D5 Annex report WP 3: ETIS Database methodology ... - ETIS plus

D5 Annex report WP 3: ETIS Database 

methodology development and database user 

manual– Freight transport demand V2.0 

CONTRACT N° : GMA2/2000/32051SI2.335713 ETISBASE 

PROJECT N° : 2.1.1/9 

ACRONYM : ETISBASE 

TITLE : Core Database Development for the European Transport policy Information System (ETIS) 

PROJECT COORDINATOR : NEA Transport Research and Training BV 

PARTNERS : 

Nouveaux Espaces de Transport en Europe Application de Recherche 

Istituto di studi per l’integrazione dei sistemi 

Universität Karlsruhe (TH) 

MDS Transmodal Limited 

MKmetric Gesellschaft Fuer systemplannug MBH 

Technical Research Centre of Finland 

Eidgenoessische Technische Hochschule Zuerich 

PROJECT START DATE: 1122002 

Report reference number: R20030249 

Date of issue of this report: 27052004 

DURATION : 33 Months 

Project funded by the European Community under the 

‘Competitive and Sustainable Growth’ Programme 

(19982002)

D5 Annex WP 3: DATABASE METHODOLOGY AND DATABASE USER 

MANUAL – FREIGHT TRANSPORT DEMAND 

CONTENTS 

page 

1 INTRODUCTION ...............................................................................7 

2 OBJECTIVES AND STRATEGIC ASPECTS.....................................9 

3 SETTING THE FRAMEWORK ........................................................11 

3.1 Introduction...............................................................................................11 

3.2 Supporting indicators assigned to WP 3 Freight Transport Demand and 

method of calculation ................................................................................11 

3.3 Variables to be included in WP 3 Freight transport demand .......................12 

3.4 Variables required for calculation of supporting indicators that will not be 

included in the ETIS reference database.....................................................16 

4 THE FREIGHT OD DATA MODEL ..................................................19 

4.1 Introduction...............................................................................................19 

4.2 General structure .......................................................................................19 

4.3 Combining trade/transport/transhipment data sources ................................19 

4.4 Data availability and reliability; an example ..............................................20 

4.5 The topdown approach .............................................................................21 

4.5.1 Phase I The construction of a countrytocountry matrix ............................21 

4.5.2 Phase II Including transhipment regions on the basis of transhipment 

statistics ....................................................................................................23 

4.5.3 Phase III Regional division of countrytocountry totals.............................23 

4.5.4 Phase IV Incorporating domestic transport.................................................24 

4.6 Data gaps identified...................................................................................25 

4.7 Estimating data gaps..................................................................................26 

4.8 Adding information to the freight OD matrix .............................................26 

4.8.1 Cargo types and characteristics..................................................................26 

4.8.2 Containerisation ........................................................................................27 

4.8.3 Number of transport units (vehicles/vessels) by type..................................27 

4.8.4 Number of TEUs .......................................................................................28 

4.8.5 Transport performance information (tonnekm, vehicle km/vesselkm, 

TEU km)..................................................................................................29 

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D5 Annex WP 3: DATABASE METHODOLOGY AND DATABASE USER MANUAL – 

FREIGHT TRANSPORT DEMAND 

5 TRANSPORT CHAIN FREIGHT O/D MATRIX – DATA NEEDS, 

DATA COLLECTION AND REMAINING DATA GAPS ..................31 

5.1 Introduction............................................................................................... 31 

5.2 Data needs................................................................................................. 31 

5.2.1 Trade data ................................................................................................. 31 

5.2.2 Transport data ........................................................................................... 32 

5.2.3 Maritime transhipment data .......................................................................33 

5.2.4 Container data ........................................................................................... 34 

5.2.5 Vehicle/vessel movement data...................................................................34 

5.2.6 Relation with other datasets in the ETIS reference database ....................... 34 

5.3 Data collection .......................................................................................... 36 

5.4 Remaining data gaps .................................................................................40 

5.4.1 Data available at Eurostat not available for the ETIS project ...................... 40 

5.4.2 Registration of mode of transport for intraEU trade becomes optional in 

Eurostat trade statistics .............................................................................. 40 

5.4.3 Transhipment data of seaports and inland terminals ...................................40 

5.4.4 Data availability in the accession countries ................................................ 41 

6 DESCRIPTION OF THE WP 3 TESTING PHASE AND ITS 

RESULTS ........................................................................................43 

6.1 General description of the testing phase..................................................... 43 

6.2 Tested methodologies................................................................................ 43 

6.3 Lessons learned and changes compared to the original planned methodology64 

6.4 Output of the testing phase ........................................................................ 64 

6.5 Interpretation of the output data for the users .............................................65 

7 OBSERVATIONS UP TO PHASE 2.................................................67 

7.1 Introduction............................................................................................... 67 

7.2 Legal aspects and organisational aspects.................................................... 67 

7.3 Other remarks............................................................................................67 

ANNEX A: LIST OF INDICATORS CONSIDERED IN WP 3...................................69 

ANNEX B: INDICATOR COMPILATION TEMPLATES ..............................................77 

ANNEX C: EXPERIENCE FROM CONCERTED ACTION ON SHORT SEA SHIPPING 

PROJECT ............................................133 

ANNEX D: THEORETICAL ANALYSIS OF TRANSPORT CHAIN STRUCTURES IN 

DATABASES; CONSTRAINTS AND POSSIBILITIES ..........................149 

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ANNEX E: NEWCRONOS TABLES (RELEVANT FOR ETIS)..................................165 

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1 INTRODUCTION 

The present report represents the WP 3 annex report of deliverable D5 of the reference database 

development within the ETIS project 1 . The present document describes the ETISDatabase 

methodology development and is the database user manual for the freight transport demand data 

set, which is being developed within WP 3. 

The objectives of the methodological report can be characterised as follows: 

· Determine the variables to be included 

· Describe the methodology to obtain the variables and to fill the remaining gaps 

· List the sources being used and document the status of data purchase 

· Describe the scope of and methodology for the pilot data set to be generated for freight 

transport demand indicators. 

· Describe the testing phase and its findings. 

This annex report and the annex reports of the other WPs are summarised in the synthesis report 

of D5. 

1 The full title of the reference database part of ETIS project is ETISBASE `Core Database Development for the European 

Transport policy Information System (ETIS)’ 

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2 OBJECTIVES AND STRATEGIC ASPECTS 

The work on the ETIS reference database responds to key action 2, ‘Sustainable Mobility and 

Intermodality’, objective 2.1 ‘Socioeconomic scenarios for mobility of people and goods’, 

task 2.1.1/9, ‘Development of a European Transport policy Information System (ETIS) as a 

basis for transport planning and policy formulation’. The task is separated into three subtasks. 

The ETIS reference database addresses subtask 2, ‘the development of a reference database for 

the modelling element’. 

The objectives of ETIS reference database are: 

1. To contribute to the building of a consensus view of the reference pan European transport 

modelling data set. 

2. To develop an open methodology to generate a version of such a set from existing 

international and national sources. 

3. To produce a first compilation of the data set by applying the methodology mentioned 

above, as online database. 

During the kickoff of the project it has been decided by the European Commission that within 

this project the work should focus on: 

1. the development of an ETIS for TENT policies, 

2. the procedures and data should face especially a monitoring of the TENT corridors, 

3. the geographic scope has to be adjusted to the forthcoming 10 new members, 

4. the PAN European scope has to be defined along the geographic hemispheres, 

5. the degree of detail in general can be reduced including a concentration upon a few 

indicators mentioned in the white paper, 

6. the results have to be available for further use within the GETIS system, 

7. the work tasks and responsibilities have to be adjusted in respect of the new focus and the 

limited budget. 

The reference database of ETIS will serve a various series of TENT policy issues: the level of 

detail and the variables will be appropriate for this purpose. It will allow obtaining in an 

accurate way the performance and the impacts (environmental, economic) of transport, as well 

as the traffic at specific nodes or links of the networks. But the internal structure of the database 

will allow proceeding easily to any aggregation in order to get a compact view of transport 

performance (vehiclekms etc) and effects (emissions level, energy consumption by mode etc). 

The work organisation of ETIS reference database is established in close cooperation with the 

external promotion 2 and contacts of ETIS. As part of this external promotion, support to the 

development of the ETIS reference database is an essential element. 

There are several aspects on which synchronisation of the two projects take place: 

2 Promotion work is covered by the ETISLINK project 

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· 4 workshops specifically related to the development of the ETIS reference database 

· Open Conferences in which dissemination of results takes place 

· Participation to the ETIS Steering group 

· Testing of the system with pilot users, i.e. also testing of results of the development of the 

ETIS reference database 

· Dealing with specific issues like legal and organisational aspects. 

In addition the ETIS reference database will be incorporated in the ETIS software tools 3 . The 

two most important outputs of the reference database development that serve as input to the 

system tools being developed are: 

1. The metadata concerning indicators and data sources serve. 

2. The final reference ETIS database 

Furthermore in order to make it possible for the software tool developers to continue their work 

while the reference database is being developed working material is being delivered which has 

also been used in the TENSTAC project. Intermediate results from the reference database 

development will be delivered as soon as it comes available. ETIS reference database 

construction will, where possible, use the results of GETIS (GIS data) and TENSTAC 

(indicator definitions and use of a selection of the input data) and find cooperation where 

possible. 

The ETIS reference database project, ETIS promotion and external contacts project and ETIS 

software tools project 4 have come up with a common and better harmonised focus during 

meetings from November 2003 up to January 2004 in order to be able to come up at the end of 

the three projects with one consistent and unique ETIS product. This product is a pilot and it is 

expected that one will continue to maintain and to develop the ETIS tool to the interest of the 

European transport policy. 

3 ETIS software tools are covered by the ETISAGENT project 

4 respectively ETISBASE, ETISLINK and ETISAGENT, the trilogy of ETIS projects 

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3 SETTING THE FRAMEWORK 

3.1 Introduction 

In this chapter a full description of the key elements of the Work Package data and outcomes are 

provided. The emphasis, due to its importance in the overall database, is devoted to the 

indication of the scale and time scale dimensions of data collected and method of calculation of 

supporting indicators. 

3.2 Supporting indicators assigned to WP 3 Freight Transport Demand and 

method of calculation 

In table 3.1 the supporting indicators that are assigned to WP 3 have been listed. These are 

supporting indicators related to TEN policies and specifically related to freight transport 

demand. More information on these supporting indicators are listed in Annex A. 

Table 3.1: 

TEN policy supporting indicators considered in WP 3 Freight transport 

demand 

Domain ref. Definition 

Mobility 1.1.4 

Breakdown of journeys by origin/destination, proportion of long distance traffic using the 

TEN 

Mobility 1.5.1 Road freight volumes on TEN by cargo type 

Mobility 2.1.2 

Mobility 2.5.1 Freight volumes on TEN by train type 

Traffic volumes on the TransEuropean rail network, by type (passenger / freight), 

including non fulfilled demand 

Mobility 4.1.2 Freight volumes on the inland waterway network 

Mobility 5.1.1 Port throughput (passengers, freight) 

Capacity 4.1.3 I/C factor of bridges and locks (intensity versus capacity) (waiting time) 

Utilisation 6.1.1 Traffic volumes served at the terminal 

Energy consumption 1.3.1 Estimated / measured energy consumption on roads 

Energy consumption 2.3.1 Estimated/measured energy consumption on railways 

Energy consumption 4.2.1 Estimated/Measured Energy Consumption on waterways 

Transport emissions 1.3.2 



Estimated /measured road transport emissions 

+corresponding marginal unit cost 

Estimated /measured rail transport emissions 


Estimated /measured Inland waterway transport emissions 


Transport noise 1.3.3 Noise levels generated by road transport + corresponding marginal unit cost 

Transport noise 2.3.3 Noise levels generated by rail transport +corresponding marginal unit cost 

Investments and return 

on capital 

3.2.2 Amount of investment made in the development and maintenance of air traffic control 

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In Annex B for each supporting indicator a template is included in which the method of 

calculation and required variables, modelling, model input and other characteristics are 

described. 

