Multimodal multicommodity teks mangayun international

Telecommun Syst (2009) 40: 39–54
DOI 10.1007/s11235-008-9129-6

Multimodal, multicommodity international freight simultaneous
transportation network equilibrium model
Mohamad K. Hasan

Published online: 8 October 2008
© Springer Science+Business Media, LLC 2008

Abstract An implementation of the International Freight
Simultaneous Transportation Equilibrium Model (IFSTEM)
that developed in United Nations Economic and Social
Commission for Western Asia (ESCWA), to the goods trade
through the ports and lands of Jordon, Syria, and Lebanon is
presented. Although some socio-economic variables, which
are not available, were required for IFSTEM model calibration, some reasonable assumptions were made and it was
good enough to draw the following main findings: the IFSTEM model was able to replicate the observed path and
O-D pair goods flows for year 2001 through its initial solution; the IFSTEM final solution suggested that the path
distribution for most observed O-D pairs flows is not optimal due to the exporters depend only one some measure
of attractiveness in their path choice and it should be redistributed to have a great saving in the total freight cost; the

IFSTEM can be consider as a good decision support tool
that is able to evaluate the value of any scenario that can
be reflected through any change in the costs and/or times of
its link cost function or any change in the socio-economic
variables, as the case of year 2007 prediction.
Keywords International multimodal multicommodity
network · Simultaneous transportation network equilibrium
model · Integrated transport network · Integrated transport
system · Globalization and regional integration ·
International freight transport · Exporters · Importers

M.K. Hasan ()
Department of Quantitative Methods and Information Systems,
College of Business Administration, Kuwait University,
P.O. Box 5486, Safat 13055, Kuwait
e-mail: mkamal@cba.edu.kw

Introduction
The United Nations Economic and Social Commission for
Western Asia (ESCWA) is one of five UN regional economic commissions in the world that was established in

1973. While other regional commissions cover entire continents, ESCWA is made up of only 13 member countries,
namely: Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon,
Oman, Palestine, Qatar, Saudi Arabia, Syria, United Arab
Emirates and Yemen.
The main objective of the ESCWA secretariat is to increase the effectiveness and efficiency of sustainable social
and economic development processes in Western Asia by
developing and strengthening regional cooperation and integration. One of the most important issues within this context
is intraregional trade. In 1997 exports from ESCWA member
countries totaled US $124 billion (2.36 percent of world exports); the six Gulf States, namely, Bahrain, Kuwait, Oman,
Qatar, Saudi Arabia and the United Arab Emirates, accounted for 87 per cent (US $108 billion) of the region’s
export total. During the same year, imports to the ESCWA
region amounted to US $109.5 billion (2.36 percent of world
imports), with the Gulf countries accounting for 69.44 per
cent (around US $75.96 billion) of the total for the region.
The average export-import ratio was 1.42 for the Gulf countries and 0.48 for the other seven ESCWA members (those
with more diversified economies). Trade between the ESCWA members remained relatively low: between 1990 and
1997 their export share fell from 10.9 to 8.6 percent and
their import share rose from 9.1 to 10.4 percent. A similar situation is observed for other Arab countries. The Arab
Monetary Fund and other financial institutions made a considerable effort to increase these percentages by establishing
a US $500 million fund to finance intraregional trade. However, the demand for such support was weak owing to the

40

complexity of border procedures and formalities and the imposition of high tariffs between ESCWA member countries
[4]. The same trends continue to prevail until the present
time.
The ESCWA secretariat recognizes the important role
transport plays in supporting sustainable development
processes. The integration of transport networks, the easing of border procedures and formalities, and the reduction
or elimination of tariffs are vital to facilitating the movement of goods and passengers within and between ESCWA
member countries and between those countries and the rest
of the world. Effective transport connections can serve markets and communities and create or strengthen links between
centers of production and consumption. In addition, facilitating regional and international transport flows through the
member States is likely to contribute significantly to improving the international trade competitiveness of local industrial
and agricultural products and services.
In the present context of increasing globalization, the ESCWA secretariat is playing a key role in promoting an integrated transportation system linking all the countries of
the region, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon,
Oman, Palestine, Qatar, Saudi Arabia, Syria, United Arab
Emirates and Yemen. The system is designed not only
to facilitate intraregional trade and promote greater economic integration, but also to connect the ESCWA members

with neighboring countries and regions and further integrate
Western Asia into the global economy. This is an essential
component of efforts to achieve sustainable socio-economic
development and prosperity in an era characterized by interconnectedness.
During the twentieth session of ESCWA, held at United
Nations House in Beirut on 27 and 28 May 1999, the Commission, in its resolution 221 (XX) of 27 May 1999, took
note of the contents of the summary reports submitted by the
subsidiary bodies of ESCWA to that session, including, by
implication, the statement on the adoption and development
of an Integrated Transport System in the Arab Mashreq (ITSAM). Conceptually, the contribution of the ESCWA secretariat to the development of ITSAM comprises the following
three basic components:
1. ITSAM-NETWORK, an integrated transport network;
2. ITSAM-INFOSYS, an associated information system;
3. ITSAM-FRAMEWORK, a methodological framework
for issue analysis and policy formulation.
The above-mentioned statement included a declaration
by member States regarding the development of ITSAM
and the adoption of an integrated transport network for
that system in the region. The first edition of the ITSAMNETWORK map, incorporating the statement in its entirety,
was officially approved and presented at the Commission’s

twentieth session and was published in June 1999.

M.K. Hasan

With regard to ITSAM-INFOSYS, the second major
component, several schemes at various stages of maturity
have been proposed by regional institutions, and prospects
for their implementation will be determined. A working paper by Farahat [10] provides details on the purpose, scope
and structure of this information system.
A comprehensive methodological framework was required in order to develop the integrated transport system
in the region and achieve specific goals in line with a comprehensive and integrated analytical method that can be applied in a systematic and sustained manner. As part of its
program of work for the biennium 2000–2001, the Transport Section of Globalization and Regional Integration Division has therefore begun preparation of that methodological
framework, in two volumes. Volume I [5] is entitled General Outline and Main Features and volume II [6] is entitled A Policy-sensitive Model for Predicting International
Freight Flows (Trade). Volume I draw a broad outline of the
proposed methodological framework and review the main
hypotheses, variables, relationships and groups used in the
analysis, together with the options, anticipated effects and
prediction process. It also deals in some detail with the main
features of the proposed methodological framework and development priorities.
Volume II reviews a mathematical model for predicting international freight flows (trade) in the Arab Mashreq.

