Computer Modeling of the Dynamics of Interregional Freight
Transport Depending on Macroeconomic Indicators
Maksim Tatarintsev
a
, Petr Nikitin
b
and Sergey Korchagin
c
Department of Data Analysis and Machine Learning, Financial University under the Government of
the Russian Federation, Shcherbakovskaya, 38, Moscow, Russian Federation
Keywords: Analysis of Macroeconomic Indicators, Dynamics of Freight Traffic, Freight Turnover Assessment,
Forecasting, Import, Export, GDP, GNI, Graph, Correlation Matrix, Correlation-regression Analysis.
Abstract: The purpose of this research is to consider various macroeconomic indicators of world regions(North America,
South America, Europe, Mediterranean, Persian Gulf, CIS, South Africa, Central America, West Africa, East
Africa, East Asia, South Asia, Southeast Asia, Oceania, Australia) and federal districts in the Russian
Federation (Central, North-western, Southern, North Caucasian, Volga, Ural, Siberian, Far Eastern), visualize
the received data, determine their impact on the volume of freight traffic between the designated zones,
identify patterns with the help of which we could carry out various kinds of forecasting. Further, it is necessary
to carry out direct forecasting for several products of the selected industry, applying the necessary math model.
1 INTRODUCTION
It's no secret that interregional trade is important in
the global economy. Even though each state is unique,
the countries within certain zones have somewhat
similar socio-economic and physical-geographical
characteristics. Based on this kind of "kinship", 15
regions of the world can be conditionally defined:
East Africa, West Africa, Central America, South
Africa, Oceania, Mediterranean, CIS, Persian Gulf,
South America, Southeast Asia, South Asia, Europe,
Australia, East Asia, North America.
Each of these zones has completely different
infrastructure, and, speaking directly about our topic,
macroeconomic indicators that are very different
from each other, which stimulate the active
development of interregional trade.
In addition to the global dynamics in the field of
freight traffic and their impact on the economic
indicators of the regions, it is necessary to highlight
trends and relationships in a country. The Russian
Federation was chosen for the research as one of the
most geographically vast and economically
interesting countries in the world.
a
https://orcid.org/0000-0003-1783-9411
b
https://orcid.org/0000-0001-8866-5610
с
https://orcid.org/0000-0001-8042-4089
In a country of 8 major districts: Central Federal
district, North-western Federal district, Southern
Federal district, North Caucasian Federal district,
Volga Federal district, Ural Federal district, Siberian
Federal district and Far Eastern Federal district. For
each of these regions, we selected key economic
indicators for a detailed analysis and identification of
various interesting patterns that allow us to draw
concrete conclusions about the direct impact of
macroeconomic indicators on the volume of cargo
flows both in these zones and in the whole country
Thus, the article will consider: the dynamics of
cargo flows between the selected regions,
macroeconomic indicators of the regions, as well as
their direct influence on each other; the relationship
between freight turnover within the districts of the
Russian Federation and their socio-economic
characteristics. Another important task is to be able to
predict in the short term how the volume of selected
cargo will change in 2020. For this, a special
mathematical algorithm will be used.
Tatarintsev, M., Nikitin, P. and Korchagin, S.
Computer Modeling of the Dynamics of Interregional Freight Transport Depending on Macroeconomic Indicators.
DOI: 10.5220/0010390912491257
In Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021) - Volume 2, pages 1249-1257
ISBN: 978-989-758-484-8
Copyright
c
2021 by SCITEPRESS Science and Technology Publications, Lda. All r ights reserved
1249
2 IMPACT OF CARGO FLOWS
ON THE MACROECONOMIC
INDICATORS OF THE WORLD
REGIONS
2.1 Analysis of the Mean Nominal GDP
of the World Regions
Gross domestic product (GDP) is the most important
macroeconomic indicator, which can be understood
as the value of all goods and services produced on the
territory of the country. There is another important
characteristic that, along with GDP, reflects the
degree of development of a country. Gross national
income (GNI) is defined as the sum of GDP and the
balance of primary income (summing up the income
of citizens received from abroad, subtracting funds
taken out of the country by foreigners). To analyse
entire macro-regions, it is necessary to determine the
characteristics by which these districts will be further
investigated. So, the mean nominal gross domestic
product and gross national income were selected.
