INTEGRATED MODELING
TRANSPORT NETWORK
OF
THE
EUROPEAN
TRADE
AND
Jinxue Hu and Olga Ivanova
TNO – Netherlands Organization of Applied Scientific Research
Abstract
Trade and transport statistics are two sides of the same coin. Ideally we would like to
have the data on the trade flow between two countries in combination with the
information about the split of this flow between various (multi-modal) routes linking
the pair of countries. In reality trade and transport statistics are quite difficult to match
due to differences in methodology for data collection and the use of surveys. In
particular transport statistics show us only the location of loading and unloading and
does not say anything about origin and destination of the commodity flows. Trade
data provides information on the origin and destination of the flows but may also
include re-exports and transit flows. The unification and reconciliation of transport
and trade statistics is of great value to both policy makers and the scientific
community. The methodological approach presented in the present paper attempts to
combine trade and transport data into a single consistent multi-commodity and multimodal freight OD matrix for Europe. We apply logit shares for route choice in
combination with a cross-entropy function in order to find the optimal trade-off
between available trade and transport statistics. The result of this exercise is the
identification of the transhipment flows and pure trade flows for the intra and extraEuropean trade. The methodology presented in this paper has been developed within
the framework of the ETIS-Plus project for DG MOVE.
1.
INTRODUCTION
Trade and transport statistics are two sides of the same coin. Ideally we would
like to have the data on the trade flow between two countries in combination
with the information about the split of this flow between various (multi-modal)
routes linking the pair of countries. The unification and reconciliation of
transport and trade statistics is of great value to both policy makers and the
scientific community. The methodological approach presented in the present
paper attempts to combine trade and transport data into a single consistent
multi-commodity and multi-modal freight origin-destination (OD) matrix for
Europe.
As an introduction to the methodological description we would like to provide
some explanations about the content of existing statistical data on trade and
transport and indicate the main difficulties for reconciling the two data
sources.
Ideally we would like to include in the dataset the information about trade
flows from country A to country B split between different transport routes that
the trade flows follow. The routes can in principle include multiple modes of
transportation. Unfortunately none of the elements of such data is readily
available from statistics for reasons that are explained in details below.
Existing trade statistics usually contains not only pure trade flows between the
countries (that are of interest for our dataset) but also re-exports. This
complicates the process of separating real trade flow from an accounting one.
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1
In case of European countries, re-exports represent part of the transit of the
extra-European trade flows via European countries. Not all transit flows are
registered as re-exports but only the part that changes ownership status at the
border. Hence even by separating re-exports we will not get information about
the total extra-European transit flows through the European countries.
Intra-European trade statistics is currently based on surveys which reduces its
reliability for the analysis and makes it more difficult to use it as hard data.
Extra-European trade statistics is more reliable since it is based on actual
customs registrations, however it also includes re-exports.
Transport statistics is based on surveys (hence it is less reliable) and provides
data on loading-unloading matrices of transport flows between countries. This
data does not give information about the initial origin and the final destination
of transport flows neither within Europe nor worldwide, instead it includes data
on transport flows which could be separate parts of the multi-modal routes.
The goal of our methodological procedure is to estimate the matrix of initial
origin to final destination trade flows (called IO-FD matrix) within Europe
based on the transport data. This matrix includes only the pure European
trade flows including the transport routes that these trade flows take.
In order to create the reconciled set of transport and trade data we have
followed the steps described below. In our methodology we take transport
statistics as given to the extent possible and introduce only changes to it
when needed. The trade statistics is allowed to vary in order to fit the
transport-related data.
1. Read in transport and trade data and bring it to harmonised
dimensions: (1) at the country level and (2) at the NSTR1 commodity
level.
2. Check whether transport data allows for generation of single or twomode routes between all existing intra-European trade flows. In case
this is not possible adjust the transport data.
3. Group European countries into several geographical groups and
identify the transit country for each of these groups on the basis of
transport statistics. The transit country is the country with the largest
amount of in and out-going transport flows.
4. Generate all possible transport routes between European countries on
the basis on transport statistics. The routes include one mode routes
where from transport statistics we see that there is a direct loadingunloading connection and two-mode routes that combine road, rail,
inland waterways and sea modes. Additionally we generate routes that
consist of road mode with transit at the countries determined in step 3.
