Ijciet 10 02_044

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International Journal of Civil Engineering and Technology (IJCIET)
Volume 10, Issue 02, February 2019, pp. 424-445, Article ID: IJCIET_10_02_044
Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=10&IType=02
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication Scopus Indexed
ESTIMATION AND ANALYSIS OF DEMAND
STRUCTURE FOR THE RAIL TRANSPORT
SECTOR IN IRAQ FOR THE PERIOD (1999-2016)
*Prof. Manihal Mustafa Abdel Hamid
Department of Economics, Faculty of Management and Economics,
University of Baghdad, Iraq.
*Corresponding author
ABSTRACT
This study concerned the study and analysis of the demand function of the railway
transport sector in Iraq and the factors influencing it. These factors influenced the
numbers of passengers and the quantities of goods transported by them. It was found
by estimating the demand analysis that the railway transport sector in Iraq suffers
from a decline in the level of services provided passenger and cargo transport, due to
the deterioration of infrastructure lines and units moving and lack of comfort, speed
and safety in which factors, led the sector important loss in support of the national
economy , And the reluctance of most passengers and owners of goods for the
acquisition of this service in the mobility and transition to other modes of transport
and then lost to compete with other means of transport. In the estimation of the
demand function, the variables that should increase the number of passengers and the
quantities of goods have a negative effect .Despite the increase in the size of the
population, this increase did not increase the number of passengers and the quantities
of goods. This indicates that the population does not want to purchase the railway
mode. The speed of the train is very slow and does not meet the ambition. The reason
for this is the footing and weariness of lines, trains, cars and trucks, as well as the
lack of comfort and safety in that medium, making it a mode of expulsion rather than
attracting individuals
Keywords: The railway sector in Iraq, Demand function for passenger transport,
Demand function for transporting goods.
Cite this Article: Prof. Manihal Mustafa Abdel Hamid, Estimation and Analysis of
Demand Structure for the Rail Transport Sector in Iraq for the Period (1999-2016),
International Journal of Civil Engineering and Technology, 10(2), 2019, pp. 424-445.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=02

Estimation and Analysis of Demand Structure for the Rail Transport Sector in Iraq for the Period
(1999-2016)
1. INTRODUCTION
The railway sector is most important modes of transport in the world as constituting a vital
artery and a strategic actor in development, and is working on linking agricultural and
industrial areas, markets and cities, as well as it represents a cultural dimension in the
economic and social fields, and of interest to the governments of various countries in the
world as a means of transport Safe, clean and low cost for sustainable development. Iraq
suffered from neglect in this sector, especially after a year 2003 Because of the situation in
which the country in general, especially the railways from the destruction of fixed assets and
mobile, and the loss of networks and locomotives lost a large part of the components of
safety and safety, speed, flexibility and comfort and the absence of real plans to develop and
raise the efficiency of rail in all respects. In this research we highlight the structure of
demand for railways and analysis of demand functions in both passenger transport and cargo
transport.
2. RESEARCH PROBLEM
The problem is the low demand for rail transport services, which is the result of inefficient
functioning of railways, stations, locomotives, vehicles and trucks.
2.2. Search Hypothesis
The factors affecting the demand for railway transport services are linked to the inverse
relationship with the required quantities of passengers and quantities of goods.
2.3. Search Objective
The aim of the research is to estimate and analyze the demand functions of the railways in
both the transport of passengers and goods in Iraq and to indicate the factors affecting it.
2.4. Research importance
The importance of research is that the railway sector from the important economic sectors
and knowledge of the demand structure contributes to the drawing up of future investment
plans in its development.
2.5. Structural Research
A search of the first two demands included highlighting the transport sector by rail in Iraq
and the statement of the current reality, while the second was the demand analysis functions,
the demand for transport services, both railway passengers and goods for the duration of
transport (1999-2016).
3. THE FIRST REQUIREMENT: THE THEORETICAL FRAMEWORK
FOR THE DEMAND FUNCTION OF THE RAILWAY TRANSPORT
SECTOR IN IRAQ
The demand function for the railway sector consists of several variables that affect the
quantity required for this sector and have a role in increasing or decreasing the number of
passengers and the quantities of goods according to the development and efficiency of these
variables (General Company of Iraqi Railways, 2016: 4-20).

Prof. Manihal Mustafa Abdel Hamid
3.1. Preparing passengers and quantities of goods
The movement of passengers and quantities of goods of and t such a good indicator of the
efficiency of the railway integration with other indicators, and railways have seen in Iraq,
uneven passengers transported preparation and quantities of goods transported during the
period in Table 1 as shown decline in the size of the demand railway after 2002 this decline
was caused by the events and circumstances experienced by Iraq in 2003 and continued to
decline in passenger numbers and reached its lowest level in 2007 as it reached (4.443)
thousand/ passengers, and increased passenger numbers slightly and reached the highest
number of passengers in 2016 reached 369 thousand/ Miss Laver , But the quantities of goods
also saw a significant decline after 2002 And fell to the lowest level in 2016, reaching (56)
thousand/ ton. The number of passengers who use the railway in mobility did not meet the
ambition during the period of study and this confirms the inefficiency of this medium and led
to a decline in the volume of demand for them.
3.2. Passenger and Cargo Passage
The definition of transport of passengers and goods constitute an important factor in the
traveler and the owner of the goods to choose the means of transport acquired by type,
Valoasith which is less expensive that attract the traveler and the owner of the goods in
particular, and the table (1) and (2) high tariff transport railroad rail shows from year to year
depending on The level of prices and the rate of inflation, but after 2007 there was stability in
prices and tariffs for the transport of passengers and goods (Ministry of Planning, 4: 2016).
3.3. Train speed
The speed of the train is an important indicator that affects the volume of demand for the
passenger transport service. Table (1) shows the speed of the passenger train and ranges
between (40-50) km / h. Table 2 shows the speed of the freight train, This speed is very low,
due to factors related to the nature of the lines and vehicles used and the nature of the land in
which the trains have led to a decrease in the rate of speed and caused a decrease in the
number of passengers on that medium compared to other means of high speed.
3.4. Population
The population is one of the factors influencing the volume of demand for rail service and the
existence of a reciprocal relationship between them. The population affects and is influenced
by the different modes of transport whether this effect is negative or positive. Table (1) shows
the size of the population in Iraq which is continuously rising from year to year, Despite the
increase in the number of people, the number of passengers did not rise, which shows that
individuals do not prefer the acquisition of railways in the movement, as the increase in the
size of the population affect and work to increase the goods transferred, but did not increase
the amount of goods transported on the railways.
3.5. Number of mobile units
The mobile units in the Iraqi Railway are determined by locomotives, passenger cars and
cargo trucks, and have an influential role in the preparation of passengers and quantities of
goods, either by increasing or decreasing. The higher the number of locomotives, vehicles
and trucks, the more efficient the number of passengers and the quantities of goods
transported on the rail, (1) shows the number of passenger vehicles and table (2) shows the
number of cargo vehicles and despite the increase in the number of vehicles and trucks during
the study period, but it was not the required level of services provided in it and most of these

