7.3

percent of production (measured by ton) and 11.9 percent of transportation (measured

by tn.km).

Table 4-1(a)

Freight production data of UBR

Freight production

Report for 2005 Report for 2006 Repert for 2007tous.tn mil.tn.km tous.tn mil.tn.km tous.tn mil.tn.km

1. Domestic coal 4963.9 1138.7 4804.0 1156.1 4920.3 1198.4

2. Exporting coal 154.0 61.8 244.2 97.9 154.0 61.8

3. Iron ore 238.3 214.5 1000.5 900.5 238.3 214.5

4. Copper concentration 613.9 686.3 585.8 654.9 613.9 686.3

5. Oil product 493.5 202.4 538.0 220.6 493.5 202.4


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Table 4-1(b)

Freight production data of UBR by Percentage

Freight2005 2006 2007

% ofton

% ofton.km

% ofton

% ofton.km

% ofton

% ofton.km

1. Domestic coal 35.22 13.87 34.47 14.79 35.04 14.12

2. Exporting coal 0.95 0.65 1.61 1.15 0.96 0.63

3. Iron ore 1.69 2.61 7.18 11.52 1.70 2.53

4. Copper concentration 4.36 8.36 4.20 8.38 4.37 8.08

5. Oil product 3.50 2.47 3.86 2.82 3.51 2.38

6. Fluorspar 1.41 1.88 2.28 2.88 1.41 1.827. Imported production (fromChina) 3.38 3.74 4.89 5.66 3.40 3.62

8. Transshipment 24.43 46.58 16.47 32.61 24.52 45.03

Freight2008 2009 2010 Average

% ofton

% ofton.km

% ofton

% ofton.km

% ofton

% ofton.km

% ofton

% ofton.km

1. Domestic coal 35.57 15.22 36.11 16.02 34.71 14.73 35.19 14.79

2. Exporting coal 1.53 1.08 1.95 1.36 0.04 0.03 1.17 0.82

3. Iron ore 6.85 10.88 9.90 15.59 16.65 28.35 7.33 11.91

4. Copperconcentration

4.01 7.92 4.09 8.00 3.40 6.24 4.07 7.83


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(measured by tn) and 2.3 percent of transportation (measured by tn.km) in six years

average.

Imports (from China): China is the biggest trading partner with Mongolia.

Imported products from China mainly divided into four categories which are building

materials, equipments, food and container. It account for 3.8 percent of production

(measured by ton) and 4.2 percent of transportation (measured by tn.km) in six years

average.

Transshipment: Transshipment is China to Russia and Russia to China

production transportation. Demand is determined from three countries agreement also

volume of production depend on UBR capacity. It account for 18.7 percent of

production (measured by tn) and 35.7 percent of transportation (measured by tn.km) in

six years average.

Passenger service of UBR is obligated by government also it is declining slightly


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4.2.2 UBR Cost Statement Data

Also we collected information about the railway transport cost data. We received

UBR cost data from Authority of Mongolian Railway. These Data involved with in 5

years which from 2006 to 2010 (appendix 2).

We also simplified UBR cost statement data for future LP formulation and

reduced to 6 main categories from 7 main categories and over 360 minor categories.

We merged Locomotive and pulling cost and Rolling stock and Loc maintenance

together then named Rolling stock cost. Central operation office cost and Cost

of finance department costs are not nearly related with production volume but we

didnt merge them together in order to generate more precise forecasts (see Table 4-3).

Table 4-3

Refined Cost Statement Data of UBR


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Table 4-4

Cost of per ton km transport service.

Types of costs Demonstrations2006 2007 2008 2009 2010 Average

tug/t

n/km

tug/tn/

km

tug/tn/

km

tug/tn/

km

tug/tn/

km

tug/tn/k

m

1. Operation of

freight transport cost

divided by all

freight 0.4 0.5 0.6 0.6 0.6 0.6

2. Infrastructure

renewal and service

divided by all

product 4.7 3.6 5.8 4.5 4.6 4.7=Pi

3. Rolling stock cost

divided by all

product 10.0 9.3 14.5 11.6 14.2 11.9

4. Operation of

passenger transport

cost

divided by all

pass 6.4 5.7 7.5 10.1 9.2 7.8

Then we calculated fixed costs related to considered work each year.