The review describes the basic hypothesis of the model, the
method used to represent the integrated transport network in
order to apply that model; and the results obtained, using an
example that demonstrates the capacity of the model to predict freight flows over the network and analyze policies and
options for improving performance levels and increasing demand for international freight transport.
Volume I was prepared by Dr. Nabil Safwat, Chief of
the ESCWA Transport Section and immediate supervisor of
the study. Dr. Safwat was also involved in the preparation
of Volume II, together with Dr. Mohamad K. Hasan, First
Economic Affairs Officer in the Section from March to July
2000.
To increase the economic integration between some ESCWA countries, the ministers of Transport in Jordon, Syria,
and Lebanon requested from ESCWA to prepare a study
about the economic feasibility to facilitate the trade of goods
through the ports and land of the three countries. A team to
facilitate transport and trade from the Transport Section of
Globalization and Regional Integration Division under the
supervision of Dr. Nabil Safwat, Chief of the ESCWA Transport Section prepared the following three studies:
1. An Economic Feasibility Study to Facilitate Goods Trade
through the Ports and Lands of Jordon, Syria, and

Lebanon [5].
2. Information System for the Integrated Transport System
In The Arab Mashreq: Information of the Existing Sit-

Multimodal, multicommodity international freight simultaneous transportation network equilibrium model

uation for Good Trade through the Ports and Lands of
Jordon, Syria, and Lebanon [6].
3. Methodological Framework for The Integrated Transport
System In The Arab Mashreq (Volume III): Methodology For Future Prediction And Economic Evaluation of
Different Scenarios of Goods Trade facilitation through
the Ports and Lands of Jordon, Syria, and Lebanon [9].
In ESCWA, [9] study, the authors sequentially used only
the trip generation and the trip distribution models of the
International Freight Simultaneous Transportation Equilibrium Model (IFSTEM) that was developed in ESCWA, [6]
(see also [22]) in their prediction methodology.
The prediction of multi-commodity freight flows on a
multimodal network has attracted substantial interest in recent years. The prediction of passenger flows on multimodal
urban transportation networks has been studied extensively,
and many of the research results have been applied at the

practical level [3, 13–21, 23]. Boyce [1] gives very good reviews and prospects for network equilibrium models. Boyce
[2] gives a good view for future research on urban transportation network modeling. However, the study of freight
flows at the national, regional and international levels, perhaps owing to inherent difficulties and complexities, has
received less attention. A good review of freight transport
modelling may be found in [11]. Ham et al. [12] develop an
interregional commodity flow model, incorporating input–
output relationships, and integrated with a transportation
network model. The model was implemented for the US
highway and railway networks to forecast flows of 11 commodity sectors. The model was solved using a partial linearization algorithm, and provided estimates of intraregional
and interregional flows and link flows by mode for each sector.
In this research a fully implementation of the IFSTEM
methodology, as it is described in [6] and [22], to the goods
trade through the ports and lands of Jordon, Syria, and
Lebanon are carried out. The analysis and the demonstration
of the IFSTEM ability to simulate the existing situation and
future prediction of goods flows are presented. The model
capability to evaluate different scenarios that will facilitate
goods grade through the ports and lands of Jordon, Syria,
and Lebanon are also presented.

1 The existing goods flows system through the ports
and lands of Jordon, Syria, and Lebanon
According to the Information of the Existing Situation for
Good Trade through the Ports and Lands of Jordon, Syria,
and Lebanon study [8], the existing goods flows network,
as shown in Fig. 1, consists of six origin-destination (O-D)
pairs. Each O-D has its own paths as shown in the Appendix
Table A.1. For each path, there are specifications for:

41

1. Loading ports represent the origin of the goods
2. Unloading port represents the first entry point for the
good to any of the three counties
3. Shipment type represents the procedure type that will be
required at the unloading port
4. Countries pass through represents the countries that the
good will be transported by land through it to reach its
destination
5. Borders points that goods pass through it during its land

transport route to reach its destination
6. Observed goods flows in tons for year 2001.
Table A.2 shows the legal cost for each path that consists
of the following:
1. Maritime transport cost from the loading port to the unloading port
2. Total costs inside the unloading port that consist of port
service costs and maritime dealer costs
3. Land transport cost from the unloading port to the destination
4. Border producers and fees costs at each land border
point.
Table A.3 shows the time taken to deliver the goods from
the origin to the destination for each path and it includes:
1. Maritime transport time from the loading port to the unloading port
2. Total procedures times inside the unloading port to release goods
3. Land transport time from the unloading port to the destination
4. Total border producers time at each land border point to
release goods.
Table A.4 shows the illegal costs for the three countries
in the unloading ports and land border points in case of import or transit goods. These costs represent how much the
goods owner or the shipping dealers pay to the employees in

different authorities in terms of money or gifts to facilitate
the good release procedures.