5. Select the most feasible multi-modal routes of all possible multi-modal
routes per each pair of European countries, on the basis of their
transport costs. We select the three cheapest multi-modal routes and
remove the other multi-modal routes. This set of selected multi-modal
routes plus all single mode routes constitute the set of possible
transport routes.
© AET 2013 and contributors
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6. Set-up a nonlinear programming problem for the estimation of the OD
matrix transport flows between European countries. The optimization
problem consists of several constraints. It includes the fixed transport
data, the set of possible routes for each country pair, the choice of
each trade flow to take particular routes (according to logit choice) and
the objective function which minimises the difference between the
generated OD transport matrix and the intra-European trade matrix
(based on actual trade data). The difference is measured as an
entropy function. The result is a OD transport matrix, which includes all
transport in Europe and are translated into routes.
7. Once we know the OD transport matrix between European countries
we can set-up a procedure for the estimation of the IO-FD matrix. The
OD transport matrix that we have estimated in step 6 contains both
pure intra-European trade and the extra-European transit flows. The
IO-FD matrix should only include the pure intra-European trade and
thus the extra-European transit flows should be identified and
separated. We use data on the extra-European maritime flows and the
extra-European trade flows to estimate the extra-European transit
flows. We use a nonlinear programming approach for the estimation of
extra-European transit flows. The optimization problem includes
constraints on non-negativity of trade and transit. Extra-European
maritime flows and the European OD transport matrix are considered
fixed. The objective function minimises the difference between the
generated extra-European trade and transit flows and the extraEuropean trade flows (based on actual data). The difference is
measured as an entropy function.
The result of the estimation procedure is the set of consistent trade and
transport chains for each pair of countries by commodity type, which is based
on the following outcomes of the methodological procedure:
1.
IO-FD matrix of consistent trade and transport flows between
European countries, which consists of only pure intra-European trade
flows.
2.
Transportation flows on the routes which link each pair of
European countries. This includes one-mode routes, multi-modal
routes and road routes with transit points.
3.
Extra-European transit flows via a particular European country.
After entering Europe or before departing Europe, these flows follow
the same transportation routes as intra-European trade flows.
The methodology presented in the paper has been developed within the
framework of ETIS-Plus1 project for DG MOVE. In this project we used an
adjusted approach compared to the approach described in this paper. In the
project the transport statistics were considered fixed while in this paper we
allow it to vary to some extent.
© AET 2013 and contributors
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1.1
Literature review
There are only a few studies that try to reconcile existing transport and trade
statistics for a particular country. Existing studies are done for large transit
countries such as the Netherlands and Hong Kong and focus on the
identification of the volume and value of the transit flows that pass through
these countries. For the Netherlands data on transit flows is estimated by the
national bureau of statistics on the basis of statistical model but does not
include the origin and destination at country level (CBS, 2008). The main
focus of the CBS procedure is to find an estimate of the transit flows that pass
through the Netherlands by type of transport mode on the basis of Bayesian
techniques. Other studies have also focused on identifying transit flows. This
was done for Hong Kong by using data from the Hong Kong Census and
Statistics Office (Feenstra and Hanson, 2000). The Dutch Bureau for
Economic Policy Analysis (Centraal Plan Bureau) has done a comparison of
trade data from the generalized trade system and the specialized trade
system (Gelhar, 2006). The generalized trade system includes re-exports
while the specialized trade system does not.
There are multiple studies that have estimated the OD traffic matrix using
traffic counts. This is known as the OD matrix estimation problem (Lo and
Chan, 2003). The data application is different from our paper but the approach
is similar. The challenge is that there are many alternative combinations
possible from the transport counts. Exogenous data can be used to address
this challenge. Many studies have used a target matrix as exogenous
information, towards which the estimated OD matrix can be optimized
(Hazelton, 2003). Such a matrix estimation method can either be done with
statistical estimation procedures or by using a mathematical programming
method based on an entropy theory (Sherali et al, 2003). Other methods of
using exogenous data are Bayesian estimation and generalized least
squares. We will use the entropy function and minimize the difference
between the estimated OD matrix with the target OD matrix.
The major difference of our methodology is that we make use of statistical
data (transport and trade data) rather than the results of surveys. The
estimated matrix that we use in the study is based on the transport flows data
whereas the target matrix represents the data on bi-lateral trade flows.
2.