Table 4-5

Cost of per ton km Transport Services.

2006 2007 2008 2009 2010


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Table 4-6

Price List of All Production

Products Unit Price

1. Domestic coal Tug/ton/km 21.0

2. Exporting coal Tug/ton/km 33.0

3. Iron ore Tug/ton/km 29.0

4. Copper concentration Tug/ton/km 33.05. Oil product Tug/ton/km 27.0

6. Fluorspar Tug/ton/km 26.0

7. Imported production (from China) Tug/ton/km 26.6

8. Transshipment Tug/ton/km 33.0

9. Passenger Tug/passenger/km 23.0

To determine price ton/km method is following:

To choose shipping price from pricing table depend on product type and shipping

distance then use follow formula.

Price (ton /km) = Shipping price/ Distance/Per car cargo mass


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Table 4-7

Price Calculation of Imported Product from China

Imported products

from China

Percent of

total Tariff Weighted price

Building products 17.9 % 25 tug/ton/km 4.5

Food 22.7 % 14 tug/ton/km 3.2

Equipment 24.3 % 20 tug/ton/km 4.9

Other goods (container) 35.0 % 40 tug/ton/km 14.0

Price 26.5 tug/ton/km

International agreement freight transportation tariff is valid to transit shipment

but there were 14 type of product. Defining a passenger transport price is also burden

of calculation. In this case we chose average price as a representing price of

transshipment and passenger from UBR statement report (Railway, 2011).

4.3 Forecasting

We determine which of our time series data should be forecast. Therefore we


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Table 4-8

Production Data for Forecasting

Year

Domestic

coal

Exporting

coal

Copper

concentration

Oil

product Fluorspar

Imported

production

Passenger

number

Passenger

travel

distance

tous.tn.km tous.tn.km tous.tn.km tous.tn.km tous.tn.km tous.tn.km tous km

2005 4963.9 154.0 613.9 493.5 198.2 476.8

2006 4804.0 244.2 585.8 538.0 257.6 681.5

2007 4920.3 154.0 613.9 493.5 198.2 476.8 4482.4 313.8

2008 5195.3 244.2 585.8 538.0 257.6 681.5 4358.8 321.3

2009 5180.0 279.0 586.3 446.6 175.1 484.5 3118.3 323.4

2010 5834.7 8.2 571.3 644.0 297.8 483.3 3516.3 347.0

2011 3832.1 365.3

Passenger transport data is special (see Table 4-8). Passenger numbers are

declining smoothly but the average distance per passenger is increasing. Hence, we

forecasted separately passenger transport data in order to adapt this trend in the future.

We see cost data for Central operation office cost and cost of the Finance


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((In order to investigate MAD we suppose that in the past, UBR had forecasted

transport services for each year to be the same as the actual transport services from

the previous year. This is sometimes called a nave model.)

There are other measures of the accuracy of historical errors in forecasting that

are sometimes used besides the MAD such as Mean squared error (MSE) and mean

absolute percent error (MAPE) but these two measurements functions similar to

MAD.

A time series data is based on e sequence of evenly spaced data points (in our

case we used yearly data). Forecasting time series data implies that future values are

predicted only from past values of that variable and that other variables, no matter

how potentially valuable, are ignored.

4.3.1 Moving Average

Moving averages are useful if we can assume that market demands will stay


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Forecasting of moving average results on Table 4-9.

Table 4-9

Next Year Forecasting Results of MA Method (2011)

Names of Forecasting variables Unit Forecasted amount MAD

Central operation office cost bil.tug 6.52 16.32

Cost of Finance department bil.tug 44.72 19.63

1. Domestic coal tous.ton 5507.36 284.68

2. Exporting coal tous.ton 143.63 105.91

4. Copper concentration tous.ton 578.81 14.09

5. Oil product tous.ton 545.34 66.32

6. fluorspar tous.ton 236.47 48.42

7. Imported production (from China) tous.ton 483.88 99.77

Passenger number tous.pass 3674.19 679.81

Passenger travelling distance kilometer 356.11 20.19

4.3.2 Weighted Moving Average


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where

wi= weight ofith observation

We chose two time period weighting and results are on the Table 4-10.