(1999-2016)
vehicles and trucks suffer from foot and foot As a result, the railways lost their competitive
edge to the more advanced means of transportation (Iraqi Railways Company, 2016: 10) .
Table 1 Passenger numbers, transport tariffs, train speed, number of population and number of
vehicles
The year
Preparing
passengers A
/ traveler
Passenger
Transport Tariff
Dinar
Train speed
How many
Population
Million
Number of
vehicles
1999 1274 319 65 22702 215
2000 1006 534 63 23382 220
2001 1002 712 60 24086 220
2002 1248 906 60 24813 225
2003 345 926 55 25565 225
2004 63 896 55 26340 225
2005 6.009 993 50 27139 238
2006 13.448 1231.7 40 27963 237
2007 4.443 3466.1 40 28810 237
2008 107.3 6898.4 45 29682 282
2009 219.8 9808 50 31895 250
2010 212 11292.4 40 31664 307
2011 178 10905.6 45 32490 307
2012 148 10736.4 50 33338 307
2013 134 1110.4 55 34208 307
2014 146 13472.6 50 35096 325
2015 336 13016.9 45 36005 339
2016 369 10523.8 50 38857 339
Source: Ministry of Transport, General Company of Railways, Financial Section.

Table 2 Quantities of goods, tariff of transport, train speed, number of population and number of
trucks
The year
Quantities of
goods
Thousand /ton
Tariff of goods
transport Dinar
Train speed
How many
Population
Million
Number of
trucks
1999 2589 1118.3 45 22702 8234
2000 2703 1416.2 39 23382 8600
2001 3273 2798.2 40 24086 8790
2002 5227 4340.4 46 24813 9012
2003 1269 4323.5 49 25565 9170
2004 439 5674.7 52 26340 9253
2005 234 5252.1 44 27139 9956
2006 259 5361.7 35 27963 10266
2007 165 6357.5 42 28810 10266
2008 432 9995.3 39 29682 10266
2009 644 12327.3 37 31895 10326
2010 995 11664.8 35 31664 9315
2011 660 15278.7 43 32490 9315
2012 850 16678.8 34 33338 9315
2013 1703 20432.9 35 34208 9315
2014 1067 22879.9 32 35096 10984
2015 134 25678.6 31 36005 11084
2016 56 27400.8 44 38857 11084
Source: Ministry of Transport, General Company of Railways, Financial Section.
4. THE SECOND REQUIREMENT: RESULTS OF THE ESTIMATION
OF THE DEMAND FUNCTION FOR THE RAILWAY SECTOR IN
IRAQ
That the objective of analyzing the function of demand for rail transport in Iraq is to find out
the factors affecting the volume of demand represented by the number of passengers and the
quantity of goods transported through this means of transport and the feasibility statement if
the efficiency of this service is upgraded and developed, this is what we will know through
the relationship demand factors affecting it, to estimate the demand for transportation service
railway in Iraq function includes the demand for passenger and function of the transfer of the
demand for the transport of goods which depend on the idea of economic analysis of the two,
which means the relationship between the crossing order quantity by the number of
passengers and the amount of Ald function Ia and factors affecting this demand depending on
the time series data ( 2016-1999 ).
5. FIRST: RESULTS OF THE ESTIMATION AND ANALYSIS OF THE
DEMAND FUNCTION FOR THE PASSENGER TRANSPORT SERVICE
FOR THE RAILWAY SECTOR
The demand function for passenger transport service takes a period of (2016-1999) follows
the formula of:

(1999-2016)
𝐿𝐿𝐿𝐿𝐿𝐿 = 𝐵𝐵0 + 𝐵𝐵1LNPN + 𝐵𝐵2LNPR + 𝐵𝐵3LNVN + 𝐵𝐵4LNST + 𝑈𝑈𝑖𝑖 (1)
whereas ( LNQ ) Is the dependent variable of the number of passengers (1,000
passengers), and LNPN Population (million), ( LNPR ) Average Passenger Transport Tariff
(1,000 Dinars) , ( LNVN ) Number of vehicles, LNST) The train speed (km) is the
independent variables affecting the required quantity . The statistical program will be used
(EVIEWS9) to extract the results of the demand function for the passenger transport service.
The data will be converted quarterly and using the logarithmic formula. The stability of the
time series will be tested and the integration of the time series will be realized at any point
and then the model will be applied the appropriate.
5.1. Unit root test results
Before estimating the demand function, the time series of the model must be tested and the
degree of complementarity of the variables should be tested to verify the existence of a time
trend and to eliminate it if any, in order to obtain time series Gujarati, 2004.57).
5.1.1. The Results of the Dicky Fuller Test ADF)
From the table ( 3 ) showing the test results ADF , And the results showed that the variable
number of passengers LN Q ) At its original level , which means acceptance of the null
hypothesis H0 ), Which states that the time series is not silent, and we reject the alternative
hypothesis (H1 Which states that the time series is silent and this is confirmed by the value of
Prob) ), Which was greater than 5% at the level and was not silent in the first difference.
The results of the table below showed the acceptance of the null hypothesis at the level
and the first difference of population variables LNPN), the average passenger travel tariff
(LNPR, and the number of vehicles (LNVN), for the non-dormancy of these variables. The
same table shows the variable speed of the train (LNST) at the first difference and this is
confirmed by the value of Prob) Smaller than 5%.
Table 3 Dicky Fuller test results ADF) for the variables of passenger numbers, population, average
passenger transport tariff, number of vehicles and train speed
The first difference the level
variable
Without a
fixed limit
or a
general
trend
Fixed limit
and general
direction
Fixed
limit
only
Without a
fixed limit
or a general
trend
Fixed limit
and general
direction
Fixed
limit only
Prob * Prob * Prob * Prob * Prob * Prob *
0.0257 0.4236 .2017 0.3591 0.4313 0.1443 LNQ
0.7027 0.7345 0.4619 0.9831 0.5034 0.9978 LNPN
0.0137 0.2319 0.0744 0.9083 0.5573 0.7007 LNPR
0.2826 0.9433 0.7146 0.8500 0.1146 0.7490 LNVN
0.0012 0.0778 0.0000 0.9083 0.0023 0.3236 LNST
Source: Based on the outputs of the program EVIEWS9).
5.1.2. Philips Peron Test Results PP
The results of Table (4) of the Phillips Peron test indicate that the variable number of
passengers LNQ) At its original level, which means acceptance of the null hypothesisH0),
Which states that the time series is not silent, and we reject the alternative hypothesis (H1

Which states that the time series is silent and this is confirmed by the value of Prob), Which
was more than 5% at the level and was found to be the first team.
The results of the table below show the acceptance of the null hypothesis at the level of
population variables LNPN, The average passenger travel tariff (LNPR, The number of
vehicles (LNVN) Train speed (LNST) for not keeping these variables at their original level. It
is clear from the same table that the population variables LNPN ), The average passenger
travel tariff (LNPR , The number of vehicles ( (LNVN) Train speed ( (LNST) At the first
difference because the value of ( prob ) Smaller than 5%, by testing ( (ADF) And( PP ), It is
clear that the results of the population variables, the average passenger transport tariff, the
number of vehicles and the speed of the train are different. Therefore, PP) because it is more
accurate and efficient, especially in small samples of the test ADF) and now it is possible to
apply methodology (ARDL) Model that accepts variables to be integrally class I (1).
Table 4 Dicky Fuller test results ADF) for the variables of passenger numbers, population, average
passenger transport tariff, number of vehicles and train speed
variable
Without a
fixed limit
or a
general
trend
Fixed limit
and general
direction
Fixed
limit
only
Without a
fixed limit
or a general
trend
Fixed limit
and general
direction
Fixed
limit only
0.0000 0.0029 0.0005 0.3495 0.8017 0.3891 LNQ
0.1735 0.0278 0.0088 1.0000 0.7504 0.9966 LNPN
0.0000 0.0002 0.0000 0.8552 0.2724 0.3540 LNPR
0.0000 0.0001 0.0000 0.9996 0.5110 0.7530 LNVN
0.0000 0.0010 0.0001 0.5781 0.3027 0.2401 LNST
Source: Based on the outputs of the program EVIEWS9
5.2. Test (ARDL)
After the dormancy test has been conducted for the time series and knowing its
complementarity, we will estimate the model ARDL). To estimate the demand function for
the passenger transport service by adopting the variables population, average passenger
transport tariff, number of vehicles and train speed rate as explanatory variables affecting the
model.
5.2.1. The results of joint integration according to the border test
The results of Table (5) show that there is no correlation between the long-term common
integration between the variables of the search, and consequently accept the hypothesis of
nothingness (H0) Which states that there is no long-term relationship between variables and
reject the alternative hypothesis ( H1 ), Which provides for a long-term integrative
relationship between the variables, and this is confirmed by the value of ( F (2.374), which is
smaller than the calculated value of the upper limit of (3.49) at a significant level (5%).

(1999-2016)
Table 5 Boundary testing results for joint integration
K Value Test Statistic
4 2.374 F-statistic
Critical Value Bounds
I1 Bound I0 Bound Significance
3.09 2.2 10%
3.49 2.56 5%
3.87 2.88 2.5%
4.37 3.29 1%
Source: Prepared by the researcher based on the outputs of the program (EVIEWS9).
The results of the statistical tests showed that there is no problem of consistency of
homogeneity of variance because the value of Prob Larger than 5% where it reached)
Prob.Ch-Square 0.5674 (And as the results showed there was no auto-correlation problem
because Prob.Chi-Square More than 5% as its value) 0.1875), as the results showed Prob (F-
statistic) The statistical significance of the model was 0.0003, which is smaller than 5%.
5.2.2. Regression of joint integration according to model ARDL
Table (6) indicates that the model ARDL It automatically determined the optimal delay times
by giving time delays to the variable number of passengers , giving a time lag to the
population, no time lag for the passenger transport tariff, one time lag for the passenger
vehicle variable and time lag for the train speed variable , The statistical tests of the model
illustrate the quality of the estimated model through the value of the adjusted limiting factor
of 9 7 %, as well as test value (F-Stat), Which is statistically significant (211.1302 ) and
Prob-F ) ADULT (0.0000) This confirms the significance of the model as a whole.
Table 6 Estimation of model ARDL Integration
Variables
Parameters
Coefficient
Statistical value t T-
Ratio
Values P P-value
LN Q (-1) 1.476843 13.68421 0.0000
LN Q (-2) 0.582544- -5.789207 0.0000
LNPN 9.463585 - -1.455120 0.1510
LnPN (-1) 20.15873 1.729378 0.0891
LnPN (-2 ) 15.11411 - -2.116376 0.0386
LnPR 0.060378 1.069588 0.2892
LNVN 0.098638 0.031945 0.1965
LNVN (-1) 4.129396 1.306737 0.1965
LNST 3.102630 - -3.381023 0.0013
LNST (-1) 5.709344 4.209990 0.0001
LNST (-2) 2.241104 - -2.618978 0.0112
CON 21.79597 2.545980 0.0136
R-Bar-Squared = 0.9 8 R-Squared = 0.97
F-Statistic = 211.1302 Prob (F-Statistic) = 0. 0000 DW-Statistic = 2.24
Source: Based on the outputs of the program (EVIEWS9).

