=Paper=
{{Paper
|id=Vol-2098/paper36
|storemode=property
|title=Interactive Simulation Software for
Multi-Regional Model of Freight Transportation
|pdfUrl=https://ceur-ws.org/Vol-2098/paper36.pdf
|volume=Vol-2098
|authors=Andrey Velichko
}}
==Interactive Simulation Software for
Multi-Regional Model of Freight Transportation==
https://ceur-ws.org/Vol-2098/paper36.pdf
Interactive Simulation Software for
Multi-Regional Model of Freight Transportation
Andrey Velichko
Institute for Automation and Control Processes,
Far Eastern Federal University, Vladivostok, Russia
vandre@dvo.ru, velichko.as@dvfu.ru
Abstract. The paper develops a know mathematical model of multi-
regional flows of goods. We determine the most probable spatial distri-
bution of flows. Transportation costs depend on the distance between
the regions within the “gravity model” approach. We use the shortest
way length in a transportation network as a measure of these distances.
The underlying mathematics of the model is a convex mathematical pro-
gramming. The problem is represented as nonlinear minimization of a
function with linear constraints which is solved numerically. Developed
software is implemented for interactive modeling and visualization of the
problem solution. The code is implemented on a high-performance cloud-
server platform and consist of modules for simulation, visualization and
control. Asynchronous http-queries are used for interaction between the
platforms. For the data exchange between these modules a declarative
model in the JSON format is implemented. The paper demonstrates a
practical problem solution for the gasoline freight transportation for Pa-
cific Russia region and the simulation software usage.
Keywords: Spatial · Multi-regional · Transportation · Gravity · En-
tropy · Network · Pacific Russia
1 Introduction
Modeling multi-regional freight transportation flows for a national economy is
the important problem for studying a level of economic integration among regions
of the country. In a market economy such flows are formed in accordance with
the economic agents own free interests. We assume that such flows are self-
organized in a transportation network. In a case of incomplete statistics about
all the bilateral flows among regions it is possible to use the equilibrium network
approach which allows to get the most probable spatial distribution of flows.
The power of such equilibrium models is that they can be used for simulation
Copyright c by the paper’s authors. Copying permitted for private and academic purposes.
In: S. Belim et al. (eds.): OPTA-SCL 2018, Omsk, Russia, published at http://ceur-ws.org
420 A. Velichko
the flows distribution among regions in a ‘comparative statics’ approach when
technical and economic parameters of the model are changed.
It seems that the issue of multi-regional flows simulation was pioneered by
Leont’ev [9]. Anderson and Wincoop [2] demonstrated how the multi-regional
general multi-product equilibrium model from microeconomics theory can ex-
plain the outflows and inflows of goods. They show that the fundamental of
trade flows volume that emerge among territories are very similar to the “grav-
ity” rule in physics and this fact and was widely applied for international trade
and the theory and practice of regional economics. Wilson and others [10] has de-
veloped the entropy modeling approach to take into account the incompleteness
of information in the application to an equilibrium modeling for complex commu-
nication systems which can be applied to simulate multi-regional multi-product
flows. The gravity modeling approach and the principle of entropy maximiza-
tion [3] are interrelated in many ways. Further development of Wilson’s approach
was conducted by Boyce and others [3]-[7]. The researchers use the mathematical
models for practical application of multi-regional trade flows analysis regarding
the configuration of the transportation network of USA with multi-modality of
flows concerning various modes of transport.
The paper is organized as follows. First we describe a mathematical model
for solving the problem mentioned above. Then a web-based computer service for
more convenient analysis and interactive simulation of the problem is presented.
And afterwards we provide an example of the proposed mathematical model and
software for the multi-regional flows modeling for Pacific Russia.
2 “Gravity” Principle in the Model of Freight
Transportation
This section sketches the model, solution for the correspondent mathematical
problem and gives its visualization for gasoline freight transportation for Pacific
Russia region.
2.1 Mathematics of Freight Transportation Model
The model consider the economy of k regions producing and consuming a prod-
uct. Let zij is an unknown volume of a product delivered from region i to the
region j, i, j = 1, . . . , k. The flow zii is not necessarily assumed to be zero.
Strictly positive values of zii corresponds the part of the production of a region
that is consumed in this region.
The total flow from region i to j can be interpreted as the total consumption
Pk
of the product in the region j which equals zij . We define Vj as a known
i=1
inflow (import) of a product to the region j given by official statistics. Obviously
Pk
Vj = zij . Total outflow (export) of a product from the region i to other regions
i=1
Interactive Simulation Software for Freight Transportation 421
k
P
is the total production of a product in the region i. The value zij is assumed
j=1
to be known from official statistics, and let it be defined as Wi .