3.3 Variables to be included in WP 3 Freight transport demand 

In this section the variables are listed that are needed for the calculation of the supporting 

indicators described in section 3.2. Furthermore these variables are selected in such a way that 

the database will be flexible enough to cover also future changes to the indicators and to make it 

as useful as possible for modelling and forecasting. It should also provide a proper basis for 

comparison between modes and submarkets. For this reason it should be mentioned that the 

requirements for the indicators listed have been considered as a minimum requirement and do 

not reflect one to one the selected variables to be considered in this work package. 

Freight OD matrix 

Many of the TEN policy related indicators listed in section 3.2 are directly or indirectly linked 

to OD transport flows. For instance information about transport volumes on specific links on the 

infrastructure network can be produced by applying an assignment where the assignment 

procedure needs as input the freight OD matrix. This also holds for many of the other indicators 

such as noise levels or energy consumption. Therefore the freight OD matrix has first priority. 

Once the freight OD matrix is available, other information and data can be added to it and 

indicators can be derived from it. 

It has been chosen to build a region to region (also cross border) transport chain structure in 

which change of mode and transhipment location are included. A theoretical analysis on the use 

of transport chains is given in annex D and in chapter 4 the methods to be used to construct the 

transport chain database are described. 

A record structure will be applied with two transhipment locations and three modes (from 

experience it appears that a record structure with more transhipment locations is not feasible, 

see also annex D). The transport chain matrix will then have the following structure: 

· Origin 

· Transhipment 1 

· Transhipment 2 

· Destination 

· Mode at origin 

· Mode between transhipment points (active mode) 

· Mode between transhipment points (passive mode) 

· Mode at destination 

· Commodity 

· Cargo type 

· Unitised / nonunitised 

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· Other typology 

· Measuring unit 

For these variables the characteristics will be considered as listed in table 3.2. 

Table 3.2: 

Variables considered in the freight OD transport chain matrix 

Core countries 

EU15, Norway, Switzerland, Estonia, Latvia, Lithuania, Poland, Czech 

Republic, Slovak Republic, Hungary, Slovenia, Malta, Cyprus 

Regional detail NUTS 2 or similar regional detail where no NUTS classification is valid (#) 

Country and country 

group detail 

Transhipment location 

Modes 

Commodities 

Cargo types 

Cargo characteristics 

Unitised 

Other Typologies 

Measuring units 

All European countries separate with exception of the smallest (like Andorra, 

Vatican, etc), Meda countries separate, USA, Rest North America, Middle 

and South America, Japan, Rest Asia, Rest Africa, Australia and New 

Zealand, Rest world 

Selection of Ports 

Road, rail, inland navigation, maritime, rest 

Distinction between active and passive mode (*) for maritime transport (if 

required data is available) 

NSTR 2 digits (as much as possible), if no data is available or when 

modelling becomes necessary aggregation to NSTR 1 digit 

General cargo, liquid bulk, semi bulk, dry bulk, vehicles, crude oil 

Hazardous, conditioned 

Yes/No, share of unitised flows in the total flows 

Vehicle/vessel types (definition depending on data availability) 

Values 

Tonnes 

Base year 2000 

Tonnekm 

Number of vehicles / vessels 

Vehiclekm / vesselkm 

TEU 

TEUkm 

(#) 

The NUTS2 classification forms the basis for the regional division. For some countries the 

NUTS2 classification has only one region (the country). If in such cases the NUTS3 

classification has more than one region and if data on this regional level of detail is available, 

the regional division according to the NUTS3 classification will be used as much as possible. 

(*) 

Definition of transport with active and passive mode: transport of goods using two modes of 

transport in combination, where one (passive) transport means is carried on another (active) 

transport means which provides traction and consumes energy (for instance truck (passive mode 

road) on a train (active mode rail)). 

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Remarks 

Concerning the regional detail it can be remarked that NUTS2 (or NUTS3) can only be 

achieved where the data availability allows. In many cases estimation models can be applied 

requiring more aggregate regional data and/or socioeconomic data. In the remaining cases the 

country level will be used. 

Concerning the modes it can be remarked that if sufficient data becomes available active and 

passive mode use can be applied. In case of transhipment several modes can be found in one 

transport chain. 

Transhipment in ports will be feasible (has been proven to work in NEAC and projects like 

INFOSTAT, MESUDEMO, CONCERTO and ALPNET) but the number of ports is depending 

on the availability of data in the ports. 

The commodities will be kept as much as possible at the NSTR 2 digit level. However, some 

data sources only have information on NSTR 1 digit level. Furthermore, existing estimation 

procedures are only available on a NSTR 1 digit level since otherwise no reliable result can be 

obtained. 

Also cargo type can not be collected from all sources. Here estimation by using translation 

tables will be needed. 

In order to be able to translate volumes into vehicle movements additional information is 

needed. Here information on vehicle/vessel type and empty vehicle movements will be used as 

input. For the empty vehicles/vessels it will be attempted to construct a database which will be 

separate from the transport chain database. Furthermore some general figures on loading factor 

is needed. The question of translating commodity flows expressed in tonnes into vehicle flows 

expressed in units follows on from the estimation of transport chains, as it depends upon the 

mode of transport being known. Estimates of vehicle numbers, particularly within the road 

mode provide an important input for calculations involving external effects. 

From this transport chain structure where the commodity is followed from origin to destination 

it can also become clear which flows are deep sea and which short sea. This is done by looking 

at the location of origin or transhipment and destination or transhipment. 

If the value is available in the database also the share of low and high valued goods can be 

identified. 

In an ideal situation where all required information is available the freight OD matrix as 

described above in this section can be completely filled. It is expected that in practice not all 

information will be available. Therefore the contents and the level of detail included in the final 

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freight OD matrix depend on the availability of data and on the possibilities to estimate data 

gaps. 

Possibilities for extracting subdatabases 

Once such a transport chain has been developed it is possible to extract more specific subdatabases 

from it. Here are some examples listed. 

Trade matrices: 

· Origin of commodities 

· Destination of commodities 



· Commodity 

· Cargo type 

· Containerised 



or 



· Commodity 

· Cargo type 




Transport matrices: 

· Origin of vehicles/vessels 

· Destination of vehicles/vessels 

· Mode 

· Commodity 

· Cargo type 




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Transhipment matrices: 

· Transhipment location 



· Mode incoming 

· Mode outgoing 

· Commodity 

· Cargo type 




Or 

· Transhipment location 

· Incoming/outgoing/transit 

· Commodity 

· Cargo type 




Time series 

Besides the OD matrix only aggregate terminal and port figures are needed as can be concluded 

from the indicator descriptions. Due to the complexity of the construction of the ODmatrix it is 

only possible to develop a base year 2000 OD matrix. 

Furthermore we will extract existing material from EUROSTAT describing other freight 

demand aggregate variables where a lot of data has already been harmonised. In annex E the 

relevant tables from New Cronos for ETIS have been listed. 

3.4 Variables required for calculation of supporting indicators that will not be included 

in the ETIS reference database 

During the test phase of the development of a freight O/D matrix two things have become clear 

in relation with the transhipment at inland terminals: 

· The required data is not available for inland terminals (this conclusion has already been 

drawn in other projects and it also became clear during the data collection in the ETIS 

project); 

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· Methods can be applied to fill the above mentioned data gap by estimating the data. 

These methods are too complex and too time consuming to fit in the scope of the ETIS 

project. 

As a result, it has been decided to leave information about transhipment at inland terminals out 

of the freight O/D matrix. Information about transhipment included in the freight O/D database 

concerns solely transhipment at seaports. 

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4 THE FREIGHT OD DATA MODEL 


In this chapter the proposed freight OD transport chain data model is described. The exact 

structure of the type of model to be used is depending on the specific structure of the available 

data. For this reason not all details but merely the basic principles and available tools will be 

discussed. The exact model in full detail will be developed along the construction process in the 

remaining of the project. Some results obtained in the testing phase are already described in 

chapter 6. 

4.2 General structure 

The general structure of this model to be used can be split up into the following parts: 

1. Combining trade/transport/transhipment data sources 

2. Estimating data gaps 

3. Adding information to the OD matrix 

In the first part all available trade and transport data sources are being combined into one 

database by a topdown approach. This approach will have the same structure as the NEAC 

database construction model. It might be the case that some regional information is missing in 

transport or trade data or that no data are available at all for a specific geographical area. In this 

case estimation methods are required that can estimate data by modelling. Next procedures will 

be applied to add information to the OD matrix. Container information, cargo type information 

and characteristics on the OD flows will be estimated. Also estimation procedures will be 

applied for the translation from tonnes to vehicles/vessels and if possible by type, translating 

containerised tonnes into TEUs. Finally all this information will be used to determine transport 

performance information (tonnekm, vehiclekm/vesselkm and TEUkm). This will be described 

in more detail in the following sections. 

4.3 Combining trade/transport/transhipment data sources 

The first step in the topdown approach is to combine all collected trade/transport sources into 

one database. Here we use the trade data as the foundation of the whole database and refine this 

with information that can be found in the transport and transhipment data sources. In the 

INFOSTAT project a pilot database has been constructed with the topdown approach. As 

described in paragraph 6.4 there are some restrictions on data combinations. In INFOSTAT a 

database with two transhipment points has been constructed. A conclusion was that for all 

elements of the chain, data should be available to ensure quality of the results. 

Up to this point two transhipment points in the chain structure by combining sources seems to 

be the limit on the level of detail. 

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A topdown database construction method (see section 4.5) for combining 

trade/transport/transhipment data sources is proposed which is based on the NEAC as developed 

by NEA and which was also used in INFOSTAT. 

4.4 Data availability and reliability; an example 

The methods used in the topdown approach are very much dependent on the source data that 

are available. These sources can have very different structures (chain, O/D, aggregated flows 

through a node) and the transport flows described can be based on different definitions (trade, 

transport, only national carriers, etc). When developing a model we can choose different 

philosophies. We could for instance develop one robust model that requires the same level of 

detail for all data sources. The advantage is that the application of the model is relatively easy. 

The big disadvantage is that we will loose a lot of useful information since the model will be 

directed to the worst case of the available data sources. The other extreme is to develop a 

method for inclusion of each data source separately (NEAC database construction model). The 

advantage is that all information incorporated in the data sources can be used to the maximum. 

The disadvantage of this method is that it is very costly especially when many sources have to 

be considered and the reliability of transport flows may vary between different OD relations 

since some originate from observed data and others from estimation procedures. 

In the ETIS reference database model we have to take account of the fact that we will have to 

deal with quite a lot of data sources. We therefore have to look for an efficient topdown 

method that can make use of as much information in the sources as possible, but is not being 

held back by the poorest source. 

Each of the possible methods has its pros and cons. In any case it can be stated that only reliable 

information can be obtained from database construction if for all elements of the transport chain 

information is available. If this is not the case then some parts or elements of the database will 

be less reliable. 

Since different fragments of the total transport chain will be collected that have to be matched to 

each other it may be unsure whether the total constructed records will be reliable. In the 

INFOSTAT project (task 3 WP E) attempts have been made to include a second transhipment point 

in the record structure for flows of the European continent in relation with Norway. In this pilot 

case transhipment data was available for Belgium, the Netherlands, Germany and France on the 

continent and in Norway. A method was developed to combine a transport chain database for the 

continent with a transport chain database for Norway. Unfortunately by that time no port to port 

information was available which could be used to match the Norwegian ports and the ports on the 

continent. As a result the link between the ports in the eventual database was not reliable. 