DATA
2.1
Trade and transport data
Trade and transport data both include different information on the flows of
goods. The table below gives an overview of this. On the one hand transport
statistics show transported weights by transport mode and by loadingunloading location. On the other hand we have trade data given in weight or
value. This trade data is also available with information on the main transport
mode used. However it does not show information on the multi-modal route or
the location of loading-unloading. In our exercise we only use trade data
© AET 2013 and contributors
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without information on the main transport mode. By combining the two data
sources one can get the different types of goods flows, pure import and
export, re-exports and transit flows.
Figure 1. The matrix of trade and transport statistics by type
Source: Linders et al. (2006)
We use the trade and transport data from the project ETIS-Plus2 for DG
MOVE. In this project public transport data by mode and commodity has been
harmonized and disaggregated. In our dataset intra country flows are omitted.
We use the trade and transport data in volumes, at the country level and in
the NSTR1 commodity classification.
The commodity NSTR3 “petroleum products” is excluded from the estimation
procedure. This is because a vast amount of oil is transported via pipelines.
However good data on pipeline transport is not available in OD matrix format.
2.2
Transport cost data
Transport costs of the different routes depend on several factors. We have
used the needed transport costs data from the model TRANSTOOLS3. Firstly
the average load rate is calculated using the load capacity, load as fraction of
capacity and the number of loaded trips as fraction of total number of trips.
Secondly we calculate costs related to time using speed, distance, loading
and unloading time and waiting time. Finally, costs related to distance are
calculated using energy costs per km and distances. The data was provided
for the four different transport modes and 10 types of NSTR1 commodities.
The transport costs have been calculated for each country pair, commodity
and transport mode. The average transport costs per tonne and per intraEuropean trip are presented in the table below to provide an indication of their
values.
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Table 1: Average transport costs by transport mode, in euro per tonne per trip within
Europe
Inland
Sea
waterway
32
24
Rail
Road
97
124
We have used the transport costs from TRANSTOOLS for intra-European
transport routes. In case of extra-European routes we do not use transport
costs. We assume that all transport flows go via the maritime transport routes
which makes mode choice redundant. Also these are often long distance
routes where transport costs don’t play an important role in the choice
between various seaports.
3.
METHODOLOGY
3.1
Part 1: Estimation of the European multimodal transport network
The first part of the estimation procedure includes the construction of the
multimodal transport network for Europe. This network represents all transport
routes in Europe and is based on the transport flows data at the country level.
This European multimodal network is created by merging the different modal
networks into one multimodal network. We take into account rail, road, inland
waterway and sea routes. The multimodal network consists of both single and
multimodal routes. In order to create routes from transport links of various
modes we need information on the existing connections between the transport
links. However, country level data from ETIS-Plus does not provide enough
detail on existing connections (transit points) between the modes. Therefore
we assume that all transport links can be connected to each other. Also we
assume each route to either consist of one or two transport links in order to
simplify the procedure. We derive the transport flows on the multi-modal
transport network as follows.
ORIG
EUtranspod  EUtranspod
  ( EUtranspojORIG  EUtransp ORIG
)
jd
j
Where EUtranspod
stands for the estimated intra-European multimodal
transport network, EUtranspojORIG  EUtranspORIG
stands for the multimodal
jd
ORIG
routes created from the original transport data and EUtranspod
stands for
single mode routes based on the original transport data.
For each intra-European trade flow at least one transport route should exist. If
there are no single or two mode routes possible we adjust the transport data.
We also identify a list of potential transit countries that are countries with
either large ports or with large logistic centers. We group European countries
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into several geographical zones and identify the transit country for each of
these zones based on transport statistics. The transit country is the country
with the largest amount of in and out-going transport flows.
We generate all possible transport routes for each pair of countries by taking
transport statistics as it is for single routes and by combining the road, rail,
inland waterways and sea transport statistics for the multi-modal routes.
Besides that we also generate routes that consist of road mode with transit to
another road mode at the earlier identified transit countries. Only for the bulk
goods we assume no multi-modal routes except when there are no alternative
single routes available for a country pair. (NSTR2, 4, 6 an d 7 are considered
bulk goods).
On the basis of data on transport costs, we select the three cheapest multimodal routes and remove the other multi-modal routes. This set of selected
multi-modal routes plus all single mode routes constitute the set of possible
transport routes.
We can now setup the first nonlinear programming problem for the estimation
of the European OD transport matrix. We do this by matching the transport
network with trade data. The intra-European trade flows are assigned to the
transport network by applying logit shares based on transport costs as
follows.
r
odc
P
r
exp(TCodc
)

r
 exp(TCodc )
r
r
Where TCodc
stands for the transport costs for a specific route between country
o and d by commodity c .