They were calculated as the arithmetic mean of the
corresponding indicators of all countries located in
this zone.
So, in Fig.1 shows a heat map of the world, where
each of the 15 regions is highlighted in colour,
reflecting the value of the average nominal GDP in
this zone in comparison with others. Data for
modelling was taken from. The most economically
developed macro-regions are North America and East
Asia with an average GDP of 5896 billion USDs and
2766 billion USD, respectively. Australia also ranks
high with a GDP of 1376 billion $. Of course, you
need to understand that there are countries that have
a very developed economy, but due to the fact that the
region is dominated by countries that have a low
GDP, the average indicator is decreasing. For
example, the United Kingdom, France and Germany
have a GDP in 2019 more than 2,500 billion USD. but
taking into account some other countries that have
this indicator more than five times less, it turns out
that Europe, as one of the most comprehensively
developed parts of the world, in comparison with the
leading macro-regions in this characteristic, has a
nominal gross domestic product equal to $ 659
billion. But if we consider some other countries that
have this indicator more than five times less, it turns
out that Europe, as one of the most comprehensively
developed parts of the world, in comparison with the
leading macro-regions in this characteristic, has a
nominal gross domestic product equal to 659 billion
USD.
The situation of Africa is not surprising.
Unfortunately, most of the countries of this continent
have a very underdeveloped economy, which is
proved by extremely low average GDP figures: South
Africa - $ 82.5 billion, East - $ 26 billion, West - 34.5
billion $.Many CIS countries are economically
developed at the same level. But the Russian
Federation stands out a lot: its GDP is 1,638 billion $
in 2019.
Figure 1: Mean nominal GPD of world regions (million $).
Python heat map implementation for East Africa:
pip install pygal
import pygal
import numpy
from pygal.style import Style
custom_style = Style( colors =
('#96c5f1'))
worldmap = pygal.maps.world.World(style
= custom_style)
worldmap.title = ' Mean nominal GPD of
world regions (million $).'
worldmap.add('East Africa',
['bi','dj','zm','zw','ke','mu','mg','mw
','mz','rw','sc','so','sd','tz','ug','e
r','et'])
2.2 Analysis of the Mean GNI of the
World Regions
It is advisable to further consider the average GNI in
different regions of the world (fig. 2) and understand
how this characteristic differs from the previously
considered indicator. The overall situation has not
changed much: in the top three developed economic
regions are still North America with an mean GNI of
5729 billion $, East Asia - 2764 billion $, and
Australia - 1346 billion $.
Countries of Africa and Oceania have the worst
indicators-less than 70 billion $. The initial
conclusions before the analysis were as follows: in
ICAART 2021 - 13th International Conference on Agents and Artificial Intelligence
1250
countries where most of the income is exported
abroad, GNI is less than GDP. On the contrary, if
citizens of a country have high incomes in companies
in other countries, then GNI will exceed GDP.
In practice, the previously made conclusions were
confirmed. A while the difference between gross
domestic product and gross national income may be
significant in theory, in practice it turned out to be
insignificant. For example, if the mean GDP in
Europe is 659 billion USD, the mean GNI is 682.8
billion USD. Relative to these amounts, the difference
is small, but noticeable and is the highest among all
the districts considered.
Figure 2: Mean GNI of world regions (million $).
2.3 Analysis of Cargo Flows between
World Regions
In order not to be unfounded, let's move on to the
analysis of freight traffic between the world macro-
regions. Fig. 3 and Fig. 4 show graphs reflecting the
import and export of all goods between the regions in
monetary terms. The 15 regions into which we united
all states earlier are now presented in the form of 5
larger zones.
The most promising is cooperation between
America and Asia, which can be explained by their
developed economic and trading system. Indeed,
paying attention to the mean nominal GDP and mean
GNI that we examined earlier, it becomes clear that
they are indicators for high trade between these
macro-regions. It should be noted that the import of
products from Asia to America (1611million USD) is
almost 2 times more than in the opposite direction
(980 million USD). Why is this happening?