Analysing either of the transhipment points in relation with all other variables except the other 

transhipment point is reliable however. The advantage of the eventual database is that all figures 

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are consistent. In case the port to port data would have been available a multidimensional entropy 

method could have been applied to combine all sources. 

From this exercise we learned that combining sources is useful in any case to ensure consistency, 

but that the reliability is in the first place depending on availability of data for all elements of the 

chain. 

One of the main advantages of a topdown approach is that detail can be added to the database by 

inclusion of different sources on specific transhipment points or links without being held back by 

the nonavailability of data on other transhipment points or links. A danger might be that in case the 

information for the construction of a transport chain is incomplete, like was the case in the example 

above, unreliable links can be included. In these cases it is important that these links can be 

identified in the database or that the main database can only be approached by trained experts and 

that subsets of the database constructed by aggregation of one or more variables will be available 

for a wider audience. 

4.5 The topdown approach 

The philosophy of the “transport chain principle” implicates that the transport flows are 

determined by the trade flows; we preserve the trade relation and follow the route of the 

transported goods. This means that, besides the origin and destination, the location of 

transhipment and the modes before and after transhipment are included. To do this we use a topdown 

approach, which means that we take the rough countrytocountry trade information and 

refine this, step by step, using the various national data sources. This approach allows us to 

introduce the most disaggregated level permitted by the data sources available in each 

individual country, without being limited by the lack of data in other countries. By constructing 

the database using the topdown approach, it is possible to use a restricted number of sources with a 

maximum of regional detail. Additionally it is possible to give different parts of the database a 

different level of detail without introducing inconsistencies. This method is being materialised in an 

approach in which four phases can be distinguished. 

The phases in the topdown approach are the following: 

1. The building of a countrytocountry matrix 

2. Including transhipment regions on the basis of transhipment statistics 

3. Regional division of countrytocountry totals 

4. Incorporating domestic transport 

These phases will be described in the following paragraphs. 

4.5.1 Phase I The construction of a countrytocountry matrix 

As a reference for the development of the database the EUROSTAT trade by mode (COMEXT) is 

used for the EU countries and national trade data sources for the other countries in the core area. In 

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cases where also no national sources can be found UN trade data will be used having the 

disadvantage that no mode information is included. In the trade data every trade relation is being 

registered from the export as well as the import side. 

These trade data sources, whose main function is to provide information relating to the value of 

trade, need to be modified before the information can be related to the volume of trade, even 

where volumes are recorded by the relevant customs authority. Corrections have to be applied 

due to incompatible measurement units (litres, kilograms, square metres), or due to data errors, 

often of significant magnitude. The first problem (incompatible units) can be solved by applying 

conversion ratios for the same commodity groups in instances where the ratio is known. The 

second problem (data outliers) can be solved by applying a smoothing technique to a time series 

of data. Such a technique has been applied with the MDS Transmodal trade forecasting model. 

The technique works by scanning the COMEXT data for a single trade flow (e.g. French exports 

of SITC 56 in tonnes to Italy) for quarterly time periods (of several years). The smoothing 

software samples four data points for each year and calculates the mean and the standard 

deviation for that year. It then compares each year's mean and standard deviation with all the 

others. Then if there are any years with unusual levels of variance, they are investigated by the 

software, and according to certain thresholds individual quarterly values may be marked as 

outliers and the software will replace them with interpolated values. It means that very erratic 

series will be left untouched, but erratic sequences within normally stable series will be 

corrected. 

Furthermore there will be adjustment required due to the resale of (bulk) commodities. For 

instance there is a significant amount of ores traded from the Netherlands to Germany according 

to COMEXT. These ores however do not originate from the Netherlands but are resold after 

being imported in the Netherlands, which implies that in fact this flow of ores between the 

Netherlands and Germany is just one link from the total transport chain. In the NEAC 

construction history many of these cases have been identified and can be traced back by 

comparing the NEAC forecast up to the year 2000 with the COMEXT 2000 data. The large 

outliers are being manually analysed. This procedure will be applied on top of the earlier 

mentioned procedure. For a future update of the ETIS reference database freight OD the OD 

matrix currently being developed can then be used for identification of outliers. 

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The transport mode is registered at the border of the country in most trade sources and at the border 

of the EU for the extra EU trade in COMEXT; as a result it is possible to estimate the part that is 

transhipped onto another mode. When the trade statistics show that a flow leaves Spain by sea and 

enters Poland by road, it can be concluded that somewhere transhipment has taken place. In this 

phase all direct transport without transhipment and indirect transport with transhipment is 

registered. 

A difference in definition appears here since for the extra EU trade the mode is not anymore 

registered at the border of the countries but at the border of the EU. Specific solutions will be 

analysed amongst which the option of estimation of the country border mode by assignment in 

following steps. 

4.5.2 Phase II Including transhipment regions on the basis of transhipment 

statistics 

The identification of transhipment regions is taking place with the help of the available statistics 

originating from the national transhipment sources or ports and terminal. The inclusion of inland 

terminal information will be considered here as an experiment since no proof of concept is 

available. 

In this step two transhipment points will be included for intra ETIS reference database core area 

short sea flows. All collected port flows will be combined into one database. Here double countings 

have to be eliminated in the cases where for two ports transhipment data are available and where 

these ports have a connecting service; these flows are then registered at both ports. 

The port flows are then included in the trade database of step one taking account of all information 

included in the data (origin, destination, commodity, modes) and again removing all the double 

countings. This way the trade volumes on country to country level are the same as in step 1, but it is 

known whether transhipment takes place along the route, where this takes place and from what 

mode to what other mode. 

4.5.3 Phase III Regional division of countrytocountry totals 

The first two steps resulted into information ranging from both the country of origin to the country 

of destination. The regional detail is added to the database by means of the different sources 

dividing the trade flows over the regions in a country. Countries for which this can be done we will 

call A countries. For the other countries to be called B countries, regionalisation can be performed 

by using domestic transport statistics (for instance New Cronos). These domestic transport statistics 

often only show us the total flows arriving or departing from a region. The remaining countries will 

be called C countries. For this last category estimation procedures will be applied which are 

developed in ODESTIM and which make use of socioeconomic data (see section 6.6). 

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In annex C a description is included of a pilot exercise performed by MDS within the ‘Concerted 

Action Short Sea Shipping’ project where a method has been tested to estimate regionregion flows 

from regioncountry data sources (A countries). 

4.5.4 Phase IV Incorporating domestic transport 

The previous phases are completely directed towards international transport. The fourth step 

concerns the inclusion of domestic transport in the database. 

A problem arises in the port regions when international and domestic transport is being connected. 

Transport that is transhipped in a port is registered twice; in the international trade statistics as well 

as in the domestic transport statistics. For example, a trade flow from Brussels via Antwerp by road 

to the USA by sea is registered in the domestic transport data as road from Brussels to Antwerp. 

A procedure will be applied to remove this type of double counting in the database. It reconstructs 

the complete chain by attaching domestic egress or access modes to the harbour for inland 

transport. 

It can be assumed that the transport between inland regions (without a sea port) is determined by 

domestic production and consumption patterns. Also it can be assumed that front or end transport 

connecting rail and inland waterways is taking place within the region of origin or destination (intra 

regional transport). 

The connection between the two sources of information is complicated by the fact that international 

trade flow statistics are based on documents that continuously register all trade passing the border, 

while domestic transport is based on surveys in a short period of time. When necessary, a 

correction has to be made for this fact in the direction of the trade statistics. 

The total topdown approach is schematised in figure 4.1. 

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Figure 4.1 

Topdown Approach 

4.6 Data gaps identified 

The topdown approach described above uses the available data sources to a maximum level. 

However, for some types of data no sources are available (on the complete EU level). Important 

data gaps identified are: 

· International region to region trade and transport flows 

· Transport mode on the European territory of intercontinental flows 

· Port transhipment data 

· Inland terminal transhipment data 

· Intermodal transport statistics 

· Container transport data 

· Commodity characteristics 

· Transport performance data (number of transport units, loading factors, number of 

loaded/empty trips) 

Since no complete data is available for these subjects (or data is available but not on the 

required level of detail or not for all required regions) estimation procedures have to be applied 

to fill these data gaps. 

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4.7 Estimating data gaps 

A broad collection of estimation models exists where socioeconomic, network data and 

transport sector data serve as input. At this stage the ODESTIM project (4 th framework EC) 

will be used as reference. In this project different models are tested for estimation of transport 

data based on the fourstage model of generation, attraction, distribution and modelsplit. 

Models are developed for different levels of availability of data ranging from no transport data 

at all to estimation of only the modalsplit. No models for estimation of intermodal data or 

loadingunits data are considered in ODESTIM. These models have proven to be successful as 

one of the tools for filling the remaining data gaps in the construction of the NEAC databases 

for Eastern Europe, the Russian Federation and Kazakhstan. 

In the topdown approach estimation procedures are applied to estimate the region to region 

flows. The results of the topdown approach can be used to make an assignment on the transport 

network. The assignment can be used to estimate missing data such as transport modes used and 

transhipment locations. 

Once the freight O/D matrix has been build from available data sources and data gaps have been 

filled, other data can be added. 

4.8 Adding information to the freight OD matrix 

After the freight OD matrix has been made available additional information that is not available 

in data sources can be added relatively easy by applying estimation procedures. For instance 

when the transport volume between an origin and a destination is known, transport performance 

information (expressed in tonnekilometres) can be calculated by multiplying the volume by the 

distance between the regions. In this section an overview is given of how characteristics of the 

transport flows (cargo types, cargo characteristics, containerisation, number of TEUs, number of 

transport units) and transport performance information (tonnekm, vehiclekm/vesselkm, TEUkm) 

can be estimated. Along the project these estimation procedures will be further elaborated. 

4.8.1 Cargo types and characteristics 

Inclusion of cargo types and characteristics in the database has to be done by estimation since 

not all sources used in the topdown approach contain this type of information. 

Where the characteristics directly relate to the commodity itself , e.g. whether the goods are 

temperature controlled or ambient, or whether they are hazardous, it makes sense to relate these 

attributes to the commodity classification scheme, and to make the translation at an early stage 

of the processing so that this information is accessible in other stages. 

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4.8.2 Containerisation 

Determining the containerisation is a real challenge due to the lack of data and the 

unreliability/incompleteness of the available material. An important source in which a container 

indicator is included is the COMEXT data. Another important source is the maritime statistics 

of EUROSTAT in which container information is available. The use of this source in the 

method will be analysed. 

The ability to observe and measure levels of containerisation within COMEXT is typically 

limited to extraEU trade flows. However, intraEU flows also need to be covered within ETIS. 

One possibility is to use a methodology developed by MDS Transmodal in which a broad 

segmentation is made by commodity according to the expected handling requirements of the 

products in question. The trade data is analysed at 5 digit SITC level (3500 different 

commodity descriptions) and they are assigned to two main cargo types: 

· Nonunitised; 

· Unitised. 

The nonunitised set, can typically be excluded from an analysis of containerisation. Instead, it 

can be further subdivided into dry bulk, liquid bulk, semi bulk and general cargo categories. 

The ratios differ by corridor, so that for example, deep sea flows may have different 

characteristics compared to short sea. 