5.2.3. Error Correction Model and Short Term Relationship
Table (7) shows the short-term relationship of estimating the error correction model, which
represents the expression of the variables used in the first difference formula, adding the error
correction threshold by one-time slowdown. (𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡−1) As an explanatory variable, a value is
obtained Ect From the decline of common integration in the first step. Table (7) shows the
error correction model and the short-term elasticity's between the model variables. The results
indicate that there is an inverse relationship between the variable population and the
dependent variable such as the number of passengers. Because the population increase leads
to an increase of passenger counter This indicates turnout is little for the acquisition mode rail
in Iraq, while taking the average tariff passenger variable signal positive and this is contrary
to economic theory, because the price is linked to the relationship of an inverse of the amount
requested, and take a variable number of passenger vehicles reference Positive, ie, increasing
passenger vehicles leads to increased passenger numbers , And taking variable speed rate
signal train The lack of efficiency and this confirms the inefficiency of the railway sector in
Iraq and the reluctance of individuals to acquire this medium because of the poor transport
and the transfer to other modes of transport, which led to a decline in the number of
passengers transported by that medium.
The error correction factor of 0.10 - negative as expected and very significant 3 0.000)
this means that there is a long-term equilibrium relationship between the economic variables
studied in the short term.
Table 7 Results of the short-term relationship of the model ARDL
Variables
Parameters
Coefficient
Ratio
Values P P-value
LNPN. -9.398879 -1.640225 0.1064
LNPR. 0.000830 0.007886 0.9937
LNVN. 0.621260 0.224867 0.8229
LNST. -3.039656 -3.984365 0.0002
ECM (-1). -0.107261 -3.889257 0.0003
A source: prepared by the researchers based on the outputs of the program (EVIEWS9).
5.2.4. Estimating the long-term relationship between model variables
Table (8) shows the long-term impact of population, average passenger traffic, number of
passenger vehicles and train speed. The results indicate an inverse relationship between
population and passenger numbers. This confirms the unwillingness of passengers to acquire
railways for lack of comfort, the efficiency of this medium in Iraq , which suffers from
extinction and feet and worn out in the railway, locomotives and trains complementary and
other services lines, and the back of the results and a positive relationship between the
average tariff passenger transport passengers and the preparation of this is contrary to
economic theory may be the reason that thousands Ed prefer the acquisition of transport
mode which are available in which all means of comfort, safety and speed on the cost and this
means that the alternatives are more sophisticated and the speed and convenience of the mode
of rail, and results also indicate that there is a positive correlation between the number of
vehicles traveling and preparing and this is logical as the development and increase in the
number of vehicles Which leads to attracting passengers especially if they have all the means
of development and comfort , and taking the variable rate of speed of the train positive signal,
the more trains speed the number of passengers increased in this mode of transport, but in

(1999-2016)
Iraq the train speeds are very low and reached less than 50 Km / h, resulting in a decrease in
the number of passengers transported by that means of transport.
It is noted from Tables (7) and (8) that the short and long-term elasticities of the
dependent variable representing the demand for rail transport relative to the explanatory
variables did not have the same signal for some variables and that the long-term elasticities
are larger than the short term, Economic logic and behavior as there is enough time to adjust
and respond in the long term.
Table 8 results of the long-term relationship of the model ARDL
Variables Parameters Coefficient
Ratio
Values P P-value
LN PN
population
41.806129 - -4.070261 0.0001
LNPR
Average passenger
transport tariff
0.571211 1.019768 0.3121
LNVN
Number of vehicles
39.999825 4.146440 0.0001
LNST
Train speed rate
3.458898 0.938686 0.3518
CON
Fixed limit
206.203385 2.909671 0.0051
Error correction model equation ECM
𝑬𝑬𝑬𝑬𝑬𝑬 = 𝑳𝑳𝑳𝑳𝑳𝑳 − 𝟐𝟐𝟐𝟐𝟐𝟐. 𝟐𝟐𝟐𝟐 − 𝟒𝟒𝟒𝟒. 𝟖𝟖𝟖𝟖𝟖𝟖𝟖𝟖𝟖𝟖𝟖𝟖 + 𝟎𝟎. 𝟓𝟓𝟓𝟓𝟓𝟓𝟓𝟓𝟓𝟓𝟓𝟓 + 𝟑𝟑𝟑𝟑. 𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝟗 + 𝟑𝟑. 𝟒𝟒𝟒𝟒𝟒𝟒𝟒𝟒𝟒𝟒𝟒𝟒
5.2.5. Test results for optimal model delays
Table (9) shows that the optimal delay time for the model is estimated and based on
criteria
Table 9 Test results of optimal deceleration of the model
Lag AIC SC HQ
0 -0.821704 -0.661097 -0.757909
1 -14.86944 -13.90580 -14.48667
2 -15.94807 * -14.18139 * -15.24632 *
3 -15.40402 -12.81375 -14.37637
Indicates the number of optimal delay periods and all the tests are significant at level 5%
Standard Hanan-Quinn: HQ, Standard Schwarz: SC, The standard of AKIK: AIC
Many of them (SC, AIC, HQ) is (2) any two years because three criteria were the least
valuable in the second year.
5.3. Cranger Causality Test
Table (10) shows the results of the causal relationship between the variables used in the
model by the causal Kanger method. The null hypothesis H0. That there is no causal