In order to identify the optimal balance between the available trade data and
the OD transport matrix we use the following constrained optimization
problem. European trade flows choose their preferred routes on the transport
network using logit shares. The input transport volume data is considered
fixed in the model whereas trade volume is allowed to vary. The objective
function minimizes the distance between the estimated OD transport matrix
and the trade matrix (that is based on existing trade statistics). We use the
cross-entropy function as follows.
min CE   EUtranspod log(
od
EUtranspod
)
ORIG
EUtradeod
Where CE is the sum of cross-entropies, EUtranspod the volume of an estimated
ORIG
trade and transport route between origin and destination and EUtradeod
the
original volume of the trade flow. The CE is minimized subject to the
constraints and this procedure is referred to as the minimum sum of cross
entropies approach or MSCE (McDougall, 1999).
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The volume of trade data is allowed to vary and hence adjust in order to
match the transport statistics. The transport data is considered fixed (at the
aggregate level). In this manner we attempt to implicitly correct for re-exports
in the trade data. However, at the level of the individual transport links the
transport volume can vary compared to the original transport data.
3.2
Part 2: Estimation of the extra-European transit flows
The European transport matrix for Europe is estimated in part 1. The transport
flows in this matrix can either be an intra-European pure trade flow or a transit
(hinterland) transport flow, which is part of an extra-European trade flow. The
second step is to make a distinction between these two types of flows by
splitting in a proper way the European OD transport matrix. In order to do that
we combine the extra-European trade data and maritime data with the
previously estimated OD transport matrix for Europe.
We firstly identify for each of the extra-European trade flow its European port
of entry. After entering the port the goods can either be consumed in the
same country or take a hinterland route to another destination country. The
choice of port is as follows.
portoid
ORIG
extraEUtranspoid  extraEUtradeod
 portod
i
Where extraEUtranspoid stands for the estimated extra-European trade flows
ORIG
going through port i , extraEUtradeod
stands for the original extra-European
trade data,
portoid
stands for the port choice for each OD pair. Each OD
 portod
i
pair can choose a port from a set of available ports. The set of available ports
can be small or large depending on the number of connecting hinterlands
routes to the destination country. The choice of port is based on the volume
transported to the ports and over the hinterland routes.
In our first optimization problem we have identified a consistent intraEuropean multimodal network representing all volumes transported over
routes. This network is the basis for our hinterland network. In the porthinterland network construction we estimate which of the flows follow the
hinterland routes and which not. We assume all the intra-European routes to
potentially be a hinterland routes in case they are linked to a seaport.
Firstly the port-hinterland transport network is created by merging the
European transport network with the extra-European maritime transport flows.
We assume connections between the maritime flows and the European
transport network exists in all countries with a seaport.
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Secondly we optimize the port-hinterland network by matching it with trade
data. The European transport network and the maritime transport flows are
considered fixed. The extra-European trade flows will have to be assigned to
a port and optionally to a hinterland route as well. In the model the choice of
port and hinterland route is allowed to vary. The objective function minimizes
the distance between the port-hinterland flows and the original extraEuropean trade flows while respecting the transport data. Again we use the
Cross-Entropy function as the objective function.
min CE   (extraEUtranspoid ) log(
od
i
 extraEUtransp
oid
i
ORIG
extraEUtradeod
)
Here extraEUtranspoid stands for the extra-European transport routes from
ORIG
country o to country d going through seaport i and extraEUtradeod
stands
for the original trade extra-European data. Note that seaport i can be in the
destination country d .
The above described statistical modeling exercise is applied for both the
incoming and the outgoing extra-European flows.
3.3
Estimation of European IO-FD matrix
We estimate the European IO-FD matrix by taking the estimated European
multimodal transport network from part 1 and subtracting the extra-European
transit flows. These transit flows were identified in the extra-European porthinterland network from part 2. The European IO-FD matrix consists of only
pure intra-European trade flows with their transport route.
We have now identified the optimal intra-European transport OD matrix, which
OPTIMAL
is EUtranspod
and the optimal extra-European port-hinterland network
OPTIMAL
which is extraEUtranspoid
. We can now estimate the extra-European transit
flows and the pure intra EU trade flows by using the following logic.