2 IFSTEM model description
The IFSTEM model is a simultaneous trip-generation, tripdistribution, modal-split and traffic-assignment model that
most appropriately illustrates the behaviour of exporters and
importers of different commodities over an international
multimodal network. IFSTEM is constructed in such a way
that commodity exporters make decisions about where and
how to transport their freight; choices are made regarding
destination, mode, trans-shipment and routing. The IFSTEM
model can be described as follows (for more detailed description see [6] and [22]):

42

M.K. Hasan

Fig. 1 Original network for goods flows through the ports and lands of Jordon, Syria, and Lebanon

For each commodity r ∈ C,



Sir = max 0, ln
exp(−θir urij + Arj )

∀i ∈ I r

j ∈Dir

Gri = α r Sir + Eir ∀i ∈ I r
⎧
r r
r
⎨ Gr  exp(−θi uij r+Ar j ) r
if imc ≥ 0,
i
k∈Dir exp(−θi uik +Ak )
T rij =
∀ij ∈ R r
⎩
0
otherwise

= urij if Hpr > 0,
∀p ∈ Pijm(r) , ij ∈ R r
Cpr
r
r
≥ uij if Hp = 0
where
(N, A) = A multimodal multi-commodities network
consisting of a set of N nodes and a set of A




links and N = r∈C N r and A = r∈C Ar
C = Set of all commodity types


M = Set of all mode types, M = r∈C m(r)
O = Set of all Administrative and Logistical Operations


ALO types, O = r∈C o(r)
m(r) = a set of mode types possible for commodity
type r (combinations of road, rail, air
and/or maritime modes)
o(r) = a set of ALO types for commodity type
r (combinations of export, import, transit-in,
transit-out, pre-export, pre-import and/or

transfer operations)


I = Set of origin (export) nodes I = r∈C I r and N r ⊇ I r
I r = Set of origin (export) nodes for commodity r
i = An origin (export) node in the set I r
Dir = Set of destination (import) nodes that are feasible
for importing commodity r from origin i
j = A destination (import) node in the set Dir
R r = Set of origin-destination pairs (ij ) for commodity r
R = Set of all origin-destination
pairs (ij ) in the system,


where R = r∈C R r
p = A simple (i.e., no node repeated) multimodal path
(i.e., it may include a combination of links with
different modes m(r)) for commodity r
in the network (N r , Ar )
Pijm(r) = Set of simple paths that can be used to transport
commodity r from origin i to destination j using
only m(r) modes of transport
P r = Set of simple
the network (N r , Ar ),

paths inm(r)
r
i.e., (P = ij ∈R r Pij )
a = A link in the set A. Each link is identified by (k, l, q),
i.e., the link connects node k to node l by
mode/operation q
Sir = the accessibility of the exporter of commodity r
at origin i
Gri = the number of tons of commodity r exported from
origin i
Tijr = the number of tons of commodity r exported from
origin i to destination j

Multimodal, multicommodity international freight simultaneous transportation network equilibrium model

Hpr = the flow of commodity r on multimodal path p
Eir = a composite measure of the effect the socio-economic
variables, which are exogenous to the transport
system, have on the number of tons of commodity
r exported from origin i
r
Eli = the value of the lth socio-economic variable that
influences the number of tons of commodity r
exported from origin i
ql (Elir ) = a given function specifying how the lth
socio-economic variable, Elir , influences
the number of tons of commodity
r exported from origin i
Arj = a composite measure of the effect that
socio-economic variables exogenous to the transport
system have on the number of tons of commodity
r imported at destination j

r
r
Arj = W
w=1 θiw gw (Awj )
r
Awj = the value of the wth socio-economic variable that
influences the number of tons of commodity
r imported at destination j
gw (Arwj ) = a given function specifying how the wth
socio-economic variable Arwj influences
the number of tons of commodity r imported
at destination j
Cpr = the total perceived delivery cost for commodity
r transported from export origin node i to import
m(r)
destination node j on any multimodal path p ∈ Pij
The quantities α r and αlr for l = 1, 2, . . . , L and the quantir for w = 1, 2, . . . , W are coefficients
ties θir (θir > 0) and θiw
to be estimated.
The model decision variables for commodity r ∈ C are
Sir , Tijr and Hpr , which are interrelated through the minimum
delivery cost urij . These interrelationships allow a simultaneous prediction of trip generation, trip distribution, modal
choice and trip assignment.

3 IFSTEM modelling procedures and assumptions
for goods flows system through the ports and lands
of Jordon, Syria, and Lebanon
3.1 IFSTEM network representation
To adapt the goods flows network of Fig. 1 for IFSTEM
methodology we modified this network as shown in Fig. 2
where we considered the following:
1. Each destination node connected to a number of dummy
destinations; depend on the maximum number of paths
that reach this destination, through dummy links with
link cost equal to zero on all of them.
2. Each origin node connected to the same number of
dummy destinations as its corresponding destination

43

through dummy links with link cost equal to very big
value.
For example, Amman destination (node number 500) is
connected to six dummy destinations nodes number 501,
502, 503, 504, 505, and 506 with six dummy links. And
the origin Black Sea (node number 810) is connected to
the same destinations 501, 502, 503, 504, 505, and 506
with six dummy links. The links cost of any of the links
500 → 501, 500 → 502, 500 → 503, 500 → 504, 500 →
505, 500 → 506 is equal to zero, and the link cost of any of
the links 810 → 501, 810 → 502, 810 → 503, 810 → 504,
810 → 505, 810 → 506 is equal to 999999999.
By this network presentation each original path for any
original O-D can be consider as a new origin-destination
pair that has exactly two paths, the first one from the original origin to the dummy destination and the second one is
from the original origin to the same dummy destination. For
example, the original path 1 of the original O-D 1, 810-500,
that represented by the sequence
• 810 → 421 → 413 → 511 → 500 will be the new O-D
810-501 that has exactly two paths,
• 810 → 421 → 413 → 511 → 500 → 501 and 810 →
501.
Considering the network representation in Fig. 2 as the
basic network and using the same concepts of network representation of ESCWA, 2000b study, we were able to create
a multimodal network (sea and road) for each new O-D pair
(original path) that include four Administrative and Logistics Operations (ALO) (export, import, transit in, and transit
out). There is no interaction between any O-D pairs, that is,
the goods are forced to move through its own O-D network
only which represent the physical movements of goods.
3.2 Demand models assumptions
We assumed that only one general commodity is transported
from the origins to destinations of the network in Fig. 2.
Recalling the IFSTEM description in Sect. 2, we have
two demand models, trip generation and trip distribution. To
have a real calibration for these models, we should have l
socio-economic variables Eli , for each exporter, that influences the number of tons of the general goods that exported
from that origin, i.e., at each of the following origins: Black
Sea (node 810), West Mediterranean Sea (node 710), North
and North East Europe (node 910), North and South America (node 310), Far East and South East Asia (node 210).
These socio-economic variables were not available and we
assumed the following to compute the exogenous variables
Ei for each origin i from the equation
Ei =