Table 4-10

Next Year Forecasting Results of WMA Method (2011)




1. Domestic coal tous.ton 5,616.46 275.68

2. Exporting coal tous.ton 98.50 111.124. Copper concentration tous.ton 576.31 15.29




Passenger number tous.pass 3,726.82 581.91


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Ft= previous forecast (for time period t)

= smoothing constant (0 1)

Yt= previous periods actual demand

The latest estimate of demand is equal to the old estimate adjusted by a fraction

of the error (last periods actual demand minus the old estimate).

The smoothing constant, , can be changed to give more weight to recent data

when the value is high or more weight to past data when it is low. In our case we

choose two different smoothing constants. For the first, we used =0.5 and for the

second =0.8. Forecasting results are on Table 4-11.

Table 4-11 (a)

Next Year Forecasting Results of ES Method=0.8(2011)



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Table 4-11(b)

Next Year Forecasting Results of ES Method=0.5












4.3.4 Least Squares Method

Another method for forecasting time series with trend is called trend projection.

This technique fits a trend line to a series of historical data points and then projects the


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technique also results in the trend line that minimizes the MSE. But we more focus on

MAD in order to compare all forecasting method. Results on Table 4-12.

Table 4-12

Next Year Forecasting Results of LS Method (2011)









7. Imported production (from

China) tous.ton 511.99 86.03




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Table 4-13

Forecasting Results in 2014

Names of

Forecasting

variables

UnitMAD of

MA

MAD

of

WMA

MAD

of ES

=0.8

MAD

of ES

=0.5

MAD

of LS

LS

method

result in

2014

Central operation

office cost bil.tug 16.32 1.49 0.97 1.23 0.35 12.4

Cost of Financedepartment bil.tug 19.63 18.05 10.82 11.91 8.13 79.4

1. Domestic coal tous.ton 284.68 275.68 207.08 223.51 166.4 6218.8

2. Exporting coal tous.ton 105.91 111.12 91.52 84.71 77.77 81.4

4. Copper

concentration tous.ton 14.09 15.29 15.7 15.54 8.44 548.4

5. Oil product tous.ton 66.32 75.68 63.64 55.79 42 622.7

6. fluorspar tous.ton 48.42 59.29 56.27 47.76 35.83 288.4

7. Imported

production (from

China)

tous.ton 99.77 117.15 122.79 107.03 86.03 481.6

Passenger number tous.pass 679.81 581.91 375.72 373.67 349.71 2575.6

Passenger travelling

distance kilometer 20.19 18.34 11.72 14.29 4.31 411.3


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4.4 Linear Programming

4.4.1 Linear Programming variables and constraints

After data collection and forecasting we found enough data for LP formulations

these data are given on Table 4-10

pi price of each transport service is determined from price data collection

li transported distance of each product is from product data collection

ci cost of each product is from cost data collection

Di demand of each product is from forecasting

Wi volume of each transport service of UBR is going to be determined from LP

analysis.

wi volume of each transport services provided by private companies is going to

be determined by LP analysis.


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We consider main two capacities limitations that in the Mongolian Railway faces

nowadays. Rolling stock and railway line infrastructure capacities are the main

constraints in LP formulation.

This research mainly focuses on increasing rolling stock capacity because

infrastructure capacity is 22 mil.tn in each year compared to 16 mil.tn for rolling stock

in 2010. The reason is that rolling stock age is still declining over the year. If do not

invest in additional UBR rolling stock, it would be only 14 mil.tn year in 2014. If

UBR uses private companies rolling which have already bought by them, it would be

16 mil.tn year in 2014 (reference).

First LP investigated only UBR performance in 2014 without additional

investment for railway rolling stock from government. They use rolling stocks which

already had bought by private companies.