The unitised or “unitisable” set relates to commodities that are typically higher value goods that 

may be handled in containers, but which may also travel in road trailers. It is not possible to 

ascertain from a commodity description the actual mode of transport as this also depends upon 

the distance from origin to destination, the transport mode choices available, their costs, and in 

some cases upon the balance of containerised cargo in each direction. Therefore the assignment 

of the category ‘unitised’ a commodity is the limit of this process. 

Unitised flows can also converted into FEUs (forty foot equivalent container units), analogous 

to 4044 tonne truckloads. Again this is based upon the 5 digit SITC commodity, which 

improves the accuracy when there is a mix of commodity types within a single 2 digit 

definition. Like this, the volume traded can be expressed as unit loads without the actual mode 

being assigned. 

4.8.3 Number of transport units (vehicles/vessels) by type 

The determination of the number of transport units (number of vehicles for road, number of 

trains for rail and number of vessels for inland waterways and maritime transport) depends very 

much on the quality of the mode split data, and upon surveys providing profiles of vehicle type. 

The methodology depends upon knowing the mode of transport, the length of haul and the 

commodity. 

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For the rail and sea modes, the commodity classification can establish whether the goods are 

bulk or unitised. These headings can then be used to determine load factors in tonnes and FEUs 

respectively which will allow conversion to train numbers or ship numbers. In the case of the 

sea mode, there is a wide range of ship sizes, and in practice it may be easier to measure vessel 

flows (e.g. between ports) with reference to supply side data. For rail, provided that the mode 

share can be ascertained, conversions of tonnes or FEUs to train numbers could be usefully 

made. 

The road mode needs to be main focus of this procedure, as there is a wide variety of vehicle 

size possibilities, and there is no supplyside data (e.g. timetables) for road haulage services. 

The estimation needs to be informed by vehicle stock and survey data. 

There are differences in data availability by country, for instance in some national statistics up 

to 20 vehicle types are being distinguished where in other countries gaps exist. This data 

availability will be studied in the next phases in order to see whether improvements to the 

currently most used estimations are possible. 

In most cases the translation from tonnes to vehicles or vessels is done by one figure indicating 

the average number of tonnes per vehicle or vessel. Sometimes an estimate is made in this way 

of more than one type for instance van and truck. 

4.8.4 Number of TEUs 

Determining the number of TEUs will be done by estimation. Different determining factors are 

present in this respect amongst which the volume of the commodities and the weight which has 

to be compared with the maximum volume and weight of a TEU and of course the efficiency of 

transport. Tables are existing of the volume per weight of the different commodities. 

Different container types have different maximum weights like for instance: 

Max weight: 

· 20 foot container (1 TEU) with a maximum weight of 20 tonne and 

· 40 foot container (2 TEU) with a maximum weight of 23 tonne 

Often the TEU is determined by applying the average of 9 or 10 tonne per TEU. It will be seen 

whether this method can be refined with available data. 

MDS Transmodal have developed conversion tables, similar to those used to estimate other 

cargo characteristics to relate tonnes of a product at the 5 Digit SITC level to the expected 

number of 20’ or 40’ units, using estimated stowage factors (tonnes per 20’ and tonnes per 40’). 

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4.8.5 Transport performance information (tonnekm, vehicle km/vesselkm, TEU 

km) 

The estimation of transport performance information such as tonnekm, vehiclekm/vesselkm 

and TEUkm will be done in cooperation with the European transport network development 

since assignment on the network is required to determine the distance. 

Once the distance by mode has been determined between regions or by link in the transport 

network, transport performance information can be calculated by multiplying the transport 

volume information (in tonnes, in transport units and in TEUs) with the transport distance. This 

can be done for specific parts of the nework (for instance for the national territory of a country) 

or on a more aggregate level for total transport generated or attracted by a region or a country. 

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5 TRANSPORT CHAIN FREIGHT O/D MATRIX – DATA NEEDS, DATA 

COLLECTION AND REMAINING DATA GAPS 


In this chapter first the data needs for building a complete transport chain freight O/D matrix are 

described for an ideal situation (where all required data is available). Next, an overview of the 

data collection is given. Finally, data that is needed but that cannot be collected within the 

project is described, this is called the remaining data gaps. 

5.2 Data needs 

The exact structure of the data construction methods that are applied is dependent on the input 

to be found. In this section an overview is given of data needed in the database construction 

process in an ideal situation where all required data is available and all available data can be 

processed within certain limitations. 

In general different types of data need to be collected to be able to construct the freight transport 

database: 

· Trade data (movement of goods) 

· Transport data (movement of transport units) 

· Maritime transhipment data 

· Inland transhipment data 

· Container data 

· Vehicle/vessel movement data 

From the other datasets developed within ETIS the following is required: 

· Socioeconomic data 

· Network data 

· Transport sector data 

Each of the data types will be discussed. 

5.2.1 Trade data 

Since trade is the origin of all transport movements this data is the basis of the top down 

approach to be applied. A consistent source for all countries is needed. This source preferably 

should contain information about: 

· Origin (region, country) 

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· Destination (region, country) 

· Commodities, 

· Modes (at the national border, EU border, at origin, at destination?), 

· Containerisation, 

· Weight 

· Value 

· Route information: 

• border crossing 

• transhipment sea ports 

• transhipment other terminal(s) 

These variables should be collected in as much detail as possible. The ideal situation would be 

that all information needed to describe transport would be available from the trade registrations. 

Unfortunately this is not expected to be found. It should be attempted to come as close as 

possible to the ideal situation. Elements like region to region information or transhipment 

locations are unfortunately only available in very limited and/or fragmented cases. 

The following priority list can be followed for origin destination data: 

1. Region to region 

2. Region to country 

3. Country to country 

In the topdown approach of the NEAC model country to country data has been used from the 

COMEXT database since this is consistent for all EU countries. Region to country trade data 

from national sources is then used to include the regional detail. 

The following sources are considered: 

· EUROSTAT COMEXT 

· National trade data for all core countries 

· UN trade data 

5.2.2 Transport data 

Transport data can be used for different purposes in the topdown approach and should be 

collected for all modes. All other variables mentioned under the trade flows should also be 

considered here as much as possible. The first distinction to be made is: 

· International transport 

· Domestic transport 

· Container or Loading units flows 

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If trade data is available but excluding regional detail then regional international transport 

information can be used to estimate the regional detail. Here the following priority list can be 

used: 


2. Region to country 

3. Country to country 

International transport data without regional detail are not of use for a topdown database 

construction approach. Only in case of a database estimation procedure aggregated international 

transport data can be very useful. This has to be considered in case no other data can be found. 

Domestic transport data has the following priority list: 


2. Region incoming and region outgoing 

3. Country total 

The following transport data sources are considered: 

· EUROSTAT New Cronos 

· National transport data sources 

5.2.3 Maritime transhipment data 

This type of transhipment information is best known for the major ports or port countries in 

Europe. For these ports very reliable and complete information used to be available. After 1992 

the availability and quality of this type of data has decreased or the data are not available 

anymore (information about ports and modes have become optional for intraEU flows in the 

Intrastat system). For other ports in the EU availability of data has always been limited. It has to 

be attempted to find alternative useful sources to fill this data gap. 

Currently Eurostat collects porttoport data from the Member States according to the Maritime 

Directive (see ”Council directive 95/64/EC of 8 December 1995 on statistical returns in respect 

of carriage of goods and passengers by sea”). By the end of the testing method, a request has 

been made to EUROSTAT by DGTREN to make the data available for the construction of the 

ETIS reference database. The data have not been received yet at moment of writing. When the 

data becomes available, it will be used in the methods. This data contains no information about 

transhipment, but it does contain information about goods loaded and unloaded in ports in 

relation with partner ports. 

The following maritime transhipment data sources are considered: 

· National transhipment data sources 

· Ports 

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5.2.4 Container data 

Not only data is needed about vehicle movements (transport data) or the movements of the 

commodities in them (trade data) but also information on the containers is of interest. 

Containers can have different origins and destinations than the commodities in them and the 

vehicles transporting them. These data therefore are of at least comparable value for the ETIS 

reference database. An example of such data sources is the Piers database that describes the 

movements of containers between the USA and Europe where multiple transhipment is 

included. It will be attempted to make a distinction between filled and empty containers. 

The following container data sources are considered: 

· EUROSTAT COMEXT (container indicator, only for extraEU trade) 

· UIRR 

· ICF 

· Piers database 

· Ports 

· Inland terminals 

5.2.5 Vehicle/vessel movement data 

This type of information is available in several sources. 

The following vehicle/vessel movement data sources are considered: 

· EUROSTAT New Cronos 

· National transport statistics 

Special attention has to be paid to movements of empty vehicle/vessels 

5.2.6 Relation with other datasets in the ETIS reference database 

State of the art 

All state of the art information collected will be incorporated in the freight demand dataset. 

Socioeconomic dataset 

Where data gaps remain estimation procedure must be applied. These estimation procedures for 

a large part rely on socioeconomic input data. The required socioeconomic data can be 

different for each model. The following data can be seen as frequently used. 

· Population by sex and age classes (04, 59, ..., 6570, > 70) 

· Gross value added by three sectors: 

• Agriculture, fishery, forestry 

• Industry 

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• Services 

· Total GDP and GDP/Capita 

· GDP by three sectors: 

• Agriculture, fishery, forestry 

• Industry 

• Services 

· GDP by other sectors: 

• Basic metals 

• Metal products 

• Chemical production; petrol products 

• Chemical production; other products 

• Mining & quarrying production 

• Construction 

• Electricity/Gas/Water production 

• Private final consumption 

• Food Consumption 

• Residential Construction 

· Fuel prices for unleaded fuel and diesel 

· Vectorized cards for NUTS3 borders 

European transport network dataset 

Network data are mainly required for the freight demand dataset to determine the distance 

between regions that can be used to calculate for instance the transport performance in tonnekilometres. 

The freight O/D matrix will be used as input for the European transport network 

dataset to make assignments on the transport network in order to determine the intensities on 

each link of the network. 

Freight transport service and cost dataset 

Data about the transport sectors are needed for all countries/areas to be included in the system. 

Here variables like costs per hour and by kilometre are important but also restrictions on the 

commodities or weight to be transported. 

External effects dataset 

The freight O/D matrix will be used as input for the external effects dataset to calculate external 

effects between O/D relations or on network level (via assignments). 

Synthesis and dissemination 

The freight demand dataset will be used for the synthesis and dissemination. 

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5.3 Data collection 

Based on the description of the data needs for the transport chain freight O/D matrix in the 

previous sections many (potential) data providers have been contacted. For each of these data 

providers it has been investigated what data is available and under what conditions the data can 

be made available for use in the ETIS reference database project. An overview of the current 

status of the data collection from different data sources is given in Annex F. Tables 5.1, 5.2 and 

5.3 give an overview of available data by source and/or country for trade data, international 

transport data and domestic transport data respectively. 

If a field in the tables is indicated by ‘o’ this means that the availability of the data is known. If 

a field is empty this can mean either that the data is not available or that the availability of the 

data is unknown. It is noticed that this table presents the status of the data collection up to the 

test of the methodology; it gives a rough impression of the data availability. After the test of the 

methodology, the data collection will be continued where necessary in order to collect as much 

relevant information as possible within the limitations of the project. 

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Table 5.1: 

Overview of available trade data 

Trade data 

Type of data 

Sources country to region to transport commodity container transhipment volume trade 

country country mode type indicator location(s) in tonnes value 

Supranational 

sources 

UN Comtrade o o o (partly) o 

UNECE 

Common database o o o 

Comext o o o o o o 

National sources 

France o o o o o 

Belgium o o o o o 

Luxembourg 

Netherlands 

Germany o o o o o 

Italy 

United Kingdom o o* o o* o o 

Ireland o o o o 

Denmark 

Greece 

Portugal 

Spain o o o o o o o 

Norway o o o o 

Sweden 

Finland 

Switzerland o o o o 

Austria 

Poland o o o o o o 

Czech Rep. o o o o 

Slovak Rep. o o o o 

Hungary o o o o 

Estonia o o o o 

Latvia o o o o 

Lithuania o o o o o 

Slovenia o o o 

Malta 

Cyprus 

* Mode and transhipment information is known for UK trade with countries outside the EU. 