I propose to carefully consider Fig. 5. The total
tonnage of freight turnover between Asian countries
and the rest of the world tells us that China, Japan,
India, Korea and other large manufacturing countries
in Asia have a powerful resource base - no other
macro-region has such a volume of goods supplied to
other zones: In 2019, more than 350 million tons of
agricultural products alone were supplied, fuel - more
than 1.24 billion tons, metals and minerals - more
than 700 million tons. And this is not all the products
exported by Asia.
The trade partnership between the countries of
Europe and America is no less interesting: fertilizers,
metals and minerals, gas, oil are also in great demand
here. The supply of various goods from Europe to
Australia, as two economically developed regions of
the world, is surprising: the weight of the transported
goods in 2019 (Fig. 5) and its monetary equivalent
(Fig. 3 and Fig. 4) is not large.
How can such a commodity exchange
phenomenon be explained? Previously, the analysis
could only be based on economic indicators, but now
it is necessary to consider the geographical location
of the regions: Europe and Australia are among the
most distant trading partners. Therefore, it is much
more profitable for them to look for suppliers in
countries that are closer. This is what happens
Australia prefers to import goods mainly from Asia
and Oceania. However, why are there deliveries from
Australia to Europe and back at all? This question can
be answered if you pay attention to the types of
products delivered. The basis of trade is coal, which
is not surprising: Australia is a major exporter of this
type of raw material in the world.
It is interesting that African countries with low
macroeconomic indicators supply a lot of goods, for
example, to America (more than a billion tons in
2019). The most exported precious metals, ores and
phosphorites. At the same time, African countries buy
little cargo from other macro-regions. How can this
be explained? The federal budget plays an important
role here. Mauritania, Togo, Eritrea, Burundi,
Lesotho, Swaziland, Liberia and other countries are
among the poorest in the world (GDP less than US $
10 billion). That’s why many states do not have the
ability to purchase goods from abroad.
Trade cooperation between Europe and Asia is no
less important than the rest. Perhaps the most
prominent is the cargo turnover between these macro-
regions. The difference in the quantity of products
delivered between these zones is very noticeable: 213
million tons from Asia to Europe, 721 million tons in
the opposite direction. It is very important to
understand why there are such strong differences in
the volume of deliveries.
To do this, you need to consider the types of
products. The most priority cargoes from Europe to
Asia are forestry products, fossil fuels, agricultural
products, gold, silver and other precious metals,
palladium, platinum.
Computer Modeling of the Dynamics of Interregional Freight Transport Depending on Macroeconomic Indicators
1251
Figure 3: Import of all goods between world regions
(million $).
Figure 4: Export of all goods between world regions
(million $).
If we talk about the opposite direction, the most
common products here are fossil fuels and minerals,
precious metals. Assuming that China is one of the
world's largest gold, silver and other precious metals
mining centers, it was assumed that this type of goods
would occupy an impressive part of the total supply.
In reality it turned out that precious metals are a small
part of the total cargo turnover between the regions,
but Asia places a great emphasis on the supply of this
expensive resource. So, if we consider the monetary
equivalent of exported goods, this indicator is
comparable between both zones (from Asia to
Europe, $ 1,288 million, in the opposite direction, $
1,202 million).
After analyzing the obtained indicators, we can
make a reasonable conclusion that cargo tonnage is
not the only factor that should be considered when
analyzing cargo flows. It is very important to consider
what specific resource is being delivered, because
each product has a different market value. For
example, in our case, the supply of gold and other
precious metals from Asia to Europe is not
comparable in price with forestry products, which
take up most of the supply in the opposite direction.
Thus, cargo turnover directly depends on the
economic development of the cooperating regions,
and the partner States in particular. However, it is
necessary to take into account the geographical
factor: trade relations between Europe and Australia
show us that remote macro-regions can also cooperate
with each other, but due to the strong remoteness of
the zones, we cannot count on a high exchange of
goods. When analyzing cargo transportation between
regions, you need to consider the type of products
delivered: regions that specialize in the production
and processing of any product will always offer their
range to those who need it. In international practice,
this is confirmed by Asian countries, whose products
are in demand on the world market.