relationship between the variables, while the alternative hypothesis H1) with a causal
relationship between the variables, Prob) If it is greater than 10%, there is no causal
relationship between the variables and if less than 10%, there is a causal relationship between
the variables.
Table 10 Krantger test results causality
Probability
F.
Statistic
Period of
default
Relationship The decision
0.8813 0.12658 2 LN PN → LN Q
We accept the null hypothesis
and reject the alternative
hypothesis
0.4233 0.87124 2 LN Q → LN PN
hypothesis
0.4696 0.76464 2 LNPR → LN Q
hypothesis
0.4323 0.84956 2 LN Q → LNPR
hypothesis
0.8012 0.22238 2 LNVN → LNQ
hypothesis
0.5108 0.67874 2 LNQ → LNVN
hypothesis
0.2228 1.53652 2 LNST → LNQ
hypothesis
0.4340 0.84563 2 LNQ → LNST
hypothesis
0.9209 0.08251 2 LNPR → LNPN
hypothesis
0.0347 3.54098 2 LNPN → LNPR
We reject the null hypothesis
and accept b alternative
hypothesis
0.8961 0.10990 2
LNVN →
LNPN
hypothesis
0.0325 3.61228 2
LNPN →
LNVN
hypothesis
0.3376 1.10433 2 LNST → LNPN
hypothesis
0.0314 3.65029 2 LNPN → LNST
hypothesis

(1999-2016)
0.0852 2.55875 2
LNVN →
LNPR
We reject the hypothesis of
nothingness and accept the
alternative hypothesis
0.9549 0.04621 2
LNPR →
LNVN
hypothesis
0.0440 3.27859 2 LNST → LNPR
hypothesis
0.6395 0.45022 2 LNPR → LNST
hypothesis
0.9632 0.03754 2
LNST →
LNVN
hypothesis
0.1521 1.93872 2
LNVN →
LNST
hypothesis
Source: Prepared by the researcher based on the outputs of the program (EVIEWS9)
The results showed in Table (10) that there is no causal relationship between the variable
of the population and the number of passengers, and there is no relationship between the
number of passengers to the population because the value of Prob. The results indicate that
there is no causal relationship between the number of vehicles and the number of passengers.
There is no significant relationship between the rate of train speed and the number of
passengers.
The results also showed that there is no causal relationship between passenger tariffs and
the population, but a causal relationship between the population and the passenger transport
tariff. This confirms that if the ticket prices are reduced, the desire of the population to
purchase the railway mode will increase. The increase in the population needs to increase the
number of car seats in the case of increased demand for rail mode, and also revealed a causal
relationship of the population to the rate of train speed, the higher the speed of trains led to an
increase in the desire of individuals to move by rail , And the results showed a causal
relationship of the number of vehicles to the average tariff of passenger transport, which
means that the lower the ticket tariff the fewer seats in the vehicles, As it turns out that there
is a causal relationship between the average train speed and the average tariff and this shows
that the higher the rate of train speed will attract passengers regardless of the price of the
ticket compared to other transport alternatives and this is confirmed by the value of Prob Less
than 10%
5.4. Stability Test Results for Estimated Parameters
The structural stability of the model should be tested in a manner ARDL which is estimated
to verify the accuracy and accuracy of its results, by testing the cumulative total of
condominiums as well as testing the cumulative sum of the follow-up and development boxes
by Brown and others (Browen et al).
If the curve for both tests within the critical limits is at the level of (% 5) In this case, the
null hypothesis is accepted that the variables under study are stable. The shape of the two
tests mentioned in Figure (1) shows the stability of parameters in the short and long term of
the estimated model (ARDL) Because the curve according to the test is the cumulative total

of the residual squares falls within the critical limits and the difference around the zero value
at a significant level (% 5 ), But the cumulative pool test did not stabilize the parameters for
some years beyond critical limits and this is undesirable and reduces the reliability and
reliability of the model.
Figure 1 Stability test results of estimated parameters
Source: Prepared by the researcher depending on the program (EVIEWS9)
6. SECOND: THE RESULTS OF THE ESTIMATION AND ANALYSIS
OF THE DEMAND FUNCTION FOR THE TRANSPORT SERVICE OF
THE RAILWAY SECTOR
Tohz function demand for cargo transportation service for a period of (2016-1999) As
follows:
𝐿𝐿𝐿𝐿𝐿𝐿 = 𝐵𝐵0 + 𝐵𝐵1LNPN + 𝐵𝐵2LNPR + 𝐵𝐵3LNVN + 𝐵𝐵4LNST + 𝑈𝑈𝑖𝑖 (1)
whereas (LNQ) is the dependent variable of the quantity of goods (thousand / ton), and
LNPN (Population), LNPR) Average tariff of goods (thousand / dinar), (LNVN) Number of
trucks, LNST). Train speed rate is the independent variables affecting the required quantity.
6.1. Unit Root Test Results
Before estimating the demand function, the time series of the model must be tested and the
degree of complementarity of the variables to be verified to determine the existence of a
temporal trend and to eliminate it if any, in order to obtain time series.
6.1.1. The results of the Dicky Fuller test ADF
Of Table (11) showing the test results ADF, And the results showed a lack of sleep variable
amount of goods (LNQ) At its original level, which means acceptance of the null hypothesis
H0), Which states that the time series is not silent, and we reject the alternative hypothesis