The production and consumption of a product defined above is subject to
evident balance equations
X X k X
X k
Vj = Wi = zij . (1)
j i i=1 j=1
Equations (1) put an additional restriction on the given values of Wi and Vj so
the total flows should be balanced in the considered system of regions.
However since the system of regions cannot be closed and we can observe
flows of the assumed regions with others, the aforementioned balance (1) based
on statistics would not be observed. This means that for the system of regions j =
1, . . . , k there are flows of a product between these regions and other undefined
“external” regions. The problem is complicated by the fact that neither the total
inflow or outflow of such “external” regions are known. Obviously in this case
the model requires modification.
To solve this problem let’s aggregate the “external” regions to (k + 1)-th
region and let’s consider additional flows zi k+1 and zk+1 j which are unknown
but moreover can be identified.
Freight transportation is carried out by economic agents under the influence
of the transportation costs which depend on the distances between regions. These
distances are estimated as the shortest way length in a transportation network
by available modes of transport. Consider the gravity model for transportation
costs which can be represented by vij = exp(−d Tij ), where vij is ‘a priori’
defined the flow of products from region i to j, Tij is an assessment of the
distance between regions i and j, and d is the parameter that are responsible
for the flow sensitivity to distance transportation for a product. Parameter d is
non-negative which means that the higher the value of the distance, the smaller
an amount of flow between the regions i and j is. It is additionally assumed that
vii = 0 for Tii = 0 and vij = vji because we assume that Tij = Tji .
Calibration of such non-negative parameter d with actual official statistics is a
separate problem of applied statistics. This assessment is carried out by methods
such as least squares applied to the linear regression model ln vij = α−d Tij +εij
for all li, j = 1, . . . , k and i > j where εij is a normally distributed residuals of
the regression for all i and j.
Paper [10] it is considered an approach of modeling flows in a communication
networks corresponding to the principle of the most probable values of the spatial
distribution of flows. This approach assumes the conditions of incomplete infor-
mation when only some balance equations for these flows are given. Adaptation
of this approach for the model of multi-regional freight transportation makes it
k+1
P
necessary to minimize the nonlinear functions of the form zij ln(zij /vij )
i,j=1,i6=j
on the set of unknown flows zij .
422 A. Velichko
The presence of such features makes it necessary to specify strictly positive
freight transportation zij which is modeled by specifying lower restrictions on
flows by preassigned small parameter ε > 0.
Then we solve a nonlinear optimization problem with already considered bal-
ance equations as linear constraints and the objective function that is motivated
by the most probable flows approach in a case of incomplete information about
the communication system [10]:
k+1
X
zij ln(zij /v̂ij ) → min , (2)
{zij }
i,j=1,i6=j
where v̂ij = exp(−dˆTij ), dˆ is known estimates of the parameter. Constraints of
the problem are given further:
k+1
X
zij = Vj , j = 1, 2, . . . , k, (3)
i=1
k+1
X
zij = Wi , i = 1, 2, . . . , k, (4)
j=1
zij ≥ ε > 0, i, j = 1, 2, . . . , k + 1. (5)
2.2 The Solution and Visualization
Implemented computer software for multi-regional freight transportation simu-
lation is used to determine the equilibrium multi-regional freight traffic in the
transportation network of railway, road and marine transport of the Pacific Rus-
sia regions. The data is used as input data of Rosstat official statistical hand-
books of different years “The regions of Russia. Socio-economic indicators of
the multi-regional trade”. The main products (commodities) are foodstuff, fuel,
goods for technical purposes.
Table 1. Flows of gasoline from the Russian Pacific regions
Regions Outflow (produc- Inflow (consumpi-
tion), thous. tons tion), thous. tons
1 11.2 459
2 507 63.5
3 0.01 138
4 0.01 45.2
5 0.01 42.4
6 0.2 0.8
7 0.01 21.6
8 0.01 37.2
9 0.01 0.2
Interactive Simulation Software for Freight Transportation 423
Primorskiy kray - 1, Khabarovskiy kray - 2, Amurskaya oblast’ - 3, Evreyskaya
avtonomnaya oblast’ - 4, Respublika Sakha (Yakutiya) - 5, Magadanskaya oblast’
- 6, Sakhalinskaya oblast’ - 7, Kamchatskiy kray - 8, Chukotskiy avtonomnyy
okrug - 9.
As the centers of production and consumption corresponding administrative
centers of 9 Pacific Russia regions are considered: Primorskiy krai (Vladivos-
tok), Khabarovskiy krai (Khabarovsk), Amurskaya oblast’ (Blagoveshchensk),
Evreyskaya avtonomnaya oblast’ (Birobidzhan), Respublika Sakha (Yakutsk),
Magadanskaya oblast’ (Magadan), Sakhalin region (Yuzhno-Sakhalinsk), Kam-
chatskiy krai (Petropavlovsk-Kamchatsky), Chukotskiy avtonomnyi okrug (Anadyr).