Table 5.2 

Overview of available international transport data 

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International 

transport 

data 

Type of data 

transhipment/ 

Sources country to region to region to by transport commodity container border volume performance 

Supranational 

sources 

country country region region mode Type indicator location(s) in tonnes value in tonnekm 

NewCronos o o o o O o o o 

CAFT o o o o o o o 

UIRR o o o o 

ICF o o o o (TEU) 


France o o o o o o o 

Belgium o o o o o o o o 

Luxembourg 

Netherlands o o o o o o 

Germany o o o o o o 

Italy o o o o o 

UK o o o o 


Denmark o o o o 

Greece 

Portugal 

Spain 

Norway 

Sweden o o o o o 

Finland o o o o 

Switzerland 

Austria o o o o 

Poland 


Slovak Rep. 

Hungary 

Estonia 

Latvia 

Lithuania 

Slovenia 

Malta 

Cyprus 

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Table 5.3: 

Domestic 

transport data 

Overview of available domestic transport data 

Type of data 

Sources region to transport commodity container volume performance 

region 

by 

region mode type indicator in tonnes value in tonnekm 

Supranational 

sources 

NewCronos o o o o 


France o o o o o o 

Belgium o o o o 

Luxembourg 

Netherlands o o o o 

Germany o o o o 

Italy o o o o 

United Kingdom o o o o 


Denmark o o o o 

Greece 

Portugal o o o o 

Spain o o o o 

Norway o o o o 

Sweden o o o o 

Finland o o o o 

Switzerland o o o o 

Austria o o o o 

Poland o o o o 


Slovak Rep. o o o o 

Hungary o o o o 

Estonia o o o 

Latvia o o o 

Lithuania o o o 

Slovenia 

Malta 

Cyprus 

From these overviews of datasources and available data it becomes clear that besides data from 

Eurostat, data from many other data sources is required for the construction of the transport 

chain freight O/D matrix. 

Since the data collection is a very time consuming process, there are still negotiations going on 

about the conditions of the data delivery. The data collection process will therefore continue 

after the test phase has finished. For the test phase enough data had been collected in order to be 

able to test the different methods. 

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5.4 Remaining data gaps 

From the data collection process it becomes clear that not all data is available that is needed in 

an ideal situation. In this section the gaps between the needed data and the available data is 

described together with the consequences that follow from these gaps. 

5.4.1 Data available at Eurostat not available for the ETIS project 

In order to build for instance a regiontoregion freight O/D matrix data is required on a regional 

level. Regional data is available in New Cronos from Eurostat, but this data has limited detail. 

Since the Member States deliver region to region transport data (for road transport) to Eurostat, 

a request has been made to Eurostat to ask for this data. Due to restrictions in the statistical law, 

Eurostat is not allowed to provide the data for use in the ETIS project. In response it has been 

attempted to collect these data from the Member States. Some Member States have delivered 

the data where others reply that the data has already been delivered to Eurostat and therefore 

does not have to be delivered again tot the European Commission. This illustrates that in some 

cases more structural organisational changes are needed for the data collection by the European 

Commission. During the second open conference of ETIS Eurostat has noted this problem and 

will discuss with the Member states what would be possible solutions. 

Since it is likely that the international road transport on region to region level (NUTS3) will not 

be made available in time to be used for the for the ETIS pilot by Eurostat, the international 

region to region transport flows on NUTS2 level will be estimated. 

5.4.2 Registration of mode of transport for intraEU trade becomes optional in Eurostat 

trade statistics 

For the year 2000, the variable mode of transport is still included in the Comext trade by mode 

data (from Eurostat). From 1 January 2001, this variable will be still included in the data, but 

the variable will be less reliable. The reason for this is that the collection of mode of transport in 

intraEU trade has become optional for Member States and applies only to those providers 

above a certain threshold. This has consequences for a future update of the ETIS reference 

database. 

5.4.3 Transhipment data of seaports and inland terminals 

In the data collection process it became clear that transhipment data is very dispersed. For 

transhipment in seaports data is available, for transhipment at inland terminals data is not 

available or only on a very aggregate level. Since methods to fill this data gap are complex and 

very time consuming to develop, it has been decided to leave transhipment at inland terminals 

out of the freight O/D matrix. 

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5.4.4 Data availability in the accession countries 

Since these countries have become member of the EU from May 2004, the collection of data in 

these countries will change since they have to apply the Eurostat directives. In ETIS a 

repeatable method has to be developed, therefore it makes no sense to spend a lot of time and 

budget in using the available data for the accession countries and apply and develop methods 

and models to fill the data gaps while it is known that the data situation in these countries will 

change significantly in the near future. The data collection process in the accession countries 

was very time consuming, it is a hard job to get in contact with the right persons at the different 

institutes, data is not available on the required level of detail and limited response has been 

received. Because of these reasons it has been decided to make use of a combination of results 

from the ‘Traffic Forecast of the Ten PanEuropean Transport Corridors of Helsinki’ project, 

the INTERMODA project, NEAC, the TENSTAC project, UN data and data already available 

for the accession countries to estimate the transport flows within and in relation with the 

accession countries. Once the Eurostat directives are applied in the accession countries the same 

methodology as for the current EU countries can be applied in order to build the freight O/D 

matrix. 

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6 DESCRIPTION OF THE WP 3 TESTING PHASE AND ITS RESULTS 

6.1 General description of the testing phase 

In the testing phase the proposed methods have been tested in order to find out whether they are 

feasible and to determine whether and how the methods should be modified. 

In WP 3 in total 6 different methods are tested in this phase. In the first method a countrytocountry 

matrix in constructed. The second method determines where transhipment takes place 

along the transport chain. To store this information in the database, a record structure with two 

transhipment locations is introduced. Then in the third method the regiontoregion flows are 

estimated and included in the database. In the fourth step (pure) domestic transport is included. 

After these four steps a complete region to region freight O/D transport chain matrix results. In 

method 5 cargo characteristics and transport unit information is added to the database. Finally in 

method 6 information about the transport performance (tonnekilometres, number of vehicles, 

vehiclekilometres, number of TEUs, TEUkilometres) is included. 

6.2 Tested methodologies 

In this paragraph the applied methods are described. 

Method 1: Development of countrytocountry matrix 

Data source: COMEXT database from Eurostat 

The COMEXT database, published by EUROSTAT contains detailed trade data harmonised for 

all EU member states. The database contains the variables described in table 6.1. 

Table 6.1 Variables included in the COMEXT database 

Variable 

Description 

DECLARANT Country Reporting the Trade : EU Member States 

PARTNER 

Trading Partner: All countries 

PRODUCT CODE Commodity Code, using the 8 Digit Combined Nomenclature (CN8) 

FLOW 

Import or Export 

STAT REGIME Statistical Regime: General or Special Trade 

PERIOD 

Time Period e.g. 12 Month Period 

VALUE 

‘000s of EURO 

QUANTITY Tonnes 

SUP_QUANTITY Additional Quantity, e.g. Square Metres 

Identification of outliers 

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The COMEXT database for the Year 2000 has been used for the testing phase of ETIS. The 

first test has considered whether it is necessary to apply additional error checking routines to 

avoid the inclusion of data errors in the subsequent O/D matrices. This has been achieved by 

comparing ‘smoothed’ data to the raw data to measure the impact of erratic values in the 

database. 

A technique for identifying outliers (errors or “erratics”) has been developed within the MDS 

Transmodal trade forecasting model, taking into account a long time series of trade data. 

During this process, the COMEXT data is converted into time series vectors for individual trade 

flows (e.g. French exports of SITC 56 in tonnes to Italy) for quarterly time periods covering 

approximately fifteen years. The smoothing software samples four data points for each year and 

calculates the mean and the standard deviation for that year. It then compares each year's mean 

and standard deviation with all the others. Then if there are any years with unusual levels of 

variance, they are investigated by the software, and according to certain thresholds individual 

quarterly values may be marked as outliers and the software will replace them with interpolated 

values. It means that normally erratic series will be left untouched, but erratic points or 

sequences within normally stable series will be changed. Every year, new data is collected, and 

the process is repeated, so it is possible that what is regarded as an outlier may change over time 

as the software learns more about the time series. 

The use of the smoothing algorithm can be illustrated, by comparing the smoothed data to the 

original data. 

In 2000, imports into EU countries amounted to 2.501 billion tonnes according to COMEXT. 

After smoothing the estimate was 2.391 billion tonnes, a change of only 4%. The largest 

absolute error in a single 2 digit SITC category is 13 million tonnes, for SITC 33, petroleum. 

However this is only a 2% difference within that category. The largest percentage error is for 

SITC 83, travel goods, with a 63% percent difference. However this only amounts to an 

absolute difference of 1.133 million tonnes. Most of the difference can be traced to a figure of 

1.079 million tonnes for travel goods between the UK and Germany. 

Looking at the same trade flow using German export data a total of 0.001 million tonnes can be 

found, a level that agrees more readily with the smoothed data. This difference (1000 times) is 

untypical however. Absolute differences are typically about 1 million tonnes per 2 digit SITC 

category, and relative differences are typically about 6%. It should also be noted that 

differences between smoothed and unsmoothed series do not necessarily imply that the unsmoothed 

series contains errors, only values that are unlikely to be repeated. 

At these levels, and given the scope of ETIS, the potential impacts of measurement errors are 

not alarming, particularly when the annual version of COMEXT is used. However, the example 

related above does suggest that a high level comparison between the trade data used within 

ETIS and the data based upon a smoothed quarterly time series will reveal a small number of 

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important differences that can be corrected manually. The ability to compare counterflows in 

intra EU data is also useful in this context. 

Conversion of commodity code 

The COMEXT database is published using the international 8 digit combined nomenclature 

(CN8) system. This has the advantage that the flows can be readily and unambiguously 

converted into other (more aggregated) systems such as the Standard International Trade 

Classification (SITC) and Standard Goods Classification for Transport Statistics/Revised 

(NST/R). Conversion tables are published on EUROSTAT’s classification server. See 

http://europa.eu.int/comm/eurostat/ramon/. 

Selection between import and export registration 

The Comext data contains a for extraEU trade one registration, the registration of import or 

export of the EU country. For intraEU trade the Comext data contains two registrations for the 

same flow, once registered as export of the origin country and once registered as import of the 

destination country. In an ideal case, the transport volumes in both registrations are the same. 

Unfortunately, in many cases the registrations are not the same. The example described in the 

table below illustrates this. 

Table 6.2 

Origin country 

Example of differences in registration for the same flow 

Destination 

country 

Commodity Registration Transport volume in 

tonnes 

France The Netherlands Cereals Import registration the Netherlands 3611453 

France The Netherlands Cereals Export registration France 4252118 

In this example the export registration is about 640.000 tonnes higher than the import 

registration. In the database only one value will be included for the trade flow of cereals from 

France to the Netherlands, thus these two registrations have to be converted in a single 

registration. The trade flows are registered according to the INTRASTAT system. The rules that 

have to be followed in the INTRASTAT system give no indication that the import or the export 

registration is more reliable (in the past the import registration was considered to be more 

reliable). Since it cannot be decided what registration is more reliable, it is decided that both 

registrations are even reliable and therefore the average value of the import and the export 

registration is taken as the transport volume on this relation (in the example above the transport 

volume becomes 3931786 tonnes). In order to keep information about the difference between 

the import and the export registration, two variables are added to the data. One variable 

indicates whether the difference between the import and the export registration is more than 

500.000 tonnes (in the example given above this is the case), another variable indicates the 

relative difference between the average value and the import and export registrations (in the 

example above this percentage is 8%, indicating that the transport volume could actually be 8% 

lower or 8% higher). These indicators for the difference between import and export registration 

will be used in the second method for the identification of confusion between trade and 

transport. 