(1999-2016)
(H1 Which states that the time series is silent and this is confirmed by the value of Prob,
Which was greater than 5% at the level and became silent by the first difference.
Table 11 Dicky Fuller test results ADF for the variables of the quantity of goods, the number of the
population, the average tariff of the transport of goods, the number of trucks and the speed of the train
variable
Without a
fixed limit
or a
general
trend
Fixed limit
and general
direction
Fixed
limit
only
Without a
fixed limit
or a general
trend
Fixed limit
and general
direction
Fixed
limit only
0.0007 0.0412 0.0091 0.2536 0.4785 0.4446 LNQ
0.7027 0.7345 0.4619 0.9831 0.5034 0.9978 LNPN
0.0323 0.1851 0.0645 0.9918 0.0070 0.1749 LNPR
0.0901 0.6974 0.3761 0.8663 0.1901 0.4133 LNVN
0.0017 0.1164 0.0296 0.6849 0.0850 0.3069 LNST
Source: Based on the Outputs of the Program EVIEWS9
The results of the table below showed the acceptance of the null hypothesis at the level
and the first difference of population variables LNPN), the average tariff transport of goods
(LNPR, Number of trucks (LNVN), for the non-dormancy of these variables. The same table
shows the variable speed of the train (LNST) at the first difference and this is confirmed by
the value of Prob) Smaller than 5%.
6.1.2. Philips Peron Test Results PP
We infer from the results table (12) to test not sleep Phillips Perron variable amount of goods
(LNQ) At its original level, which means acceptance of the null hypothesis H0, Which states
that the time series is not silent, and we reject the alternative hypothesis (H1 Which states
that the time series is silent and this is confirmed by the value of Prob, Which was more than
5% at the level and was found to be the first team.
Table 12 Dicky Fuller test results ADF ) For the variables of the quantity of goods, the number of the
population, the average tariff of the transport of goods, the number of trucks and the speed of the train
variable
Without a
fixed limit
or a
general
trend
Fixed limit
and general
direction
Fixed
limit
only
Without a
fixed limit
or a general
trend
Fixed limit
and general
direction
Fixed
limit only
0.0006 0.0353 0.0077 0.2021 0.8112 0.7620 LNQ
0.1735 0.0278 0.0088 1.0000 0.7504 0.9966 LNPN
0.0026 0.0048 0.0018 0.9997 0.5634 0.3372 LNPR
0.0000 0.0039 0.0006 0.9615 0.5801 0.4539 LNVN
0.0000 0.0026 0.0004 0.5114 0.3064 0.1735 LNST
Source: Based on the outputs of the program EVIEWS9.

The results of the table below show the acceptance of the null hypothesis at the level of
population variables LNPN, The average tariff transport of goods LNPR, The number of
vehicles (LNVN) Train speed (LNST) For not keeping these variables at their original level.
It is clear from the same table that the population variables LNPN, The average tariff
transport of goods LNPR, Number of trucks (LNVN) Train speed (LNST) At the first
difference because the value of (prob) Smaller than 5%, by testing (ADF) And (PP), It is
clear that the results of population variables, average freight tariffs, number of trucks and
train speed vary, so we will rely on the results of the test (PP) Because it is more accurate and
efficient, especially in small samples of the test (ADF) And now it is possible to apply
methodology ( ARDL ) Model that accepts variables to be integrally class I (1) .
6.1.3. Test (ARDL)
After we have tested the dormancy of the time series and know their integration, we will test
the model ARDL) ) Function to estimate the demand for freight transport service by rail
variables , the adoption of the population and the average tariff transportation of goods and
the number of trucks and the rate of speed train covariates impressive illustrations
Balonmozj.
6.1.3.1. The results of joint integration according to the border test
Table 13 Boundary tEesting results for joint integration
K Value Test Statistic
4 4.436454 F-statistic
Critical Value Bounds
I1 Bound I0 Bound Significance
3.09 2.2 10%
3.49 2.56 5%
3.87 2.88 2.5%
4.37 3.29 1%
From the results of Table (13) it is clear that there is a long-term common integration
relationship between the variables of the research, and consequently we reject the null
hypothesis H0 ) Which states that there is no long-term relationship between variables and
accept the alternative hypothesis (H1), Which provides for a long-term integrative
relationship between the variables, and this is confirmed by the value of ( F (4.43), which is
higher than the calculated value of the upper limit (3.49) at a significant level (5%).
The results of the statistical tests showed that there is no problem of consistency of
homogeneity of variance because the value of Prob Larger than 5% where it reached (0.7020)
Prob.Ch-Square As the results showed, there was no auto-correlation problem Prob. Chi-
Square Is greater than 5% with a value of (0.3926), as shown by results Prob (F-statistic)
Morality of the model in terms of statistical value of 0.0000, which is smaller than 5%.
6.1.3.2. Regression of joint integration according to model ARDL
Table (14) indicates that the model ARDL It automatically determined the optimal delay
times by giving three time delays to the quantity variable, no time lag for the population, one
time lag for the tariff of the goods, no time lag for the number of cargo trucks and one time
lag for the train speed variable. The statistical tests of the model illustrate the quality of the

(1999-2016)
estimated model through (98 %), as well as the test value of F-Stat), which is statistically
significant (487.5055) and Prob-F) ADULT (0.0000) this confirms the significance of the
model as a whole.
Table 14 Model estimation ARDL Integration
Variables
Parameters
Coefficient
Ratio
Values P P-value
LN Q (-1) 1.306624 11.18066 0.0000
LN Q (-2) 0.163817 - -0.835197 0.4070
LNQ (-3) 0.254665 - -2.333677 0.0230
LNPN 0.102591 0.133058 0.8946
LN PR 1.162750 3.289276 0.0017
LNPR (-1) 1.148173 - -3.223365 0.0021
LNVN 1.387407 - -3.024124 0.0037
LNST 1.331193 - -3.212269 0.0021
LNST (-1) 1.273240 2.855722 0.0059
CON 12.42282 1.242905 0.2188
R-Bar-Squared = 0.98 R-Squared = 0.98
F-Statistic = 487.5055 Prob (F-Statistic) = 0. 00000 DW-Statistic = 1.94
Source: Based on the outputs of the program (EVIEWS9)
6.1.3.3. Error correction model and short term relationship
It will model ARDL B. Determine the short-term relationship of estimating the error
correction model, which represents the expression of the variables used in the first difference
formula, adding the error correction threshold by one time delay as an explanatory variable.
Ect From the decline of common integration in the first step.
Table (15) shows a specimen error correction and elasticity's short term between the
specimen variables , as the results indicate that there is an inverse relationship between the
variable population and the dependent variable represented by the amount of goods and this is
not logical because the increase of populations n leads to increasing the amount of goods and
this shows the turnout few owners of goods to transport their goods by means railways in
Iraq, while taking a variable average tariff freight transport signal positive and this is contrary
to economic theory, because the price is linked to the relationship of inverse required amount,
take a variable number of trucks signal negative i.e. that increased truck does not lead to an
increase km Of goods and this confirms the lack of demand from individuals to transport
their goods to this medium , And taking the variable speed of the train signal negative and
this confirms the inefficiency of the railway sector in Iraq and the reluctance of the owners of
goods from the acquisition of this medium because of the poor transport and the transfer to
other modes of transport, resulting in a decrease in the quantities of goods transported by that
medium.
The value of the error correction coefficient of (0.10-) is negative as expected and very
significant (0.0000). This means that there is a long-term equilibrium relationship between
the economic variables studied in the short term.