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max(NR){ (p1W1l1 + p2W2l2 + p3W3l3 + p4W4l4 + p5W5l5 + p6W6l6 + p7W7l7 +

p8W8l8 + p9W9l9) - (c1W1l1 + c2W2l2 + c3W3l3 + c4W4l4 + c5W5l5 + c6W6l6 + c7W7l7 +

c8W8l8 + c9W9l9)

where

variables are given on Table 4-14

constraints are given on Table 4-15

About unit explanation:

piciunits is given tug/ton/km

Wi - units is given tous.ton

li units is given km

Revenue = pi x Wi x li =(tug/ton/km) x tou.tn x km= tous.tug

Cost = ci x Wi x li =(tug/ton/km) x tou.tn x km= tous.tug


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copper concentration, oil product, imported product from China and passenger

transport service from government. Also we supposed those obligations are

implementing now if we dont choose any better way to increase railway sector it will

continuous in 2014. Another assumption is total capacity is 16mil.ton in main line,

actual UBR rolling stock capacity 14mil.tn a year and if they will use private company

rolling stock which has already bought by them capacity would be 16 mil.tn each year

in 2014.

Forecasted production unit tous.ton and our main objective is to find out optimal

transportation service mix measured by ton. Capacity data unit is also given us by ton.

We found determine the price and cost data measured by tug/ton/km it is suitable for

LP model.

In the first case Result as following: In the first case Result as following:


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government subsidy in Mongolian railway. LP showed us profitable result but we will

compare it next section result.

NetRevenue = [max{ 8=1 ]

8=1 =

= 99,708,530 12,381,418 79,352,644 = 7,974,468 tous.tug


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4.4.3 Linear Programming in Second Case

Second case net revenue determining equation as following:

Netrevenue = [ max{ 8=1

8=1 ] +

5=1

where

[max{ 8=1 ]

8=1 - LP formulation to find first case optimal production

mix for UBR

O Forecasted operation cost of UBR

F- Forecasted Financial department cost

5=1 - Additional revenue from private operator companies for UBR (we

will calculate it later)

If we extract LP formulation it looks like following:

max(NR){ (p1W1l1 + p2W2l2 + p3W3l3 + p4W4l4 + p5W5l5 + p6W6l6 + p7W7l7 +

p8W8l8 + p9W9l9) - (c1W1l1 + c2W2l2 + c3W3l3 + c4W4l4 + c5W5l5 + c6W6l6 + c7W7l7 +


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Table 4-17

Second Case LP Constraints

Products constraint Demand

tous.ton

Explanation

1. Domestic coal W1


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In order to complete whole equation we have to determine private companies

freight service product mix as followings:

First step: To find out demand of private operation companies on Table 4-18

Table 4-18

Second Case LP Production mix of UBR and Demand of Private Operators

Products UBR transport

services

Demand Di Demand for

private

operators

di = Di-Wi

W1 tous.tn tous.tn tous.tn

1. Domestic coal W1 0.0 6218.9 6218.9

2. Exporting coal W2 0.0 81.4 81.4

3. Iron ore W3 7656.0 8000.0 344.0

4. Copperconcentration

W4 548.4 548.4 0.0

5. Oil product W5 622.7 622.7 0.0

6. Fluorspar W6 0.0 288.4 288.4

7. Imported production W7 0.0 481.6 481.6

8. Transshipment W8 2597.6 2597.6 0.0


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constraints are given on Table 4-19

About unit explanation:

piciunits is given tug/ton/km

Wi - units is given tous.ton

li units is given km

Revenue = pi x Wi x li =(tug/ton/km) x tou.tn x km= tous.tug

Cost = ci x Wi x li =(tug/ton/km) x tou.tn x km= tous.tug

Table 4-19

Private Operator companies LP Constraints for Production Mix

Products Constraint Demand

tous.tn

Explanation

1. Domestic coal W1 => 6219 Private companies obligated for it

2. Exporting coal W2


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Products UBR transport (tous.tn)

3. Iron ore W3 344

6. Fluorspar W6 2887. Imported production W7 482

Private company freight service product mix determine on Table 4-20. So we can

calculate UBR additional revenue.