3 MUTIAL IMPACT OF CARGO
FLOWS ON
MACROECONOMIC
INDICATORS OF THE
RUSSIAN DISTRICTS
The Russian Federation is a vast country with the big
national resources which has a great economic
potential. Key feature of the country - very convenient
location for trade relations. The country consists of 8
districts: Central Federal district, North- western
Federal district, Southern Federal district, North
Caucasian Federal district, Volga Federal district, Ural
Federal district, Siberian Federal district and Far
Eastern Federal district. Every region has its own
macroeconomic indicators. For the analysis of cargo
flows between districts, the following characteristics
were selected: mining (million rubles), manufacturing
ICAART 2021 - 13th International Conference on Agents and Artificial Intelligence
1252
industries(million rubles), provision of electric
energy(gas) (million rubles), water supply, sanitation,
pollution elimination (million rubles), the carriage of
goods by road transport organizations of all activities
(million tons), freight turnover of road transport
organizations of all activities (million ton - km), the
turnover of wholesale (retail) trade organizations of
wholesale (retail) trade (million tons),retail trade
turnover (million tons), export and import (million
USD), foreign trade turnover (million USD).
Figure 5: Freight traffic between world regions (million
tons).
In order to determine which of these economic
indicators have the greatest impact on cargo flows
between districts, we construct symmetric correlation
matrix (Table 1). As you know, correlation is
equivalent to an approximate relationship between
any values. The linear coefficient of pair correlation
between characteristics is calculated using the
“CORREL” function program in Microsoft Excel.
All elements of the main diagonal are equal to one
the correlation of each macroeconomic indicator
with itself. The rest of the cells are filled with
numbers from the limit [-1; 1], which make it possible
to assess the strength of the relationship between
quantities. Based on the scale of the statistician
Chaddock, it can be argued that values close to -1
reflect a strong negative relationship (strong
feedback), to 1 - a strong positive relationship (direct
relationship), close to 0 - no relationship. If the
correlation coefficient is close to 0.5 or -0.5, then the
selected indicators are in moderate dependence.
Table 1: Correlation square matrix.
Therefore, it is necessary: to find such indicators
that are most dependent on the characteristics
associated with cargo transportation (the correlation
coefficient in modulus is close to 1) and to justify the
identified dependencies.
Freight turnover of road transport is highly
correlated with manufacturing industries. Since the
corresponding coefficient is close to 1, then for this
pair of macroeconomic indicators, a direct
relationship can be identified and explained.
Cargo transportation depends on the volume of
output of the chemical, timber, light, food and other
industries, which are key in the manufacturing
industry of the country. Indeed, when the demand for
goods decreases, the number of products produced is
also adjusted. As a result, the volume of cargo
transportation also decreases. On the contrary, with
an increase in the number of products created, it is
necessary to deliver proportionally more goods,
which entails an increase in the volume of cargo
transportation.
The correlation coefficient between freight
turnover of road transport and the provision of
electric energy is also very close to 1. Therefore, a
directly proportional relationship is also noticeable
between these indicators: with increased demand for
electricity, steam or gas, the need for cargo
transportation will also increase. Since provision
refers not only to direct transmission, but also to the
installation and maintenance of devices, the last two
services account for the greatest load associated with
cargo flows. You can show the relationship as
follows: if you need electricity, you need to bring and
install a system of lines, meters, and poles that
transmits energy from point A to point B. In case of
breakdowns or when the warranty period expires, it is
necessary to replace the equipment, that is, to deliver
new equipment. However, if the demand for
Computer Modeling of the Dynamics of Interregional Freight Transport Depending on Macroeconomic Indicators
1253
electricity increases, the volume of cargo flows will
increase, which will be proof of a direct proportional
relationship.
Water supply, sanitation, and pollution
elimination are strongly correlated with road
transport freight turnover, which highlights the
relationship between them. It is obvious that water
distribution for drinking, industrial and other needs is
carried out mainly by road. Road transport also
provides services for the restoration of various areas
that have been affected by pollution. The relationship
is obvious here.