L

l=1

αl ql (Eli )

44

Fig. 2 IFSTEM basic network representation for goods flows through the ports and lands of Jordon, Syria, and Lebanon
M.K. Hasan

Multimodal, multicommodity international freight simultaneous transportation network equilibrium model

1. l = 1
2. E1i = Goi (observed trip generation at origin i)
3. α1 = .60 for all origins.
Hence
Eio = 0.60Goi

∀i ∈ I

That is the observed composite measure of the effect of the
socio-economic variables, which are exogenous to the transport system, is counted for 60% of the number of tons of the
general goods that exported from that origin. Also, the w exogenous socio-economic variables Awj that influences the
number of tons of the general goods imported at each destination j , Amman, Damascus, and Beirut, were not available
to compute

αi =

0.4Goi
Ln(Goi )

45

∀i ∈ I

We estimate θi = θ = 0.0000005 for all origins by the following method:
We run the computer code that solve IFSTEM model for
different values for θ till we got this value (θ = .0000005)
that satisfied the condition

Go
 i∈I pi ≈ 1.40
i∈I Gi
p

We assumed that

where Gi is the predicted trip generation for origin i for
year 2001. This value of θ will keep the affect of system
performance (supply), as measure by the delivery cost uij ,
on the predicted trip generated from origin to be 40% in average less than the observed trip generation.
This 40% decrease is what we gain when we assumed
before that uij = 0 to represent the observed trip generation
behavior of exporters as an initial solution to IFSTEM.

Aij = ln Tijo

3.3 Link cost functions assumptions

Aj =

W


θiw gw (Awj )

w=1

∀ij ∈ R

the a proxy measure of Aj as a composite measure of the
effect that socio-economic variables exogenous to the transport system have on the number of tons of the general goods
imported at each destination j that is exported from the origin i only. By this assumption each destination has deferent
attractive with respect different origins. We assume that this
attractive composite measure is the dominate of the exporter
observed utility function
Vij = −θi uij + Aij
That is, as the observed path flow shown in the next
analysis, the exporter at origin i is concern only by this attractive measure and he didn’t consider the delivery cost uij
to be effective in his choice of the destination (the path in
the original network). This will be similar to the IFSTEM
initial solution as we will explain next.
Therefore, the accessibility measure for this behavior will
be

exp(−θi uij + Aij ) ∀i ∈ I
Sio = ln
j ∈Di

Sio = ln



exp(ln(Tijo ))

∀i ∈ I

j ∈Di

Sio

= ln Goi

∀i ∈ I

and the trip generation model will be
Goi = αi Sio + Ei0

∀i ∈ I

Goi = αi Ln(Goi ) + 0.60G0i

∀i ∈ I

Now we can estimate αi ∀i ∈ I from the observed trip generation as follows:

As described in the network representation in Fig. 2 and
using the network representation of ESCWA, [6] study, we
have the following kinds of links:
1. Modal links include:
• Maritime links from any of Black Sea, West Mediterranean Sea, North and North East Europe, North and
South America, or Far East and South East Asia origins to any of Lattakia, Tartous, Tripoli, Beirut, or
Aqaba ports.
• Land transport links from Lattakia, Tartous, Tripoli,
Beirut, or Aqaba ports direct or through Nasib–Jaber,
or Masna-Jdeydet border points to Amman, Damascus, or Beirut destinations.
• Land transport links from Black Sea origin to Bab
Al Hawwa at Turkey-Syria border and from Bab Al
Hawwa to Nasib–Jaber border point and then from
Nasib–Jaber to Amman destination.
2. Operations links include:
• Import or transit-in artificial links at Lattakia, Tartous,
Tripoli, Beirut, and Aqaba ports.
• Export artificial link at Bab Al Hawwa border point
from Turkey side and transit-in artificial link at Bab Al
Hawwa border point from Syria side.
• Import, transit-in, or transit-out artificial links at Nasib, Jaber, Masna, and Jdeydet border points.
• Pre-export artificial links at Black Sea, West Mediterranean Sea, North and North East Europe, North and
South America, or Far East and South East Asia origins.

46

M.K. Hasan

• Pre-import artificial links at the dummy destinations
401, 402, 403, 404, 405, 501, 502, 503, 504, 505, 506,
601, 602, and 603.
3. Modal Transfer include:
• Artificial links at Lattakia, Tartous, Tripoli, Beirut, and
Aqaba ports to transfer between maritime transport
mode and land transport mode.
The following link cost function was used:
C(x) = (lc + ic)x + vtx

4 IFSTEM future prediction analysis for goods flows
through the ports and lands of Jordon, Syria, and
Lebanon
Based on the assumptions described in Sect. 3, the computer
code that solve the IFSTEM model is modified and run for
ten iterations to obtain the output shown in Tables 1–5. The
path flows for the original network presented in Fig. 1 are
corresponding to the O-D flows Tij for the network in Fig. 2
while the O-D flows in these tables are corresponding to trip
generation Gi for the network in Fig. 2.
4.1 The output prediction results for year 2001

where
x = number of tons of general goods

Tables 1–3 show the output results for this application for
year 2001 for two types of results:

C(x) = Cost of x tons of general goods in US dollar
lc = legal transport or operation cost per ton in US dollar
ic = illegal transport or operation cost per ton in US dollar
i = transportation or operation time in days
v = 3.7, the value of time per ton per day as estimated in
ESCWA, [7] study.
The values lc, ic, and i for each import, export, transit-in,
and transit-out operational link and maritime transport were
obtained from Tables A.2–A.4.
The cost function for any of pre-export, per-import, and
transfer link was set to zero since there no data for them.
In Tables A.2–A.4, the land transport time are at the paths
level in the original network. The following procedure was
used to compute these values at the link level:
The distance of land transport for each path and all its
links were computed, then the land transport time i for each
link was computed as follows:
i=

link distance
× path land transport time
path distance

Using the above assumptions for different link type, different networks one for each origin-destination are built with
no interaction between any of them. In this way each link
could belongs to different origin-destination network with
different characteristics.