= 5=1 = 11,342,352 tou. tug

Final result of second case LP ar

Netrevenue = [max{ 8=1

8=1 ] +

5=1

= 142,877,000 12,381,418 79,352,644 + 11,342,352 = 62,485,290 tous.tug

In the next chapter we will explain the results of those two LP formulated UBR

net revenue.


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CHAPTER FIVE

CONCLUSIONS

5.1 Conclusions and discussion.

This paper set out to examine influence of partly vertical separation in UBR using

infrastructure charging price for private operator transportation companies. We use

analysis which includes forecasting and, mathematical methods of production mix

using purely LP method to compare the net revenue of UBR with infrastructure


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Vertical separation can increase the railway transport capacity without high

government cost. More capacity will bring chance to transport more profitable freight

for UBR.

Policy makers have to consider about important issue which private companies

willingness to enter the railway transportation sector. For example in second case

result we assumed UBR only obligated to passenger transport and oil product

transport and private companies willing to be obligated to domestic coal and import

product.

Final targets of policy makers are to support private investment for rolling stocks

and provide profitable transport market both UBR and private companies. In order to

achieve those goals they have to investigate Price sensitivity issue and Government

subsidy issue. We will discuss about it late of this chapter.

Research question 2: Can vertical separation achieve producing more transport


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more risk. We can see profit level from 1tn service (1 passenger in passenger service)

on Table 5-1.

Table 5-1

Profit level of railway service (Revenue-Variable Cost)

Products Profit level from1tn Demand in 2014

1. Domestic coal 995.4tug 6218.9 tous.tn

2. Exporting coal 6,916.3 tug 81.4 tous.tn

3. Iron ore 11,143.9 tug 8000.0 tous.tn

4. Copper concentration 17,973.4 tug 548.4 tous.tn

5. Oil product 4,172.6 tug 622.7 tous.tn

6. fluorspar 7,017.2 tug 288.4 tous.tn7. Imported production (from China) 6,277.3 tug 481.6 tous.tn

8. Transshipment 17,831.3 tug 2597.6 tous.tn

9. Passenger -470.1tug 2575.6 tous.pass


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0

20

40

60

80

100

120

140

160

0 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000

Revenue

bl.tug

Domestic cola obligation tous.tn

Profit level

Revenue

Cost

4800

tous.tn


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5.2 Limitation of Study and Future Work

Obviously we cannot contain in this study special technical feasibility and other

diversified matters. We just calculated revenue and cost of simplified model which

include 8 categories freight service. We did not examine very carefully considered

every product, transported by Mongolia railway, service cost and revenue. In this

study only used UBR data in the future if private company data available this sort of

study can explain wider view.

This research examined the optimal freight transport service mix from government

view which railway transportation service in excess demand situation without direct

competition other transport mode. LP model is suitable for production mix industry

but in railway transportation case we can use it in the excess demand. In the practice

this research main study to determine fair production mix for multiple railway

operator companies


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76

APPENDICES

Appendix 1:

Direction

Report for 2005 Report for 2006 Repert for 2007 Report for 2008 Repert for 2009 Repert for 2010

tous.

tn

tous.tn

.km km

tous.t

n

tous.t

n.km km tous.tn

tous.t

n.km km

tous.t

n

tous.t

n.km km

tous.t

n

tous.t

n.km km

tous.t

n

tous.t

n.km km

Transit 3444 3823 1110 2296 2548 1110 3444 3823 1110 2296 2548 1110 2516 2793 1110 2315 2569 1110

from Russia to China 2915 3236 1110 1919 2129 1110 2915 3236 1110 1919 2129 1110 1968 2185 1110 1797 1995 1110

Crude oil 58 65 1110 0 0 58 65 1110 0 0 0 0 0 14 15 1110

Mineral oil 113 125 1110 101 112 1109 113 125 1110 101 112 1109 227 252 1110 185 205 1110

Timber 2552 2833 1110 1664 1847 1110 2552 2833 1110 1664 1847 1110 1522 1690 1110 1400 1554 1110

Metal ore 28 31 1110 0 0 1110

Non-ferrous

metals 11 13 1110 0 0 1110Chemicalcargoes 179 199 1110 136 151 1110 179 199 1110 136 151 1110 108 120 1110 161 179 1110