OPTIMAL
EUtradeod  EUtranspod
 EUtransitij with i  o and j  d
OPTIMAL
OPTIMAL
EUtransitij   extraEUtranspoij
  extraEUtranspijd
o
d
Where EUtradeod is the pure European IO-FD trade matrix and EUtransitij
represents the transit/hinterland flows of extra-European trade flows. The
OPTIMAL
estimated matrix extraEUtradeoij
expresses the incoming extra-European
OPTIMAL
flows through port i and extraEUtradeijd
the outgoing extra-European
flows through port j .
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4.
RESULTS
The first result is an IO-FD matrix of pure intra-European trade flows without
re-exports or transit flows. The second result is the European transit flows,
which are actually a part of extra-European trade flows. Thirdly for each trade
flow we have information about the transport routes that it follows including
the transport mode and loading/unloading locations.
On the basis of the European OD transport matrix we have identified a part to
be intra-European trade, transported over a single or multi-modal mode route,
and another part as a transit or hinterland route from an extra-European trade
flow. These shares are given in the table below. The shares for the hinterland
route include both transit flows for incoming and outgoing extra-European
trade flows.
Table 2: Share of total volume transported in Europe which is European trade using
single- or multi-modal routes, or extra-European trade using hinterland route, by
commodity
European trade
transported over
a single mode
route
European trade
transported over
a multi-modal
route
Extra-European
trade
transported over
hinterland route
0 Agricultural products and live animals
53%
21%
26%
1 Foodstuffs and animal fodder
44%
9%
47%
2 Solid mineral fuels
78%
3%
19%
4 Ores and metal waste
59%
6%
35%
5 Metal products
6 Crude and manufactured minerals,
building materials
57%
18%
25%
91%
1%
8%
7 Fertilizers
74%
9%
18%
8 Chemicals
9 Machinery, transport equipment,
manufactured articles and
miscellaneous articles
47%
10%
44%
55%
19%
26%
NSTR1
Nearly half of the transport of “foodstuffs and animal fodder” and “chemicals”
in Europe is part of an extra-European transport route. It seems that these are
the products which typically go through only specific (specialized) ports and
this is why there is relatively large amount of hinterland transport.
On the other hand “crude and manufactured minerals, building materials” are
mainly transported on an intra-European single-route. This indicates that this
type of product has a more local nature.
“Agricultural products and live animals” and “machinery, transport equipment
and manufactured articles” have the highest share of multi-modal routes.
These seem to be products which are typically transhipped within Europe
through for instance distribution centres.
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From the extra-European trade flows it is interesting to see which products
are typically transhipped after arriving in a port and which products often go
directly to the port of the destination country. The graph below shows the
percentage of incoming port throughput which arrives directly in the
destination country or takes a hinterland route by transport mode.
Figure 2: Share of extra-European trade flows arriving in European ports that take a
hinterland route, split by transport mode
100%
80%
direct
60%
iww
rail
40%
road
mar
20%
0%
0
1
2
4
5
6
7
8
9
We see that NSTR2 “Solid mineral fuels” has the lowest transhipment rate.
This product often goes directly to the country of destination. And in the few
cases it does take a hinterland route it mostly goes by inland waterway.
The highest transhipment rates are observable for NSTR5 “Metal products”
and NSTR9 “Machinery, transport equipment, manufactured articles”. Road
and sea are the most used transport mode for transhipped products while
inland waterway and rail are used less.
More detailed results at the country level is given in the table below. The
top10 port countries are shown here. This table shows the countries with the
highest incoming port throughput and shows what percentage of the incoming
throughput is transhipped to another European country. Also, it gives the main
receiving country.
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Table 3: Top 10 countries with highest incoming port throughput and their
transhipment rates of incoming flows with the main receiving country
Country Transhipment rate
Main receiving country
(% of total seaport
(% received of total
throughput)
seaport throughput)
NL
IT
ES
FR
UK
BE
DE
PT
PL
RO
39%
24%
27%
31%
34%
48%
57%
28%
58%
36%
DE (16%)
ES (6%)
IT (5%)
NL (6%)
BE (6%)
FR (14%)
NL (10%)
ES (13%)
DE (13%)
IT (5%)
The Netherlands, the largest port country, has a transhipment rate of 39% for
the incoming transport flows. Of the total incoming throughput 16% has
Germany as destination country. The highest transhipment rates can be found
for Poland and Germany.