 i∈I

Goi

p
i∈I Gi

Results for legal and illegal costs These results considered
both, legal transport or operation cost per ton, lc and illegal
transport or operation cost per ton ic for each link in each
path for a given origin-destination pair.
Based on the IFSTEM demand models assumption that
described in Sect. 3.2, the initial paths flow solution results,
perfectly; replicate the observed path flows in last column of
Table A1 that represent the real exporters’ behavior. Recall
that in this observed or initial solution we assumed that the
exporters consider only the attractiveness (uij = 0)of paths
(destinations in the network in Fig. 2) Aij as measure of
observed utility Vij to distribute their goods through these
paths. But they should in addition take into account the delivery cost uij in their observed utility as
Vij = −θi uij + Aij
to distribute their goods through the different paths of the
given O-D pair.
To evaluate the results of the final solution, the results are
compared with its corresponding observed values to give the
following findings:
The O-D flows (the trip generation for IFSTEM) decreases from 984492.00 to 752001.06 for O-D 1, from
660405.00 to 507237.61 for O-D 2, from 422467.00 to
320847.82 for O-D 3, from 1443743.00 to 945609.10 for
O-D 4, from 805484.00 to 522465.15 for O-D 5, and from
525185.00 to 344711.23 for O-D 6 and

=

984492 + 660405 + 422467 + 1443743 + 805484 + 525185
752001.06 + 507237.61 + 320847.82 + 945609.1 + 522465.15 + 344711.23

=

4841776.00
= 1.427 ≈ 1.4
3392871.97

Multimodal, multicommodity international freight simultaneous transportation network equilibrium model

47

Table 1 Adjusted final solution based on legal and illegal costs for year 2001
Path

O-D 1

1
2
3
4
5
6

99331.74
188800.43
152277.79
191179.57
245317.65
107584.83

Total

Cost

Cost/ton

O-D 2

Cost

Cost/ton

13751802.13
25154062.43
19056936.64
23961349.44
28876156.99
27209055.67

138.44
133.23
125.15
125.33
117.71
252.91

80315.67
126280.74
116561.43
149867.62
187379.54

11769712.28
18314620.45
16339398.16
21196413.13
26459731.94

146.54
145.03
140.18
141.43
141.21

984492.00
Average cost

138009363.31
140.18

892.77

660405.00
Average cost

94079875.96
142.46

714.40

Path

O-D 3

Cost

1
2
3
4
5

55722.54
113027.63
37486.11
64679.05
151551.67

9675839.89
19455563.78
5490903.52
9555313.85
25659096.34

173.64
172.13
146.48
147.73
169.31

250092.78
286652.29
263701.49
327227.55
316068.88

70311867.12
80156859.87
66631670.93
83094289.80
93464466.50

281.14
279.63
252.68
253.93
295.71

Total

422467.00
Average cost

69836717.37
165.31

809.30

1443743.00
Average cost

393659154.22
272.67

1363.10

Path

O-D 5

O-D 6

Cost

Cost/ton

1
2
3
4
5

189330.04
195085.36
137153.49
140684.24
143230.87

Total

805484.00
Average cost
Total average cost

Table 2 Comparison between
observed and adjusted IFSTEM
final solution based on legal and
illegal costs for year 2001

Cost/ton

Cost

O-D

Cost/ton

O-D 4

Cost

Cost/ton

51134579.13
51587036.95
26940419.20
27606571.92
29093463.52

270.08
264.43
196.43
196.23
203.12

183620.45
225795.99
115768.56

37009664.49
43747963.12
33660306.09

186362070.71
231.37
1169.84

1130.29

525185.00
Average cost

114417933.71
217.86

Observed average

Predicted average

Observed

cost per ton

cost per ton

O-D flows

201.56
193.75
290.76

Saving in the total cost

1

139.7304751

140.1833263

984492

−445,828.44

2

141.3222818

142.4578493

660405

−749,934.47

3

169.4769156

165.3069172

422467

1,761,686.74

4

295.5521543

272.6656713

1443743

33,042,199.65

5

266.8040219

231.3665706

805484

28,544,300.02

6

195.6931669

217.8621509

525185

−11,642,817.83

4841776

50,509,605.67

Total

1208.579016

as mentioned in Sect. 3 for the choice of θ = .0000005 to
satisfy the above equation. These decreases in the trip generations for all origins support the IFSTEM model concepts
that state: if the observed utility Vij decreases, the number
of trip generation will decrease.

1169.842485

The above finding doesn’t mean that only the predicted
trip generation should be distributed, but we can keep the
relative paths distribution and distribute all observed trip
generation, i.e.,

48

M.K. Hasan

Table 3 Comparison between
observed and IFSTEM final
solution based on legal costs
only for year 2001

O-D

Observed average

Predicted average

Observed

cost per ton

cost per ton

O-D flows

Saving in the total cost

1

139.0997441

138.5581836

984492

533,161.92

2

141.0280027

140.9379905

660405

59,444.47

3

169.261728

163.8659082

422467

2,279,555.82

4

295.4742529

270.6974938

1443743

35,771,272.61

5

254.6557169

224.6689715

805484

24,153,843.65

6

195.0256607

215.3559631

525185

−10,677,169.88

4841776

52,120,108.58

Total

1194.545105

1154.084511

Table 4 Comparison between IFSTEM final solution based on legal and illegal cost and IFSTEM final solution based on legal costs only for year
2001
O-D