Estimates of the distances between regions are shown in Table 2 which cor-
respond to the shortest paths between the administrative centers of the regions
in aggregated transportation network of railway, road and marine transport of
Pacific Russia.
Fig. 1. Aggregated transportation network of Pacific Russia
424 A. Velichko
Table 2. Estimates of the distances between the regions of Pacific Russia, thous.
km
Regions 1 2 3 4 5 6 7 8 9
1 0 0.76 1.41 0.94 3.31 2.49 0.99 2.49 4.49
2 0.76 0 0.78 0.18 2.55 2.53 1.03 2.53 4.53
3 1.41 .0.78 0 0.60 2.04 3.18 1.81 3.18 5.18
4 0.94 0.18 0.60 0 2.38 2.71 1.21 2.71 4.71
5 3.31 2.55 2.04 2.38 0 1.74 3.59 2.74 4.74
6 2.49 2.53 3.18 2.71 1.73 0 1.5 1.0 3.0
7 0.99 1.03 1.81 1.21 3.59 1.5 0 1.5 3.5
8 2.49 2.53 3.18 2.71 2.74 1.0 1.5 0 2.0
9 4.49 4.53 5.18 4.71 4.74 3.0 3.5 2.0 0
Primorskiy krai - 1, Khabarovskiy krai - 2, Amurskaya oblast’ - 3, Evreyskaya
avtonomnaya oblast’ - 4, Respublika Sakha (Yakutiya) - 5, Magadanskaya oblast’
- 6, Sakhalinskaya oblast’ - 7, Kamchatskiy krai - 8, Chukotskiy avtonomnyi
okrug - 9.
Table 3. Simulation result for gasoline transportation, thous. tons
Regions 1 2 3 4 5 6 7 8 9 10
1 0 3.13 3.98 1.84 1.12 0.02 0 0.36 0.75 0
2 314.8 0 92.55 14.53 36.31 0.69 15.88 32.04 0.18 0.01
3 0.01 0 0 0 0 0 0 0 0 0
4 0.01 0 0 0 0 0 0 0 0 0
5 0.01 0 0 0 0 0 0 0 0 0
6 0.1 0.04 0.04 0.02 0 0 0 0 0 0
7 0.01 0 0 0 0 0 0 0 0 0
8 0.01 0 0 0 0 0 0 0 0 0
9 0.01 0 0 0 0 0 0 0 0 0
10 144.07 60.32 41.43 28.8 4.96 0.09 5.36 4.41 0.01 0
Primorskiy kray - 1, Khabarovskiy kray - 2, Amurskaya oblast’ - 3, Evreyskaya
avtonomnaya oblast’ - 4, Respublika Sakha (Yakutiya) - 5, Magadanskaya oblast’
- 6, Sakhalinskaya oblast’ - 7, Kamchatskiy kray - 8, Chukotskiy avtonomnyy
okrug - 9, Other regions - 10.
Table 3 shows the result for the gasoline freight transportation simulation as
solution of the problem described above as the spatial distribution of the most
probable flows in the system of the Pacific Russia regions and other “external”
regions. These “external” regions are aggregated in one additional region that is
added in Tab.3 in the last row under the number “10”.
Consider the transportation of motor gasoline between the regions of Pacific
Russia (Figure 2, Table 4). From the simulation result shown in Tab.3 it is
possible we can conclude that rather large quantities of gasoline is transported
from Khabarovskiy krai to Primorsky krai, smaller quantities are transported
to Amurskaya oblast’, and a very little amounts are transported to Respublika
Sakha (Yakutiya) and Kamchatskiy krai, as well as to Evreyskaya avtonomnaya
oblast’ and Sakhalinskaya oblast’. Second conclusion is that there is a huge
Interactive Simulation Software for Freight Transportation 425
amount of gasoline that is needed to be transported from other “external” regions
that are not regions of the Pacific Russia. Gasoline was transported mainly to
Primorsky Kray, at less extent to Khabarovskiy krai, Amurskaya oblast’ and
Evreyskaya avtonomnaya oblast’.
The visual presentation of the data from Tab. 3 is shown in Fig. 2.
Fig. 2. Simulation result for gasoline transportation
3 Software Implementation
Software that implements the described above mathematical model is realized. It
is used to find the equilibrium multi-regional freight traffic in the transportation
network system of regions. An indicator of “connectedness” of the regions for a
2(z +z )
specific industry or aggregated for all products is defined as Lij = Ei +Eijj +Iji
i +Ij
where zij is an outflow from the region of i to the region j and Em is a total
export of a product from the region m to all other regions, and Im is total
imports to the region m from all other regions.