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Recoding of variables and modification of record structure 

After the selection between import and export registration has been made, the origin country 

and the destination country variables are recoded into the ETIS reference database country 

classification. Besides, the record structure is modified: 

· Origin country (ETIS country classification) 

· Destination country (ETIS country classification) 

· NSTR 2 digit commodity classification 

· Value of the goods (in Euro) 

· Volume of the goods (in tonnes) 

· Indicator absolute difference between import and export registration 

· Indicator relative difference between import and export registration 

· Indicator for intraEU or extraEU trade 

Adding information about the mode of transport 

The used Comext database has the advantage that it includes information about the traded goods 

on a very detailed level which makes it possible to add information about the cargo 

characteristics as described above. A disadvantage of this database is that it includes no 

information about the mode of transport. Besides the Comext database used up to now, there is 

another Comext database called ‘Comext trade by mode’ that includes less detailed commodity 

information (on NSTR 3 digit level), but it includes information about the mode of transport. 

The definition of the transport mode included in the Comext trade by mode database is as 

follows: 

· For intraEU trade the mode is defined as the active means of transport with which goods 

are presumed to enter/leave the statistical territory of the Member State for import/export; 

· For extraEU trade the mode is defined as the active means of transport with which goods 

are presumed to enter/leave the statistical territory of the European Community for 

import/export. 

For extraEU trade there is one registration with one modalsplit that gives the mode of 

transport for the goods entering or leaving the European Community. For intraEU trade the 

situation is more complex, there are two registrations with two modalsplits that give the mode 

of transport on the territory of the exporting Member State and the mode of transport on the 

territory of the importing Member State. In the same way as for the different registrations of the 

total trade volumes, the different registrations of the modalsplit have to be converted into one 

registration of the modal split. 

The following method is applied: 

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1. In the Comext trade by mode database the distinguished modes are recoded into the 

transport modes used in ETIS for freight transport (road, rail, inland waterways, 

maritime, rest); 

2. From the Comext trade by mode database for each country to country by commodity 

classification by cargo characteristics combination the modalsplit is determined once in 

the export registration and once in the import registration, the trade volumes are scaled to 

the volumes as selected between the import and the export registration; 

3. A rule is applied to determine the origin mode – destination mode combinations: 

a. For each transport mode, the minimum volume is determined in the import and the 

export registration, for this trade volume the origin mode is equal to the destination 

mode. For instance if the export registration gives 1000 tonnes trade by road and 

the import registration gives 1500 tonnes trade by road, then the trade volume of 

the combination origin mode road – destination mode road becomes 1000 tonnes. 

b. For the trade volumes left after step a the origin mode and the destination mode 

cannot be the same. An order is determined in the modes of transport: maritime 

transport has the highest order, followed by inland waterways transport, rail 

transport, road transport and finally the rest category has the lowest order. The 

trade volume of the origin mode with the highest order is matched first with the 

destination mode with the highest order, then with the destination mode with the 

following order, etc. until the total trade volume of the origin mode is matched with 

the destination mode. Then for the origin mode with the next order the same 

procedure is applied etc. until all origin modes have been matched with destination 

modes. 

The example in the table below illustrates the effect of this procedure. 

Table 6.3 

Modes in export registration 

Example of procedure to match origin mode and destination mode 

Modes in import registration 

Road 2000 tonnes Road 1700 tonnes 

Rail 500 tonnes Rail 600 tonnes 

Inland waterways 500 tonnes Inland waterways 300 tonnes 

Maritime 1500 tonnes Maritime 1900 tonnes 

Rest 200 tonnes Rest 200 tonnes 

The method results in the following origin mode – destination mode combinations 

Origin mode Destination mode Transport volume Remark 

Road Road 1700 tonnes Direct transport 

Rail Rail 500 tonnes Direct transport 

Inland waterways Inland waterways 300 tonnes Direct transport 

Maritime Maritime 1500 tonnes Direct transport 

Rest Rest 200 tonnes Direct transport 

Inland waterways Maritime 200 tonnes Indication transhipment 

Road Maritime 200 tonnes Indication transhipment 

Road Rail 100 tonnes Indication transhipment 

In case the origin mode is different than the destination mode, this is interpretated as an 

indication that transhipment might have taken place along the route. In the next methods this 

information is used to include transhipment information to the database. 

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The last step of this method is the modification of the record structure. The record structure is 

modified in such a way that a transport chain with two transhipment locations fits in the 

structure: 


· First transhipment location (still empty in this phase) 

· Second transhipment location (still empty in this phase) 



· Mode between transhipment locations 








In the second method the transhipment locations will be filled. 

Method 2: Estimation of transhipment 

In the first method the database structure has been set up in such a way that information for 

maximum two transhipment locations along the transport chain can be stored. In the first 

method, the transhipment locations are still empty. In the second method the transhipment 

locations will be estimated. Since it is not feasible to estimate transhipment at inland terminals 

in the ETIS project, only transhipment at seaports is included. 

For the estimation of the transhipment locations two types of flows are distinguished: 

· Transit flows; goods transhipped in a port with both origin and destination outside the 

country where the port is located (either mode before or mode after transhipment is sea 

transport); 

· Import and export by sea; Import: transport by sea from a country to a port (located in a 

different country) where the goods are transhipped after which they are transported by a 

land mode to the destination region (which is located in the same country as the port); 

Export: transport from a region by a land mode to a port (which is located in the same 

region as the origin region) where the goods are transhipped after which they are 

transported by sea to a country (which is different than the origin country). 

In this method first the transit flows are estimated and added to the O/D matrix, secondly the 

import and export by sea is estimated and added to the O/D matrix. 

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Transit flows 

Data about transit in seaports is available for ports in Belgium, the Netherlands and Germany, 

for ports in other countries no data is available for transit. There are some problems with this 

data (intraEU transit is not included in the Belgium data, transit through the Netherlands is not 

collected anymore for the year 2000), but these gaps can be filled by applying estimation 

procedures in order to construct a complete and consistent transit database. This data has the 

following format: 

· Origin country (for instance Southern America) 

· Transhipment port (for instance Hamburg in Germany) 

· Destination country (for instance Austria) 

· Mode before transhipment (for instance sea transport) 

· Mode after transhipment (for instance road transport) 

· Commodity type (according to the NSTR2 or NSTR1 classification) 

· Volume of the goods (weight in tonnes) 

These transit flows have to included in the O/D matrix in such a way that double countings are 

avoided. This is done in three steps: 

1. Match transit flows with international trade flows; 

2. Make corrections for confusion between trade and transport flows; 

3. Add remaining transit flows. 

Match transit flows with international trade flows 

In the first step the transit flows through ports in Belgium, the Netherlands and Germany are 

matched with the international trade flows in the modified Comext database resulting from 

method 1. 

An example how these flows are matched is given in table 6.4. 

Table 6.4 

Origin 

region 

Mode 

origin 

Matching transit flows with international trade flows 

Transh. 

region 

Mode Transh. region Mode 

destination 

Destination 

region 

Volume 

(tonnes) 

Flow type 

USA sea Germany 25000 International 

USA sea Rotterdam 

(NL) 

rail Germany 15000 Transit 

In the Comext database it is known that 25.000 tonnes of a specific commodity is transported 

from the USA to Germany with mode sea (for extraEU trade the mode of transport at the 

border of the Community). From the transit data it is known that 15.000 tonnes of a specific 

commodity is transported by sea from the USA to Rotterdam where the goods are transhipped 

onto mode rail and then transported to Germany. In this example all transit can be matched with 

the international trade, in the O/D matrix the international trade will be reduced by the transit 

flows (25.000 – 15.000 = 10.000 tonnes) and a record will be added to the O/D matrix for the 

transit through Rotterdam with a volume of 15.000 tonnes. 

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In this example the volume of the transit is lower than the international trade flow (between 

countries by commodity type). It also occurs that the transit flow can be matched, but that the 

transit flow is higher than the international trade flow. In such a case the total international trade 

flow is replaced by the transit flow (in order to include transhipment data in the O/D matrix). 

The remainder of the transit flow cannot be matched (country by country by commodity type), 

with these flows is dealt in the next two steps. Another possibility is that the transit flow cannot 

be matched at all (country by country by commodity type). These flows are also being dealt 

with in the next steps. 

From the total amount of about 190 million tonnes transit through Belgium, the Netherlands and 

Germany about 115 million tonnes can be matched with the international trade flows based on 

the country to country by commodity type relation. 

Make corrections for confusion between trade and transport flows 

In method 1 it has been noticed that for some flows large differences exist between import and 

export registration. In the previous step it appears that sometimes transit flows are larger or 

cannot be matched to international trade flows between the same countries and for the same 

commodity type. In this step these observations are further investigated in order to find a 

clarification and to see in what way the flows should be included in the O/D matrix. 

There is a relation between international trade flows with large differences between import and 

export registration and transit flows that cannot be matched with international trade flows. This 

relation is clarified by an example. In the Comext data there are two registrations for the trade 

of NSTR group 41 (iron ore) from the Netherlands to Germany: export registration of the 

Netherlands equals 8.9 million tonnes, import registration of Germany equals 26.9 million 

tonnes. There is a large gap between these registrations, besides, the Netherlands does not 

produce iron ore and thus the Netherlands cannot export these commodities. A possible 

explanation for this is that it does not concern a trade flow from the Netherlands to Germany, 

but a transit flow from a country via the Netherlands to Germany. The transit flows that cannot 

be matched to the international trade flows contains flows from Middle and Southern America 

via Rotterdam in the Netherlands to Germany. This confirms the idea that trade and transport 

are mixed up. The trade flow from Middle and Southern America via Rotterdam to Germany is 

registered by Germany as import from the Netherlands (the transport of the goods is considered 

as trade). This explains why the export registration of the Netherlands is much lower than the 

import registration of Germany (this flow is no trade of the Netherlands) and why the transit 

flow cannot be matched with the international trade data (the trade flow has a different origin 

country). This confusion between trade and transport is corrected in the O/D matrix by 

including the transit flows (in the example the flow from Middle and Southern America via 

Rotterdam to Germany including information about the transport modes used) and reducing the 

trade flow (trade from the Netherlands to Germany) with the same volume. 

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This procedure is applied for all trade flows in the O/D matrix that have large differences 

between the import and the export registration as indicated by the variables indicating the 

absolute and the relative difference (see description method 1) and that can be linked to transit 

flows that have not been matched with international trade flows. 

In this step about 55 million tonnes of the total of 75 million tonnes that could not be matched in 

the first step is linked to international trade flows, simultaneously a correction for the confusion 

between trade and transport has been applied. 

Add remaining transit flows 

The remaining transit flows of about 20 million tonnes that could not be matched to 

international trade flows in the first and second step will be added to the O/D matrix. 

Finally, the commodity type information and the cargo characteristics information is added to 

the transit flows that have been included in the O/D matrix based on the distribution of the 

international trade flows. 