Predicted average

Predicted flows

cost per ton bases

bases on legal

on legal and illegal

and illegal costs

Predicted average

Predicted flows

Observed O-D

Saving in

cost per ton bases

bases on legal

flows

the total cost

on legal costs only

costs only

costs
1

140.1833263

752001.06

138.5581836

753489.02

984492

1,599,939.99

2

142.4578493

507237.61

140.9379905

508223.26

660405

1,003,722.33

3

165.3069172

320847.82

163.8659082

321361.64

422467

608,778.73

4

272.6656713

945609.1

270.6974938

945895.2

1443743

2,841,542.49

5

231.3665706

522465.15

224.6689715

523458.71

805484

5,394,808.89

6

217.8621509

344711.23

215.3559631

345608.54

525185

1,316,212.21

3398036.37

4841776

12,765,004.64

Total

1169.842485

3392871.97

1154.084511

Table 5 Comparison between IFSTEM final solution based on legal costs only for year 2001 and IFSTEM final solution based on legal costs only
for year 2007
O-D

Predicted average

Predicted flows

cost per ton

for year 2007

for year 2007
1

138.3074934

2

140.9739634

3

162.819756

4

270.6014559

Predicted average

Predicted flows

Estimated-observed

Saving in the

cost per ton

for year 2001

O-D flows

total cost

for year 2001
1017474.3

138.5581836

753489.02

1396521

350,094.15

683701.64

140.9379905

508223.26

936797

−33,699.33

434408.23

163.8659082

321361.64

599278

626,935.51

270.6974938

945895.2

2047977

196,683.23

1342685.2

5

221.8480839

744003

224.6689715

523458.71

1142594

3,223,130.46

6

215.8258424

496942.9

215.3559631

345608.54

744985

−350,052.96

Total

1150.376595

4719215.27

1154.084511

Adjusted predicted path flow
=

predicted path flow
× observed O-D flows
predicted O-D flow

Table 1 shows the results of the adjusted final solution
based on legal and illegal costs for year 2001 for different
paths for the six O-D pairs where the path cost per ton is

3398036.37

6868151.793

4,013,091.07

computed from the sum of the links costs per ton (both legal
and illegal) that comprises this path from the computer program. Path cost is equal to the path flow multiply by the path
cost per ton. The average cost for each O-D pair is equal to
total paths costs divided by total paths flows. The total average cost for the all six O-D pairs are computed by adding
the average cost of the six O-D pairs.

Multimodal, multicommodity international freight simultaneous transportation network equilibrium model

The increase or decrease in the total cost is computed as
follows:
Increase/Decrease in Total cost

49

cost and how long it take each procedure and each signature
and we add this term in the link cost function. Then running
the IFSTEM model before and after the reduction will help
in evaluating this scenario.

= (observed average cost per ton
− predicted average cost per ton)
× observed O-D flows
Table 2 shows a comparison between observed (or initial)
and adjusted IFSTEM final solution. It also shows that the
overall saving for all O-D pairs is $50,509,605.67 and suggested that the path distribution of O-D 3, O-D 4, and O-D
5 should be redistributed as the IFSTEM model suggested.
Results for legal costs only To evaluate the effect of the
scenario of canceling all the illegal costs, the illegal cost
term ic was deleted from the link cost function and we run
the modified computer code for IFSTEM model for the same
10 iterations. Tables 3 show the results of this scenario for
observed (or initial) and final solutions where all O-D pairs
average cost per ton were decreased except O-D 6. It may
need to consider O-D 6’s observed path distribution due to
the special factor that Beirut is a port and the destination for
this O-D pair and it is preferable to export most of Lebanon
goods from Far East and South East Asia direct to Beirut.
The results show an overall saving of $52,120,108.58. The
improvement in the average cost per ton of O-D 1 and O-D
2 is due to that the illegal costs had significance effect on
these O-D pairs.
Table 4 show a comparison between IFSTEM final solution based on legal and illegal Cost and IFSTEM final solution based on legal costs only for year 2001. These results
show that canceling the illegal cost will results an overall
saving of $12,765,004.64 for all O-D pairs and the total predicted O-D pairs flows increased from 3392871.97 tons to
3398036.37 tons, i.e., an increase of 5164.4 tons in the total
predicted flows which is equivalent to
4841776
× 5164.4 = 7358.62 tons
3398036.37
These results are very encourage and support for the scenario of canceling the illegal costs that result in increasing
more goods to be imported to Jordon, Syria and Lebanon
with less overall costs and it demonstrate the ability of IFSTEM model as a policy-sensitive model in predicting international freight flows under different scenarios.
Different others scenario or combination of scenarios can
be evaluated by the IFSTEM model if any of these scenarios
can be reflected by increase or decrease in the cost or the
time on any modal or operational links. For example, we can
evaluate the effect of the scenario of reducing the number of
procedures and signatures by 50% if we know how much it

4.2 The output prediction results for year 2007
After we tested and validated IFSTEM model using the
base year 2001 data, we assumed that there will be 6% annual growth in the socio-economic activities. The estimatedobserved O-D pair flows for year 2007 are computed as follows:
Estimated-Observed O-D pair flows for year 2007
= (Observed O-D pair flows for year 2001) × (1.06)6
For IFSTEM model application, the growth is reflected
through new values for the socio-economic variables Ei and
Aij as follows:
for year 2007 = (for year 2001) × (1.06)6
for year 2007 = (for year 2001) × (1.06)6
We assumed also the modal and operational link costs and
times will be the same as those of year 2001. The results
for year 2007 have the same pattern as those of year 2001.
Table 5 show a comparison between IFSTEM final solution
based on legal costs only for year 2001 and IFSTEM final
solution based on legal costs only for year 2007. The results shows that all O-D flows are increased with total increases of about 39% and the overall saving in the cost is
$4,013,091.07 based on the Estimated-Observed O-D pair
flows for year 2007 (that we can adjusted the IFSTEM predicted O-D pair flows to its value as explained earlier).
The assumption that the modal and operational link costs
and times for year 2007 will be the same as those of year
2001 is due to there is no available projected data for these
costs and times. The modal and/or the operational link costs
may increase or decrease due to the shippers competitiveness, improve in the transport technology, improvement in
the port or border points producers and regulations and use
of information technology. But modal and operational link
times may not increase due to these factors.