Output results of the program are calculated equilibrium values of the freight
traffic between transportation network nodes, the matrix of multi-regional flows
and “connectedness’ matrix of regions.
Model implementation is carried out in the MPL language [1] and Octave [5]
on the SuperServer 6037R-72RFT+ server platform by SuperMicro company.
A generalized architecture of the cloud service is presented in the Fig. 3. The
426 A. Velichko
software consists of three modules: a module of freight transportation simulation,
a control module and a visualization module.
Fig. 3. Architecture of software modules
3.1 Control Module
The freight transportation simulation module and the visualization module are
the independent sybsystems located on different servers. To provide interaction
between them an additional module of control is required. . The main task of the
control module is to get and transfer information from the simulation module and
the visualization module in a previously defined format that is understandable
for them. A convenient data format for the simulation module is a matrix rep-
resentation; a convenient data format for visualization is a graph. Both formats
are specialized and useful for specific tasks. Therefore, we need an intermediate
format for exchanging information between them. For this aim, we have devel-
oped a declarative format for freight transportation 14. That format consists of
three base types of objects: products, zones and communications. A declarative
representation of the objects is realized with JSON format. A structure of each
type and links between them are provided in the Fig. 4.
The control module uses the http-protocol and asynchronous requests. The
visualization module sends an asynchronous http-request to the control module
Interactive Simulation Software for Freight Transportation 427
Fig. 4. The declarative model of freight transportation
for getting input data from the simulation module. The control module ini-
tiates an http-request for getting or calculating data about a state of freight
transportation. The simulation module forms data in the declarative represen-
tation and then sends an answer to the visualization module using the control
module. Then the visualization module builds a graph of freight transportation
by obtained data. The control module is different from existing methods for
multi-regional flows because it provides interaction between two environments
(environment of calculations and environment of visualization) which are on the
different platforms.
3.2 Visualization Module
The visualization module gives a possibility to represent complicated matrix
data about freight flows visually (graphically) in form of oriented graph. Arcs of
that graph are freight flows and vertexes are points of destination of these flows.
The visualized transport net allows users to see results of the modeling and
to change them interactively, by editing parameters of vertexes and arcs by a
program interface.
The visualization module displays different variants of the oriented graph in
depend of needed parameters. For this on base of the declarative model there are
formed automatically control elements for the graph of freight transportation [6].
By these control elements a user can select type of communication it is needed
to display in sampling. The visualization module have some advantages, main
of them are: implementation of the module is done on the cloud platform and
software is accessible for users via the Internet; the visualization module allows
428 A. Velichko
users to change interactively parameters of the model and to see results in the
real-time mode.
Interactive editing of the graph supposes a change of the parameters vertexes
and arcs of the graph. (Fig. 5). In accordance with user modification the dynamic
asynchronous request is sent to the simulation module server via the control
module and as a results a new declarative model of trade flows will be received
and graph visualization will be changed.
Fig. 5. Interactive simulation service
The parametric sampling and the visualization of only part of information
matters a lot for clarity of the received results especially if parts of these results
Interactive Simulation Software for Freight Transportation 429
are entirely (or partially) are independent from each other. Addition graphical
parameters are used for the visualization of the transport flows graph: a color
and thickness of arcs. The color characterizes a type of communication of flows.
The thickness characterizes a volume of flows’ loads.
Implementation of the visualization module as a cloud service gives a con-
venient way to use and display the results of mathematical modeling for many
users via the Internet.
4 Conclusion
The paper sketches the “gravitation” model for multi-regional trade. The un-
derlying mathematical problem of the model is based on the approach of finding
the most probable spatial distribution of flows in a case of incomplete infor-
mation about the communication system. The nonlinear convex optimization
problem with linear constraints is needed to be solved. The paper demonstrates
the simulation of multi-regional freight transportation of gasoline for the Pacific
Russia.
The software package is a cloud service, and it consists of three main mod-
ules. First, simulation module of trade flows based on the mathematics of the
model. Second, the control module, and the third, module for visualization. Mod-
ules for control and visualization are implemented on the cloud platform using
a multi-agent approach. The interaction between the cloud platform and the
high-performance computing platform is organized with dynamic asynchronous
requests using a http-protocol.
The software is designed for professionals dealing with the problem of an-
alyzing multi-regional flows of products and planning the strategical plans of
regional economic development. Overall methodology and conclusions can be
used for multi-regional trade simulation for other regions and municipalities.
Due to a huge dimension of a problem and high practical computational
complexity further research could be managed in a way of special numerical
algorithms design including parallel ones. The use of a high-performance com-
puting platform could be efficient in a case of a huge number of constraints in
the considered mathematical problems.
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