Table 15 Results of the short-term relationship of the model ARDL
Variables
Parameters
Coefficient
Ratio
Values P P-value
LNPN. 0.106990 - 0.050216 - 0.9601
LNPR. 1.175639 4.029595 0.0002
LNVN. 1.699024 - 1.528723 - 0.1317
LNST. 1.346314 - 3.449317 - 0.0010
ECM (-1). 0.109099 - 5.080361 - 0.0000
Source: Based on the outputs of the program (EVIEWS9)
6.1.3.4. Estimating the long-term relationship between model variables
Table 16 shows the long-term effect of population, average freight tariff, number of cargo
trucks and train speed. The results indicate a positive relationship between the population and
the quantity of goods. The results showed a positive relationship between the average tariff of
goods and the quantity of goods This is contrary to the economic theory and may be the
reason that individuals prefer to acquire the mode of transport, which provides all the means
of safety and speed to transport their goods and this means that the alternatives are more
developed and faster and safer than the rail mode, and the results also indicate that there is an
inverse relationship between the number of trucks And quantities of bleach Movable goods
This confirms that despite the increase in trucks does not lead to an increase in the amount of
goods transferred because the owners of goods prefer to use other means that are available
speed and safety and keep the goods from damage , and taking the variable rate of train speed
signal negative also because this type of transport in Iraq speeds of trains E is very low and
reached less than 50 km / h, which led to a decrease in quantities transported by that means of
transport and the owners of goods are looking for the fastest way to transport their goods.
Table 16 results of the long-term relationship of the model ARDL
Variables Parameters Coefficient
Ratio
Values P P-value
LNPN population 0.917160 0.130310 0.8968
LNPR Average freight
tariff
0.130320 0.128501 .8982
LNVN Number of
trucks
12.403347 - -4.074776 0.0001
LNST Train speed
rate
0.518096 - -0.248055 0.8050
CON Fixed limit 111.059364 1.597041 0.1156
Error correction model equation ECM
6.1.3.5. Test Results for Optimal Model Delays
Table (17) shows that the optimal delay of the estimated model is based on several criteria
(SC, AIC, HQ) is (2) any two years because three criteria were the least valuable in the
second year.

(1999-2016)
Table 17 Test results of optimal deceleration of the model
Lag AIC SC HQ
0 -4.022453 -3.860562 -3.958226
1 -19.04576 -18.07441 -18.66039
2 -20.81376 * -19.03295 * -20.10725 *
3 -20.49047 -17.90020 -19.46283
Source: Prepared by the researcher based on the outputs of the program (EVIEWS9). Indicates the
number of optimal delay periods and all the tests are significant at level 5% Standard Hanan-Quinn:
HQ, Standard Schwarz: SC, The standard of AKIK: AIC
6.1.3.6. Cranger Causality Test
Table (18) shows the results of the causal relationship between the variables used in the
model by the causative kranger method. The null hypothesis H0) That there is no causal
relationship between the variables used, while the alternative hypothesis H1) With a causal
relationship between the variables used, and this is illustrated by the value of (Prob) If it is
greater than 10%, there is no causal relationship between the variables and if less than 10%,
there is a causal relationship between the variables.
Table 18 Krantger test results causality
Probability
F.
Statistic
Period of
default
Relationship the decision
0.3644 1.02529 2 LNPN → LNQ
hypothesis
0.2226 1.53765 2 LNQ → LNPN
hypothesis
0.2227 1.53714 2 LNPR → LNQ
hypothesis
0.9644 0.03630 2 LNQ → LNPR
hypothesis
0.0195 4.18405 2 LNVN → LNQ
hypothesis
0.7552 0.28192 2 LNQ → LNVN
hypothesis
0.5343 0.63298 2 LNST → LNQ
hypothesis
0.8281 0.18921 2 LNQ → LNST
hypothesis
0.9649 0.03572 2 LNPR → LNPN
hypothesis

0.0596 2.94618 2 LNPN → LNPR
and accept the alternative
hypothesis
0.4793 0.74378 2
LNVN →
LNPN
hypothesis
0.3520 1.06115 2
LNPN →
LNVN
hypothesis
0.9519 0.04934 2 LNST → LNPN
hypothesis
0.1571 1.90429 2 LNPN → LNST
hypothesis
0.9462 0.05536 2
LNVN →
LNPR
hypothesis
0.3384 1.10186 2
LNPR →
LNVN
hypothesis
0.0494 3.15139 2 LNST → LNPR
hypothesis
0.2338 1.48625 2 LNPR → LNST
hypothesis
0.9582 0.04276 2
LNST →
LNVN
hypothesis
0.3978 0.93488 2
LNVN →
LNST
hypothesis
The results in Table (18) show that there is no causal relation between the population
variable and the quantity of goods. There is no relation between the quantity of goods and the
population because the value of Prob. The results indicate that there is a causal relationship
between the number of trucks and the quantity of goods. The higher the number of trucks, the
greater the quantity of goods transported on the road. However, there is no significant
correlation between the amount of cargo to the number of trucks and the rate of train speed to
prepare passengers.
The results also showed that there was no causal link from the tariff transfer of goods to
the population, but a causal relationship of the population appeared to tariff transportation of
goods and this confirms if it has been reduced the cost of transporting goods prices will
increase the willing owners of goods acquisition mode rail, as shown the results that there is
no causal relationship between the number of trucks population, also found that there was no
causal relationship between the population and the rate of speed of the train, and the results
showed that there was no causal relationship between the number of trucks and the average