Thus, we were able to find three macroeconomic
indicators that have the greatest impact on cargo
turnover and cargo flows between districts in the
Russian Federation. Based on the stable relationship
between these characteristics, you can make forecasts
that will reflect changes in the volume of cargo flows
when one of the three indicators decrease or increase.
For example, if there is a reduced demand for water
supply due to the rise in the price of provider services,
the volume of cargo flows will significantly decrease
in this area. However, it is not necessary that a similar
situation will occur in the entire district the lack of
demand for these services is compensated by
increased demand for others.
5 FORECASTING THE EXPORT
VOLUME OF COAL AND
PETROLEUM PRODUCTS
FROM RUSSIAN FEDERATION
We have found that manufacturing industries strongly
affects the volume of cargo flows in the Russian
Federation. Based on this material, we will make a
forecast for the volume of exports of petroleum
products and coal from the country in 2020. There are
many different methods used to predict the volume of
cargo flows. Here are some of them: a method based
on calculating the coefficient of transportability of a
certain group of cargo, a method for predicting cargo
transportation based on specific standards, methods
for predicting the average distance of transportation,
and forecasting the transport and economic balance
We chose correlation-regression analysis. It uses very
clear mathematical tools and a clear algorithm that
works perfectly when predicting characteristics that
primarily have quantitative indicators, considering
the dependence of the effective value on many
factors. Moreover, the method is suitable for working
with time series, which will be discussed later. It is
obvious that the method includes a correlation that
has already been used to establish patterns between
the volume of automobile cargo flows and three types
of macroeconomic indicators, and a regression that
allows reflecting the dependence of the effective
indicator on the factor indicator. The purpose of this
study is to apply the method of correlation- regression
analysis in predicting the volume of cargo flows of
coal and petroleum in the short term (for 2020 year).
I would like to note that a relatively small-time
interval is taken, because this will increase the
accuracy of the forecast made.
So, as a productive factor, we will choose the
volume of petroleum products exported from Russia.
To conduct an analysis for this group, we must
provide data for the retrospective period for the
considered influencing factors (Table 2).
Table 2: Indicators required for correlation-regression
analysis by the export volume of petroleum products.
After selecting the necessary statistical data, we
will start calculating linear pair correlation
coefficients using the "CORREL" function in
Microsoft Excel (Table 3).
Table 3: Calculating Pairwise Correlation Coefficient.
ICAART 2021 - 13th International Conference on Agents and Artificial Intelligence
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The table 4 shows data on the calculated
correlation coefficients of the expert identified
influencing factors with the indicator volume of
petroleum products exports from the Russian
Federation (million tons).
Table 4: Correlation coefficients of influencing factors.
It’s clear that the most closely related factor to the
performance indicator is the volume of oil production
is the volume of oil mining in the Russian Federation
(modulus of the correlation coefficient 0.88 on the
Cheddock scale, the degree of correlation is defined
as high, while the relationship is negative). At this
stage, the correlation component is over.
Let's move on to finding the regression equation
that will help you find the predicted value of the
effective factor .To do this, we will plot the
correlation between export volume of petrol products
and the volume of oil mining (Fig. 6).
Figure 6: The field of the correlation dependence.
After plotting the correlation dependence, the
program outputs the regression equation. At the same
time, the coefficient at X (-0.7349) shows the degree
of influence of export volume of petroleum products
on the volume of oil mining, and the free term 556.34
shows what value the resulting value will take if the
factor indicator is not taken into account in the
regression equation (meaning the dependence of
traffic volume on other factors not described in the
model). And the free term 556.34 shows what
quantity the effective value will take if the factor
indicator is not taken into account in the regression
equation (meaning the dependence of the volume of
traffic on other factors not described in the model).
Next, to calculate the forecast traffic volume, we
substitute the prospective value of the influencing
factor (the volume of oil mining) for 2020 into the
resulting regression equation. In the energy strategy
of Russia until 2035 года, compiled by the former
Minister of energy of the Russian Federation A.V.
Novak, the desired value is 525 million tons.