Import and export by sea 

In the test of the method data about import and export by sea for Spain and Germany is used in 

order to construct complete (intraEU) maritime transport chains. For Spain trade data (source 

AEAT) is available including information about the border crossing region. Depending on the 

transport mode used for goods entering or leaving Spain, the border regions are equal to the port 

regions. The transport modes for the hinterland transport have been estimated based on 

domestic transport information. For Germany detailed data is available for German import and 

export via the ports of Hamburg and Bremen (although some missing elements have been 

estimated). Data about import and export via other German ports has been constructed by 

combining Eurostat port data with maritime data from SBA and national transport data from 

SBA and KBA. Import and export by sea data has the following format: 

· Origin region (NUTS2 region in case of export, otherwise country) 

· Transhipment region (NUTS2 region in the country) 

· Destination region (NUTS2 region in case of import, otherwise country) 



· Commodity type (NSTR2 or NSTR1 classification) 

· Transport volume (weight in tonnes) 

This data is used to estimate the origin/destination region, the transhipment region in the 

origin/destination region and the transport mode of the hinterland transport in the 

origin/destination country by commodity type (NSTR2 or NSTR1 classification) and by 

destination/origin country. Specific methods have been applied for different types of records in 

the database with two possible transhipment locations. It is noticed that for instance for 

maritime transport from Spain to Germany the port distribution in a country is estimated 

independently of the port distribution in the partner country (port distribution in Spain is 

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independently estimated of the port distribution in Germany, port distribution in Germany is 

independently estimated of the port distribution in Spain). After this method has been applied 

origin/destination regions (on NUTS2 level), transhipment regions (on NUTS2 level) and 

hinterland modes are available for all maritime transport leaving or entering Spain and 

Germany. Since the method has been applied for Spain and Germany, the complete maritime 

transport chain including all information on the required level of detail results from this method. 

Annual seaborne transport data Eurostat 

New Cronos from Eurostat contains (amongst others) the following maritime data: Annual 

seaborne transport between reporting countries/MCA (Main Coastal Area) and partner 

countries/MCA, by direction, type of cargo and nationality of vessel (in 1000 tonnes), year 

2000. Once all maritime transport chains have been estimated, the results will be validated (and 

eventually modified) using the seaborne transport data from Eurostat. 

Porttoport transport data Eurostat 

Data about maritime transport with information on porttoport level is collected by Eurostat 

(see ”Council directive 95/64/EC of 8 December 1995 on statistical returns in respect of carriage 

of goods and passengers by sea”). When this data becomes available (has already been 

requested by DGTREN), the method will be modified in order to include use of this data in 

order to produce maritime transport results that are more reliable (in the current method the port 

distribution on the origin/destination side is being estimated independently of the port 

distribution on the destination/origin side, the porttoport data from Eurostat can be used to 

determine the exact porttoport relations). When the porttoport data becomes available, the 

validation with the seaborne transport data from Eurostat (described above) becomes redundant. 

With the porttoport data a distinction can be made between an active mode and a passive 

mode for maritime transport. 

Method 3: Estimation of a regiontoregion matrix 

Although Eurostat’s COMEXT database is limited in its spatial scope to country to country 

transactions, many European countries do calculate regional versions of their own trade statistics. 

The Concerted Action on Short Sea Shipping (CASSS) for DGTREN, 1998/9, led by NTUA and 

ISL and in which NEA and MDS have been working on the statistical part, developed techniques 

for employing this existing regional data to build regional O/D matrices. These methods are being 

transferred to ETIS. 

The problem is a familiar one. The object is to construct an O/D matrix, where the cells represent 

the traffic ‘T’ between pairs of regions. The available data allows the row and column totals of the 

matrix to be measured, but an estimation procedure is required to fill the cells, for example: 

D 1 D 2 D 3 Total 

O 1 ΣO 1 

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O 2 ΣO 2 

Total ΣD 1 ΣD 2 ΣD 3 ΣD 1..3 (= ΣO 1..2 ) 

In this example, the sets of ‘O’s and ‘D’s belong to specific countries. Country to country totals are 

known, as are regions to/from country totals for a significant proportion of EU and accession 

countries. 

The solution attempted within CASSS was to derive a set of gravity models, one for each SITC 

commodity relating the volume of traffic in a given O/D cell to the distance between the origin and 

destination, and the size of the regions, measured by the total volumes of the commodity produced 

and consumed. Other measures of region ‘mass’ could be hypothesised, but in the CASSS study 

most were found to be closely correlated to the total import and export volumes. 

The functional form used was: 

T p = d n e md E p I p 

Where: 

T p 

d 

E p 

I p 

n,m 

= tonnes of product ‘p’ lifted 

= distance (kms) 

= Exports of product ‘p’ 

= Imports of product ‘p’ 

are the parameters to be estimated 

The use of a function that combines the exponential and powern (where n is negative) curves 

provides greater control in circumstances where distance is close to zero, than would be the case if 

only the powern curve were used. This is important if the parameters are being estimated for zone 

pairs where the distances are relatively high and then transferred to zone pairs where the distances 

could be small. 

The intention was to develop a technique that could estimate parameters for, potentially, a highly 

disaggregated set of commodities, and in which a number of nonlinear functions could be tested 

and compared. The parameters would be estimated at the national level, for which large volumes 

of data are available, using regional information where possible. The same parameters would then 

be transferred to compute values at a regionregion scale. So, for example, if at a national scale, the 

propensity to trade ( T p /E p I p ) for Portugal and Spain is higher than for Portugal and Finland, for a 

given product, it seems sensible to conclude that the propensity to trade for adjacent Portuguese and 

Spanish regions may be higher than for distant Portuguese and Spanish regions. 

It also appears sensible to distinguish between product groups, as, for example, any distance decay 

effects may be more significant for low value, high volume goods than for higher value densities. 

This is exemplified using results from the 1996/7 study, for two contrasting commodities. 

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Distance Decay by Commodity 

120 

Propensity to Trade (Index) 

100 

80 

60 

40 

20 

Ores and Scrap 

Scientific Machinery 

 

100 

200 

300 

400 

500 

600 

700 

800 

900 

1000 

1100 

1200 

1300 

1400 

1500 

1600 

1700 

1800 

1900 

2000 

Distance (Kms) 

The parameters calculated for these commodities indicate that distance decay is a relevant factor at 

the regional scale, with a high degree of impact between 200 and 1000 km. They also indicate that 

value density is a factor in determining the shape of the curve. 

The gravity model is used to estimate regionregion traffics which in turn, are then used to seed the 

O/D matrix and a furnessing algorithm is then used to constrain the matrix to its (known) row and 

column totals. 

The estimation of the ‘n’ and ‘m’ parameters depends on a multidimensional optimisation 

technique known as the downhill simplex method, or “amoeba”. 5 This is a relatively simple search 

algorithm that can be applied to a wide range of functional forms, such that the calling routines can 

specify parameter ranges, evaluation criteria and numerical precision. It does not involve the use of 

derivatives or statistical methods such as regression analysis. 

A test was carried out in which a version of the gravity formula was used to generate some ‘real’ 

data. A random number generator was then used to disturb these results within preset ranges, and 

these ‘sample’ points were then fed into a software routine that used the amoeba algorithm to detect 

the parameters. 

5 

Press WH, Teukolsky SA, Vetterling WT, Flannery BP, 1992, Numerical Recipes in C, Cambridge 

University Press 

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Tonnes Lifte d 

100000.0 

True Plus Error 

TRUE 

Estimated 

Tonnes 

10000.0 

50 

100 

150 

200 

250 

300 

350 

400 

450 

500 

550 

Km s 

600 

650 

700 

750 

800 

850 

900 

950 

1000 

The figure shows the ‘true’ relationship between distance and traffic generated with the solid line. 

The ‘sample’ points are shown as dots (note that the scale is logarithmic), and the error term is as 

much as plus/minus 50% of the actual. The broken line represents the curve that the amoeba 

algorithm estimates from the sample so that the sum of the errors visavis the sample points is 

minimized. This indicates a very good approximation of the original function, from only 20 sample 

points, which improves as the error term is reduced. The test implies that this simple estimation 

technique provides an accurate and computationally effective method for estimating gravity model 

parameters where the functions are nonlinear, and where a large number of models for different 

commodities are required. 

Although parameters were calculated within CASSS it now makes sense to update these results 

with the method developed here and to use a new commodity classification system. Conversion 

tables could be used but recalculation is favoured allowing more base year data to be included in 

the estimation, and different functional forms to be compared. 

The matrix estimation method can be illustrated with trade data. 

The Year 2000 COMEXT database shows the following patterns of trade under the SITC heading 

‘89’ Miscellaneous Manufactures, for ten countries. 

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Year 2000: Trade Volumes, Tonnes, SITC 89, Miscellaneous Manufactures 

F NL D IT UK IRL DK EL PT Total 

F 816,186 

NL 667,127 

D 1,403,972 

IT 960,777 

UK 519,952 

IRL 141,677 

DK 125,798 

EL 22,545 

PT 68,102 

Total 1,149,272 406,307 939,382 304,014 986,052 158,838 183,114 500,563 98,594 4,726,136 

In fact, all the cells in this table are known, but if region to region totals rather than country to 

country totals are sought, a method has to be found to complete the matrix. Therefore the test 

has been carried out at the higher level so that different estimation methods can be compared 

against known values. 

For reference, the actual matrix is: 

Year 2000: Trade Volumes, Tonnes, SITC 89, Miscellaneous Manufactures: Actuals 


F 42,646 305,370 81,523 212,456 5,734 8,779 127,529 32,149 816,186 

NL 157,066 229,456 37,627 159,176 7,616 31,419 31,298 13,469 667,127 

D 444,615 245,475 129,615 363,540 17,501 106,381 71,863 24,982 1,403,972 

IT 359,208 37,894 193,248 130,637 5,911 14,713 202,331 16,835 960,777 

UK 121,061 56,900 92,344 37,424 120,762 18,461 63,197 9,803 519,952 

IRL 28,327 4,066 14,550 4,876 85,103 1,975 2,411 369 141,677 

DK 19,991 13,039 59,594 4,789 25,413 956 1,060 956 125,798 

EL 3,704 5,669 4,618 5,413 2,569 182 359 31 22,545 

PT 15,300 618 40,202 2,747 7,158 176 1,027 874 68,102 

Total 1,149,272 406,307 939,382 304,014 986,052 158,838 183,114 500,563 98,594 4,726,136 

The first possibility is to fill in the matrix using a purely mechanical Furness algorithm, which 

attempts to find a solution (out of many feasible solutions) without the need for any underlying 

model. It works by seeding the initial matrix with zeroes on the leading diagonal, and ones 

elsewhere, and then successively factoring up the rows and then the columns so that the sums in 

the rows and columns tend toward the known row and column totals. After a few cycles, the 

algorithm “succeeds”. 