5 Conclusions and future activities
5.1 Conclusions
The authors of this study have implemented the International Freight Simultaneous Transportation Equilibrium
Model (IFSTEM) that developed in [6] (see also [22]) to

50

the goods trade through the ports and lands of Jordon,
Syria, and Lebanon. Although some socio-economic variables, which are not available, were required for IFSTEM
model calibration, some reasonable assumptions were made
and it were good enough to draw the following main findings:
1. The IFSTEM model was able to replicate the observed
path and O-D pair goods flows for year 2001 through its
initial solution.
2. The IFSTEM final solution suggested that the path distribution for most observed O-D pairs flows is not optimal
due to the exporters depend only one some measure of
attractiveness in their path choice and it should be redistributed to save in the total cost.
3. The IFSTEM can be consider as a good decision support
tool that is able to evaluate the value of any scenario that
can be reflected through any change in the costs and/or
times of its link cost function, as the case of canceling illegal cost, or any change in the socio-economic variables,
as the case of year 2007 prediction.
4. The three countries of Jordon, Syria, and Lebanon import only goods from countries outside the ESCWA region which is not the main objective of the development
of IFSTEM and ITSAM to increase the trade between
ESCWA countries. It is also very difficult to collect the
socio-economic data that required for IFSTEM methodology for these outsider countries.

M.K. Hasan

5.2 Future activities
A limitation of a full implementation of IFSTEM to all ESCWA countries or a group of them is suggested and should
include all of the following:
1. Data collection program to collect all the socio-economic
data needed to perform a real calibration of the trip generation and trip distribution models parameters.
2. Data collection program to collect data needed to perform a real calibration of the link performance (generalized cost) function for different mode and different operation.
3. Implement IFSTEM using the data collected in activities
1 and 2.
Integrating the above future activities with the following
current activities:
1. The integration of IFSTEM with its Geographic Information System (GIS) database.
2. The development of a user-friendly interface to perform
graphic policy scenario analyses on ITSAM using IFSTEM
will result in a very important Decision Support System
that help the decision makers of ESCWA countries to take
a right decision that is based on a scientific approach regarding what they have to do to increase the goods trade among
their countries.

Table A.1 Existing paths and origin-destination pairs system and its observed goods flows
Origin-Destination (O–D)

O–D 1:
From: Black Sea
To: Amman (Jordan)

O–D 2:

Path

Loading

Unloading

Shipment

Countries

Borders points that goods pass through it

ports

port

type

pass through

Entry

Exit

Entry

Observed
Exit

flows (tons)

1

Odessa–Constantia

Lattakia

Transit

Syria

Nasib

Jaber

–

–

758

2

Odessa–Constantia

Tartous

Transit

Syria

Nasib

Jaber

–

–

116894

3

Odessa–Constantia

Tripoli

Transit

Lebanon– Syria

Masna

Jdeydet

Nasib

Jaber

8988

4

Odessa–Constantia

Beirut

Transit

Lebanon– Syria

Masna

Jdeydet

Nasib

Jaber

75546

5

Odessa–Constantia

Aqaba

Import

–

–

–

–

–

640243

6

Odessa–Constantia

By land

Transit

Turkey–Syria

–

Bab Al Hawwa

Nasib

Jaber

142063

1

Barcelona–Valencia

Lattakia

Transit

Syria

Nasib

Jaber

–

–

838

From: West Mediterranean Sea

2

Barcelona–Valencia

Tartous

Transit

Syria

Nasib

Jaber

–

–

16737

To: Amman (Jordan)

3

Barcelona–Valencia

Tripoli

Transit

Lebanon– Syria

Masna

Jdeydet

Nasib

Jaber

4

Barcelona–Valencia

Beirut

Transit

Lebanon–Syria

Masna

Jdeydet

Nasib

Jaber

5

Barcelona–Valencia

Aqaba

Import

–

–

–

–

–

1

Rotterdam–Hamburg–Entrup

Lattakia

Transit

Syria

Nasib

Jaber

–

–

324

2

Rotterdam–Hamburg–Entrup

Tartous

Transit

Syria

Nasib

Jaber

–

–

27700

O–D 3:
From: North and North East Europe
To: Amman (Jordan)

7192
60458
575180

3

Rotterdam–Hamburg–Entrup

Tripoli

Transit

Lebanon–Syria

Masna

Jdeydet

Nasib

Jaber

43

4

Rotterdam–Hamburg–Entrup

Beirut

Transit

Lebanon–Syria

Masna

Jdeydet

Nasib

Jaber

359

5

Rotterdam–Hamburg–Entrup

Aqaba

Import

–

–

–

–

–

1

Baltimore–Houston–Santos

Lattakia

Transit

Syria

Nasib

Jaber

From: North and South America

2

Baltimore–Houston–Santos

Tartous

Transit

Syria

Nasib

Jaber

To: Amman (Jordan)

3

Baltimore–Houston–Santos

Tripoli

Transit

Lebanon–Syria

Masna

Jdeydet

Nasib

Jaber

183

4

Baltimore–Houston–Santos

Beirut

Transit

Lebanon–Syria

Masna

Jdeydet

Nasib

Jaber

1539

5

Baltimore–Houston–Santos

Aqaba

Import

–

–

–

–

–

1432301

O–D 4:

O–D 5:

394041
1163
8557

1

Yokohama–Hong Kong

Lattakia

Import

–

–

–

–

–

347330

From: Far East and South East Asia

2

Yokohama–Hong Kong

Tartous

Import

–

–

–

–

–

457349

To: Damascus (Syria)

3

Yokohama–Hong Kong

Tripoli

Transit

Lebanon

Masna

Jdeydet

–

–

185

4

Yokohama–Hong Kong

Beirut

Transit

Lebanon

Masna

Jdeydet

–

–

235

5

Yokohama–Hong Kong

Aqaba

Transit

Jordan

Jaber

Nasib

–

–

385

O–D 6:

1

Yokohama–Hong Kong

Tripoli

Import

–

–

–

–

–

35178

From: Far East and South East Asia

2

Yokohama–Hong Kong

Beirut

Import

–

–

–

–

–

482317

To: Beirut (Lebanon)

3

Yokohama–Hong Kong

Aqaba

Transit

Jordan–Syria

Jaber

Nasib

Jdeydet

Masna

Multimodal, multicommodity international freight simultaneous transportation network equilibrium model

Appendix

7690
51

52

Table A.2 Paths legal costs for each origin-destination
Origin-Destination (O-D)

O-D 1:

Path

Unloading

Shipment

Maritime

port

type

transport cost

Total costs inside port

Land transport

Border producers and fees costs

cost

Lebanon

Syria

1

Lattakia

Transit

17

29

3

16

8.43

From: Black Sea

2

Tartous

Transit

17

29

3

15

8.43

To: Amman (Jordan)

3

Tripoli

Transit

15

3

5

19

2.93

7.68

4

Beirut

Transit

15

3

5

19

3.93

7.68

5

Aqaba

Import

30

13.6

0.60

17.2

6

By land

Transit

O-D 2:

7.68

1

Lattakia

Transit

14

29

3

16

8.43

From: West Mediterranean Sea

2

Tartous

Transit

14

29

3

15

8.43

To: Amman (Jordan)

3

Tripoli

Transit

20

3

5

18

3.93

7.68

4

Beirut

Transit

20

3

5

19

3.93

7.68

5

Aqaba

Import

35

13.6

0.60

17.2

1

Lattakia

Transit

30

29

3

16

From: North and North East Europe

2

Tartous

Transit

30

29

3

15

To: Amman (Jordan)

3

Tripoli

Transit

30

3

5.50

18

3.93

7.68

4

Beirut

Transit

30

3

5.50

19

3.93

7.68

5

Aqaba

Import

52

13.6

0.80

17.2

O-D 3:

O-D 4:
From: North and South America
To: Amman (Jordan)

O-D 5:

8.43
8.43

1

Lattakia

Transit

45

29

3

16

8.43

2

Tartous

Transit

45

29

3

15

8.43

3

Tripoli

Transit

40

3

5.50

18

3.93

7.68

4

Beirut

Transit

40

3

5.50

19

3.93

7.68

5

Aqaba

Import

60

13.6

0.80

17.2

1

Lattakia

Import

75

29

2.50

16

2.86

From: Far East and South East Asia

2

Tartous

Import

75

29

2.50

16

2.86

To: Damascus (Syria)

3

Tripoli

Transit

65

3

4

Beirut

Transit

65

5

Aqaba

Transit

55

1

Tripoli

Import

85

O-D 6:

Jordan

3
13.60
3

5
5
0.80
3.5

6

3.14

6

3.14

25

2.86
2.86
2.86

8.78

7.68

8.78

7.60

2

Beirut

Import

85

3

3.5

3

To: Beirut (Lebanon)

3

Aqaba

Transit

84

13.6

4.5

39

M.K. Hasan

From: Far East and South East Asia

Origin-Destination (O-D)

O-D 1:

Path

Unloading

Maritime transport

Total time

Land transport

Land border time

port

time

inside port

time (hours)

Exit

Entry

Exit

1

Lattakia

9

5

8.19

2

0.5

From: Black Sea

2

Tartous

8

5

7

2

0.5

To: Amman (Jordan)

3

Tripoli

7

2-3

4.67

0.5

2

0.5

0.5

2

0.5

0.5

0.5

O-D 2:
From: West Mediterranean Sea
To: Amman (Jordan)

4

Beirut

7

2-3

4.37

5

Aqaba

10

4-6

4.86

6

By land

8

72

1

Lattakia

12

5

8.19

2

0.5

2

Tartous

12

5

7

2

0.5

3

Tripoli

10

2-3

5.67

0.5

2

0.5

4

Beirut

10

2-3

4.37

0.5

2

0.5

5

Aqaba

15

4-6

4.86

1

Lattakia

15

5

8.19

2

5

From: North and North East Europe

2

Tartous

15

5

7

2

0.5

To: Amman (Jordan)

3

Tripoli

8-10

2-3

5.67

0.5

2

0.5

4

Beirut

8-10

2-3

4.37

0.5

2

0.5

5

Aqaba

15-21

4-6

4.86

O-D 3:

O-D 4:
From: North and South America
To: Amman (Jordan)

O-D 5:

1

Lattakia

40

5

8-19

2

0.5

2

Tartous

40

5

7

2

0.5

3

Tripoli

35

2-3

5.67

0.5

0.2

0.5

0.5

4

Beirut

35

2-3

4.37

0.5

0.2

0.5

0.5

5

Aqaba

50

4-6

4.86

0.5

2

1

Lattakia

30

5-6

8

From: Far East and South East Asia

2

Tartous

30

5-6

3.76

To: Damascus (Syria)

3

Tripoli

22-25

2-3

2.83

4

Beirut

22-25

2-3

1.53

0.5

2

5

Aqaba

20

3-4

8

0.5

2

1

Tripoli

22-25

1-7

1.3

From: Far East and South East Asia

2

Beirut

22-25

1-7

To: Beirut (Lebanon)

3

Aqaba

20

3-4

0.5

2

O-D 6:

Entry

9.16

Exit

Entry

Multimodal, multicommodity international freight simultaneous transportation network equilibrium model

Table A.3 Paths transportation and port and border procedures times for each origin-destination

2
53

54

M.K. Hasan

Table A.4 Illegal costs at the ports and borders of the three countries
Country

Border/port

Jordan

Aqaba/Jaber/Amman

Syria

Tartous
Lattakia
Jdeydet
Nasib

Lebanon

Masna
Beirut/Tripoli

Import

Transit

0.06

0.1

10
15
1.61
0.81

1.5
2
0.37
0.23

5
0.5

5
0.5

References
1. Boyce, D. (2007a). Forecasting travel on congested urban transportation networks: review and prospects for network equilibrium
models. Networks and Spatial Econ