Rail 37 41 1110 15 17 1110

Other 13 15 1110 18 20 1110 13 15 1110 18 20 1110 35 39 1110 22 25 1110

from China to Russia 529 587 1110 377 419 1110 529 587 1110 377 419 1110 548 609 1110 518 575 1110

PEC 172 191 1110 64 71 1110 172 191 1110 64 71 1110 105 117 1110 130 145 1110

Coke 85 95 1110 68 75 1110 85 95 1110 68 75 1110 48 53 1110 109 121 1110

Building

products shipments 21 23 1110 14 16 1110 21 23 1110 14 16 1110 12 13 1110 16 18 1110Chemical

cargoes 26 29 1110 21 23 1110 26 29 1110 21 23 1110 11 12 1110 27 30 1110

Equipment 106 118 1110 168 187 1110 106 118 1110 168 187 1110 145 161 1110 97 107 1110

Other 118 131 1110 43 47 1110 118 131 1110 43 47 1110 228 253 1110 139 154 1110


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77

Direction

Report for 2005 Report for 2006 Repert for 2007 Report for 2008 Report for 2009 Report for 2010

tous.tn

tous.tn.km km

tous.tn

tous.tn.km km

tous.tn

tous.tn.km km

tous.tn

tous.tn.km km

tous.tn

tous.tn.km km

tous.tn

tous.tn.km km

From MGL 1776 1575 886 2565 2188 853 1776 1575 886 2565 2188 853 2897 2395 827 3800 3,382 890

CONTROL 2565 2132 831 2,955 2,352 796

to Russia 368 304 825 376 300 800 368 304 825 376 300 800 213 171 802 255 204 799

347 242 697 215 145 674

coal 20 8 410 20 8 410 20 8 410 20 8 410 410

fluorspar 198 155 780 258 201 780 198 155 780 258 201 780 175 137 780 215 168 780

Skoroportyashiysy

a goods9 6 730 7 5 730 9 6 730 7 5 730 8 6 730 6 700

Other 142 135 950 90 86 950 142 135 950 90 86 950 30 28 950 32 30 950

to China 1408 1271 903 2190 1888 862 1408 1271 903 2189 1888 862 2,685 2,225 829 3545 3,178 8972218 1891 852 2,740 2,207 806

coal 134 54 400 224 90 400 134 54 400 224 90 400 279 112 400 100 40 400

iron ore 238 214 900 1001 900 900 238 214 900 1001 900 900 1,420 1278 900 2400 2280 950

The copper

concentrate614 686 1118 586 655 1118 614 686 1118 586 655 1118 586 656 1118 594 664 1,118

fluorspar - 400 60 655 400 400 60 24 400 102 41 400 115 46 400

zinc ore 91 22 240 109 655 240 91 22 240 109 26 240 111 27 240 148 36 240

crude 35 10 280 11 655 280 35 10 280 11 3 280 45 13 280 45 13 280

timber 124 130 1050 69 655 1050 124 130 1050 69 73 1050 43 45 1050 43 45 1,050

Other goods 172 155 900 130 655 900 172 155 900 130 117 900 99 55 550 100 55 550


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78

Appendix 2:

Category2006 2007 2008 2009 2010

tous.tug tous.tug tous.tug tous.tug tous.tug1. Operation of freight transport cost 3,468,318 3,829,532 5,286,427 5,259,461 6,679,763

2. Infrastructure renewal and service 42,956,329 35,656,912 46,675,313 41,575,997 52,968,632

3. Locomotive and pulling cost 70,709,256 69,772,920 104,927,882 83,711,069 134,875,750

4. Passenger 8,270,778 7,952,625 10,547,675 10,181,094 11,167,526

5. Rolling stock and Loc maintenance 20,226,770 22,692,937 35,055,606 23,415,413 27,944,629

6.Central operation office cost 3,836,638 4,605,369 7,062,929 6,635,906 8,799,260

7.Cost of Finance department 27,273,239 22,412,833 12,888,662 50,076,289 55,789,330

TOTAL 176,741,328 166,923,128 222,444,494 220,855,229 298,224,890


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Documents

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