(1999-2016)
tariff transport of goods, and as it turns out there is a causal relationship of the speed of the
train rate to the average tariff And This shows that the greater the speed of the train rose
cargo owners will attract the acquisition of rail to transport their goods regardless of the price
of the cost of transport compared to other transport alternatives, and this is confirmed by the
value of the Prob Less than 10%.
6.1.4. Results of the stability test for the estimated parameters
Figure 2 test results of the stability of the estimated parameters
Source: Prepared by the researcher depending on the program (E VIEWS9).
Figure 2 shows the stability of parameters in the short and long term of the estimated
model (ARDL) Because the curve according to the two tests falls within the critical limits
and the difference around the zero value at a significant level (% 5).
7. CONCLUSIONS AND RECOMMENDATIONS
7.1. Conclusions
1. The results of the demand function for the passenger transport service indicate that
there is an inverse relationship between the size of the demand for railway transport
service and the number of population and the number of vehicles. There was an
inverse relation between the size of demand, the passenger transport tariff and the
speed rate in the short term. Study.
2. The results of the demand function for the railway sector in Iraq showed an inverse
relationship between the number of passengers and the population. There was also a
positive relationship between the number of passengers, the tariff of passenger
transport, the rate of speed and the number of vehicles in the long term. Between
variables.
3. The results of the demand function for the goods transport service show that there is
an inverse relation between the quantity of goods, population, speed and number of

trucks. The results showed a positive relationship between the quantity of goods and
the tariff of goods transport in the short term.
4. We infer from the demand function results for the transport sector railway in Iraq,
there is a direct correlation between the amount of goods and the number of
population and tariff transport of goods, also it appeared an inverse relationship
between the amount of goods and the rate of speed and the number of trucks in the
long term, as also turned out a long- term complementary relationship between the
variables.
5. It is clear from the results of the standard analysis of the demand function for
passenger and cargo transport that the factors influencing the numbers of passengers
and the quantities of goods, some did not take the expected signal, because these
factors such as cars, trucks and train speed, which represent the infrastructure and
service suffers from foot and wear and did not have the efficiency that Make it an
attractive mode of transport for individuals.
7.2. RECOMMENDATIONS
1. Focus on supporting the development of infrastructure and basic task by railway lines
updating and creating new lines linking all Iraqi provinces and areas of tourism,
religious and economic attractions, as well as neighboring countries and world-class ,
and this journal works to attract and stimulate the demand for that service.
2. 2- Work on the development and modernization of trains to keep abreast of the
developments in this sector in the world, which makes trains speed up to high levels
and this will contribute to increasing the competition of railways for other means of
transport.
3. 3- Contracting with international companies that equip the railway sector with
passenger carriages and trucks for the transport of goods that enjoy speed, comfort
and safety and there are all modern means that work to attract the traveler and the
owner of the goods and goods.
4. 4- Work on developing plans to develop stations and other complementary services,
such as shops and entertainment facilities, in parallel to those in the developed
countries , because the passengers and the owners of the goods are looking for an
integrated mode of transport in all aspects of services, because the railway is not
merely a medium consisting of Line, train, cart and truck, but is a mode extending to
be a system of work breaks for the traveler and places to save goods protected from
damage and this system must be sophisticated and modern in order to attract the
traveler and the owner of the goods.
5. 5- Set a set of controls that will set the appropriate pricing for passenger transport
tickets according to the distance traveled, as well as setting a suitable tariff according
to the type and size of the goods being transported, which in turn will compete with
the railway sector for other means of transport, as well as attract individuals to this
sector of transport.
REFERENCES
[1] Ministry of Planning, Central Bureau of Statistics, Directorate of Transport and
Communications, Statistics of Rail Transport Activity 1999-2016.
[2] Ministry of Transport, General Company of Iraqi Railways, Financial Section, Balance
Sheet.

(1999-2016)
[3] Attia, Mohamed Abdelkader (2004) Theoretical Economics between Theory and Practice,
Riyadh.
[4] Abu Nayla, Hassan Flowers (2014) Measuring the relationship between banking
development and poverty in Iraq for the period 1980-2010 , a thesis submitted to the
Department of Economics / University of Baghdad to obtain the degree of "thesis in
economic sciences"
[5] Abdali, Abed Ben Abed (2005) Appreciation Effect Exports On the growth Economic
development in a Countries Islamic: a study Analytical standard, Magazine center Valid
Slave Allah Full Economy Islamic, Mosque Al-Azhar, and the number 27.
[6] Edition, Revised (2007) Applied Econometrics A Modern, Dimitrios Asteriou and
Stephen G. Hall, London.
[7] Gujarati, DN (2004) Basic Econometrics, 4 Third, McGraw-Hill Companies MM, Inc.
[8] V.S. Kossov, G.M. Volokhov, M.N. Ovechnikov, E.S. Oganyan, A.A. Lunin, Methods to
Justify the Strength and Life Time of Railway Transport Objects, International Journal of
Civil Engineering and Technology (IJCIET) 9(13), 2018, pp. 1098–1104.
[9] A. Ramaraju, Operational Efficiency of Indian Railways, International Journal of
Management (IJM), Volume 4, Issue 3, (May - June 2013), pp. 209-216.

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