Often the main receiving country is a neighbouring country. From the results
is seems that Germany has very high trade figures. It has one of the highest
transhipment rates, meaning that a lot coming in their ports is transhipped to
other European countries. And at the same time Germany receives most of
the transhipment from the ports in the Netherlands and Poland.
The consistent trade and transport flows in the European IO-FD matrix and
the port-hinterland network are different from the original trade statistics. The
deviation with the original trade statistics are given in the figure below. The
volume of the estimated trade flows is much lower than the original one
except for NSTR9. The fact that the estimated trade flows are lower in volume
is because they should represent only the pure trade flows and the re-exports
are removed. The difference could give an indication for the volume of reexports. The reason why the estimated flows for NSTR9 are higher is
because NSTR9 is likely largely overestimated in transport statistics. NSTR9
contains transport via containers. The goods transported in containers are
difficult to register since the goods are not visible from the outside and
containers transport a wide variety of goods which are then difficult to classify
to the correct NSTR group.
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Figure 3: Total tonnes of trade by NSTR group in original trade data and in the
estimated trade flows, in million tonnes
700
0
600
1
500
2
400
4
5
300
6
200
7
100
8
9
0
original
estimated
The estimated trade and transport flows has almost no deviation from
transport statistics. This is at the least the case at the more aggregate level.
After all, the total volume of transport at the commodity level was fixed in the
constrained optimization problems. However, at the level of individual
transport links there could deviation from the original transport data.
5.
CONCLUSION AND RECOMMENDATIONS
5.1
Conclusions
The unification and reconciliation of transport and trade statistics is of great
value to both policy makers and the scientific community. This study has
proposed a methodological approach for reconciling a multi-commodity and
multimodal trade and transport OD matrix for Europe. A trade-off is made
between trade and transport statistics using entropy function. This
methodology corrects for re-exports and transit flows in the trade data and
estimated the multi-modal routes including information on transport mode and
location. This methodology can be further improved or adjusted and hopefully
helps to make trade and transport data more consistent as a part of further
research projects.
5.2
Recommendations
Several issues with the data and modelling can still be improved in further
research and are described here.
Not all trade data could be matched with transport data. This is for instance
the case with assigning the extra-European trade flows to the port-hinterland
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network. The hinterland of the Europe was modelled but the hinterland of the
non-European countries was not. This could lead to problems. For instance
there were trade flows of a certain commodity from the Philippines but no
maritime transport flows. This is because trade from the Philippines could be
transported via Singapore or another country. There was no information
available on the non-European hinterland network. We therefore could not
explain well the extra-European trade flows which could have been using non
EU hinterland routes. The non EU hinterland network would be useful to
model as well.
The non-negativity constraint for trade flows on occasion gave a problem in
the second optimization problem. Sometimes the modelled hinterland routes
would have a larger volume than the total volume transported on that route.
For instance the excess volume that could not be transported over this route
should choose an alternative route. In those cases we assigned the excess
volume to the direct route to the port of the destination country directly instead
of going over a hinterland route. We recommend including a more refined
mechanism.
If pipeline and air transport data would become available in the future in OD
matrix format by commodity, it would be very interesting to include those in
the model as well.
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BIBLIOGRAPHY
CBS (2008) Integration of international trade and transport flow statistics for
the Netherlands
Feenstra, R.C. and Hanson, G.H. (2000) Intermediaries in entrepôt trade:
Hong Kong re-exports of Chinese goods
Gehlhar, M. (2006) GTAP 6 Data Base documentation - 15c Re-export trade
for Hong Kong and the Netherlands
Hazelton, M.L. (2003) Some comments on origin-destination matrix
estimation, Transportation Research Part A 37 (2003) 811-822
Linders, G.M., Odekerken-Smeets, M.E.P. and Groot, H.L.F. de (2006)
“Linking trade and transport statistics: The Dutch Case” ERSA 2006
Lo, H. and Chan, C. (2003) Simultaneous estimation of an origin-destination
matrix and link choice proportions using traffic counts, Transportation
Research Part A 37 (2003) 771-788
Sherali, H. D., Narayanan, A. and Sivanandan, R. (2003) Estimation of origindestination trip-tables based on a partial set of traffic link volumes,
Transportation Research Part B 37 (2003) 815-836
NOTES
1
More information of this project is available at http://www.etisplus.eu
2
Data is available at viewer.etisplus.net
3
More information of this project is available at
http://energy.jrc.ec.europa.eu/transtools/
© AET 2013 and contributors
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