Substituting this value instead of the variable x, we
get 170.5 million tons-the forecast export volume of
petroleum products from the Russian Federation in
2020.
As the next productive indicator, let's take the
Export volume of coal from the Russian Federation
and make a table with data for the period 2014-2019
(Table 5).
Table 5: Indicators required for correlation-regression
analysis by the export volume of coal for 2014-2019 years.
The next step is to calculate the corresponding
correlation coefficients using the program Microsoft
Excel (Table 6).
Table 6: Correlation coefficients of influencing factors.
The table shows that the most dependent factor
with the effective indicator is the volume of coal
mining in the Russian Federation (the value of the
correlation coefficient is 0.97-on the Cheddock scale,
Computer Modeling of the Dynamics of Interregional Freight Transport Depending on Macroeconomic Indicators
1255
the degree of correlation is defined as a strong
positive.
Тow let's plot the correlation dependence of 1 and
3 indicators (Fig. 7).
We got the regression equation y = 0,57*x-
54,351. To calculate the forecast volume of coal
exported from Russia, substitute the prospective
value of the volume of coal mining for 2020 instead
of the variable x. In the forecast of socio-economic
development of the Russian Federation for the period
up to 2036, compiled by the Ministry of Economic
development of the Russian Federation, the required
value is 420 million tons. Then the Export volume of
coal from the Russian Federation in 2020 will be 185
million tons.
So, formalized methods of predictive
extrapolation are used when predicting certain
processes that can be characterized quantitatively.
Figure 7: The field of the correlation dependence.
When considering the retrospective period, the
trends of the past are considered, analyzed, and the
necessary mathematical methods are applied. This
results in accurate forecast data for a specific
phenomenon. In our research, we used a correlation-
regression algorithm that made it possible to make a
forecast for the selected indicators in the previously
analyzed area. The obtained values - the volume of coal
exports from the Russian Federation in 2020 will be
185 million tons, exports of petroleum products from
the Russian Federation in 2020-170.5 million tons.
6 CONCLUSIONS
So, the article considered various aspects of the
impact of macroeconomic indicators on the volume
of cargo flows.
Firstly, such macroeconomic indicators as the
average nominal gross domestic product (GDP) and
average gross national income (GNI) in each of the
15 selected regions of the world were considered and
analyzed. Next, we examined the volume of cargo
transportation between these zones, correlated it with
the import and export of goods of all types in
monetary terms. As a result, it was concluded that
cargo turnover between regions of the world depends
on many factors. These include the level of economic
development of States, the Federal budget of the
buyer country, the geographical distance of partners,
the type of cargo and its market value.
Secondly, in the Russian Federation, we also
analyzed macroeconomic indicators that could
theoretically be related to the volume of cargo
transportation between districts. In order to identify
dependencies, we have compiled a square correlation
matrix, which was used to identify indicators that
affect the turnover of road transport: output of
products by processing industries, provision of
electric energy, water supply(sanitation) and
elimination of pollution.
Thus, based on these economic characteristics, we
considered situations in which the volume of cargo
turnover between regions of the country may increase
or fall, which marks a success in the field of
interregional forecasting.
Thirdly, we applied the correlation-regression
model and predicted the volume of exports of
petroleum products and coal from the Russian
Federation in 2020. We have deliberately made a
short-term forecast, as this will increase the accuracy
of the forecast. Of course, we must not forget about
the availability of other methods that are actively used
in forecasting. However, our method is particularly
valuable it allows you to predict the required value
based on quantitative characteristics. The algorithm
also provides the ability to reflect the relationship
between the effective value and influencing factors.
In total, forecasting traffic flows is a very
significant element of the economy. A well-
functioning freight transport system is one of the most
important elements of any successful economy. This
process allows you to: predict the prospects for the
development of the entire system as a whole when
distributing goods in the future; rationally use and
distribute available resources; improve the quality of
transport services, increasing the competitiveness of
the organization; minimize the risk of bankruptcy;
make innovations in the enterprise; improve control
over the processes of transporting goods.
ICAART 2021 - 13th International Conference on Agents and Artificial Intelligence
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