A Solution by Furnessing at the 2 Digit Level 


F 0 88,519 264,571 70,492 211,403 30,995 35,631 95,597 18,978 816,186 

NL 180,483 0 176,938 47,143 141,380 20,729 23,829 63,933 12,692 667,127 

D 461,227 151,284 0 120,475 361,300 52,972 60,896 163,382 32,435 1,403,972 

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IT 255,312 83,743 250,297 0 199,998 29,323 33,709 90,440 17,954 960,777 

UK 160,429 52,621 157,278 41,905 0 18,425 21,182 56,829 11,282 519,952 

IRL 36,239 11,887 35,527 9,466 28,388 0 4,785 12,837 2,548 141,677 

DK 32,320 10,601 31,685 8,442 25,317 3,712 0 11,449 2,273 125,798 

EL 6,143 2,015 6,022 1,605 4,812 706 811 0 432 22,545 

PT 17,223 5,649 16,885 4,499 13,492 1,978 2,274 6,101 0 68,102 

Total 1,149,376 406,319 939,204 304,026 986,091 158,840 183,116 500,569 98,595 4,726,136 

The first improvement considered was to carry out the same process, but with further 

disaggregation by commodity. The data was therefore disaggregated into nine 3 digit SITC 

groups. As the algorithm is so straightforward, there is very little extra overhead involved in 

this step. The result is: 

A Solution by Furnessing at the 3 Digit Level 


F 0 84,855 263,541 70,517 213,886 28,964 36,600 98,574 19,248 816,186 

NL 180,686 0 182,292 47,370 141,986 20,704 23,693 57,030 13,368 667,127 

D 481,226 161,631 0 118,699 378,145 56,627 61,486 114,592 31,567 1,403,972 

IT 233,890 77,001 232,976 0 184,096 27,567 31,779 155,660 17,807 960,777 

UK 164,494 52,001 164,700 41,359 0 18,234 22,102 45,829 11,233 519,952 

IRL 35,235 11,999 39,500 9,837 26,004 0 4,565 11,866 2,670 141,677 

DK 32,734 11,545 34,218 8,227 25,339 4,135 0 7,337 2,263 125,798 

EL 5,595 1,654 5,283 3,057 5,165 594 754 0 444 22,545 

PT 15,647 5,666 16,422 4,955 11,554 2,022 2,146 9,690 0 68,102 

Total 1,149,507 406,353 938,932 304,021 986,175 158,848 183,123 500,577 98,599 4,726,136 

The result is not dissimilar, but the overall error is lower, and it is now possible to generate 

tables at a higher level of detail, which may be advantageous for later stages of ETIS. For 

example: 

A Solution by Furnessing for SITC 891, Arms and Ammunition 


F 0 18 396 2,052 1,285 25 131 235 138 4,280 

NL 290 0 165 855 535 10 55 98 58 2,065 

D 362 9 0 1,067 668 13 68 122 72 2,381 

IT 2,193 56 1,248 0 4,049 78 413 740 436 9,214 

UK 326 8 185 961 0 12 61 110 65 1,729 

IRL 0 0 0 0 0 0 0 0 0 1 

DK 57 1 32 168 105 2 0 19 11 396 

EL 638 16 363 1,882 1,179 23 120 0 127 4,349 

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PT 8 0 4 22 14 0 1 3 0 53 

Total 3,873 109 2,394 7,007 7,836 163 851 1,327 908 24,468 

By calculating the cell errors, and simply shading the cells involving neighbouring countries, it 

is fairly obvious that the errors are strongly correlated with distance, i.e. this method tends to 

underestimate flows where the distance is small. It would follow that if the matrix is seeded 

with initial values that reflect distance, the estimation accuracy should improve. 

Analysis of Errors: Actual vs Estimated (based on 3 Digit Furnessing) 

F NL D IT UK IRL DK EL PT 

F 0 42,209 41,829 11,006 1,430 23,230 27,821 28,955 12,901 

NL 23,620 0 47,164 9,743 17,190 13,088 7,726 25,732 101 

D 36,611 83,844 0 10,916 14,605 39,126 44,895 42,729 6,585 

IT 125,318 39,107 39,728 0 53,459 21,656 17,066 46,671 972 

UK 43,433 4,899 72,356 3,935 0 102,528 3,641 17,368 1,430 

IRL 6,908 7,933 24,950 4,961 59,099 0 2,590 9,455 2,301 

DK 12,743 1,494 25,376 3,438 74 3,179 0 6,277 1,307 

EL 1,891 4,015 665 2,356 2,596 412 395 0 413 

PT 347 5,048 23,780 2,208 4,396 1,846 1,119 8,816 0 

An allowance for distance has been introduced by using the gravity model described above. 

The amoeba algorithm was used to calculate ‘n’ and ‘m’ parameters, and then the gravity 

function was used to seed the matrix. 

A Solution by Furnessing a Seeded Matrix at the 2 Digit Level 


F 0 63,087 257,750 87,545 263,764 25,400 26,260 75,374 17,006 816,186 

NL 131,527 0 240,474 30,627 171,076 16,474 17,032 48,887 11,030 667,127 

D 469,180 209,961 0 130,518 294,419 47,174 81,149 139,989 31,584 1,403,972 

IT 294,021 49,339 240,812 0 150,721 29,562 30,563 145,966 19,793 960,777 

UK 190,362 59,222 116,732 32,388 0 31,846 19,788 56,798 12,815 519,952 

IRL 24,128 10,026 22,094 6,282 60,295 0 4,173 11,978 2,703 141,677 

DK 18,144 9,218 44,234 6,784 27,205 4,359 0 12,935 2,918 125,798 

EL 4,646 1,445 4,740 3,847 4,850 1,116 1,154 0 747 22,545 

PT 17,409 4,069 12,369 6,033 13,656 2,912 3,011 8,643 0 68,102 

Total 1,149,417 406,366 939,204 304,023 985,986 158,844 183,131 500,570 98,596 4,726,136 

If the errors from this method are examined in the same way, the correlation between the error 

sign and the distance can be seen to be lower. 

Analysis of Errors (Tonnes): Actual vs Seeded Result 

F NL D IT UK IRL DK EL PT 

F 0 20,441 47,620 6,022 51,308 19,666 17,481 52,155 15,143 

NL 25,539 0 11,018 7,000 11,900 8,858 14,387 17,589 2,439 

D 24,565 35,514 0 903 69,121 29,673 25,232 68,126 6,602 

IT 65,187 11,445 47,564 0 20,084 23,651 15,850 56,365 2,958 

UK 69,301 2,322 24,388 5,036 0 88,916 1,327 6,399 3,012 

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IRL 4,199 5,960 7,544 1,406 24,808 0 2,198 9,567 2,334 

DK 1,847 3,821 15,360 1,995 1,792 3,403 0 11,875 1,962 

EL 942 4,224 122 1,566 2,281 934 795 0 716 

PT 2,109 3,451 27,833 3,286 6,498 2,736 1,984 7,769 0 

The total error is lower using this method, and although the correlation with distance is still 

present, it is also lower. 

The conclusion is therefore that a combination of these techniques should be used. The data 

should be disaggregated by commodity, and by region where possible. The amoeba algorithm 

should be used to establish parameters for the gravity model, using known flows. These 

parameters should then be used to seed the region to region matrices, and the Furness algorithm 

to build up the O/D matrices. 

Method 4: Inclusion of domestic transport to the matrix 

In the fourth method domestic transport is included in the O/D matrix. Data about domestic 

transport flows is collected from national statistical offices and Eurostat. For a number of 

countries regiontoregion by commodity type by transport mode information is available. For 

other countries only more aggregate data is available. In the latter case, data sources are 

combined and procedures are applied in order to estimate the domestic transport flows on the 

required level of detail. 

Once the domestic transport data is available, it can be added to the O/D matrix. However, if all 

domestic transport is added to the O/D matrix double countings are introduced because export 

and import flows by sea with hinterland transport in the origin/destination country already 

include domestic transport. 

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In table 6.5 this is illustrated by an example. 

Table 6.5 

Origin 

region 

Mode 

origin 

Illustration of double countings in domestic transport 

Transh. 

region 

Madrid (ES) road Pais Vasco 

(ES) 

Mode Transh. region Mode 

destination 

sea 

SouthWest 

(UK) 

rail 

Destination 

region 

West Midlands 

(UK) 

Madrid (ES) road Pais Vasco 

(ES) 

SouthWest 

(UK) 

rail 

West Midlands 

(UK) 

Volume 

(tonnes) 

Flow type 

2000 International 

30000 Domestic 

70000 Domestic 

This table shows an international transport flow from Spain to the UK by sea with hinterland 

transport in Spain and in the UK. The hinterland transport (road transport from origin region to 

the port region in Spain, rail transport from the port region to the destination region in the UK) 

that is part of an international transport chain is also registered in domestic transport. This 

means that if all domestic transport would be included in the O/D matrix, the hinterland flows 

would be included twice; once as part of an international transport chain and once as domestic 

transport. In order to include the pure domestic transport on the O/D matrix the domestic 

transport has to be reduced by the hinterland flows (specified by origin region, by destination 

region and by commodity group). In the example given above, the volume of 30.000 tonnes 

domestic road transport between Madrid and Pais Vasco and the volume of 70.000 tonnes 

domestic rail transport between region SouthWest and region West Midlands should be 

reduced by 2.000 tonnes. 

Method 5: Estimation of cargo characteristics and transport unit information 

Trade data is highly detailed with respect to commodity type, but this is not necessarily a useful 

way of segmenting demand within the freight sector. Instead it is necessary to segment traffic 

flows according to their handling characteristics. For example, temperature controlled goods are 

limited to certain transport modes, e.g. driver accompanied refrigerated trailer, refrigerated 

container, and to certain transport services, e.g. container ships with plugin points. 

The challenge is that these characteristics have to be inferred from the commodity description, 

and cannot necessarily be drawn directly from trade data. The COMEXT database is published 

using the international 8 digit combined nomenclature (CN8) system. Because this database 

contains very detailed commodity information, the cargo characteristics are added to the 

database as one of the first steps where this detail is still available, afterwards when the data is 

aggregated to the NSTR 2 digit commodity level the cargo characteristics information stays 

available and accessible. 

The handling characteristics chosen for the testing phase of ETIS are: 

· Cargo Type/Mode of Appearance: Liquid Bulk, Dry Bulk, General Cargo, and Containers 

· Hazardous/Non Hazardous: e.g. flammable or toxic cargo 

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· Temperature Controlled/Ambient: e.g. fruit 

· Containerised/Noncontainerised 

· Transport Units: number of vehicles 

The main decision relates to the degree of commodity aggregation at which to make the 

estimation. If an aggregate approach is taken, the calculation procedure is relatively simple, and 

the conversion factors can be ‘handcrafted’. However, if a disaggregate approach is taken, the 

conversion rules are far less variable from corridor to corridor, and across time periods. 

This can be shown in relation to a simplified case, where an aggregated product group ‘Metals’ 

can be disaggregated into three types, each with different handling characteristics as far as rates 

of containerisation are concerned. 

Corridor 1 

Container Non Container. Container % Non Container % 

Metals 1500 1500 50% 50% 

Metals: Type 1 500 500 50% 50% 

Metals: Type 2 1000 0 100% 0% 

Metals: Type 3 0 1000 0% 100% 

Corridor 2 

Container Non Container. Container % Non Container % 

Metals 1600 2000 44% 56% 

Metals: Type 1 1500 1500 50% 50% 

Metals: Type 2 100 0 100% 0% 

Metals: Type 3 0 500 0% 100% 

By varying the product mix across the two corridors, the aggregate containerisation factors 

change even though the disaggregate factors do not. We can offer the hypothesis that handling 

factors depend upon: 

· The product type 

· The heterogeneity of the product type 

· The corridor, and 

· the balance of trade across the corridor 

The strength of these factors will vary, depending upon the factor considered. Of these, the key 

element which can be controlled is the degree of heterogeneity within the product category. By 

using the diasggregated form of the COMEXT database (using approximately 10,000 CN8 

headings) it is plausible to assume that for certain factors (e.g. hazardous goods, chilled goods) 

codes can be simply labelled as ‘X’ or ‘not X’ rather than, say ‘15% X’ or ‘85% not X’, where 

X is any given factor. This avoids having to adjust the factors depending on the underlying mix. 

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The corridor element is harder to control without specific data about individual country pairs. 

Of particular relevance is the extent of containerisation. The fact that for any given corridor, the 

balance of container flows may be different to the balance of cargo flows, implies that rates of 

containerisation will vary by direction. Furthermore, the absolute size of the flow is also 

important. Small flows of conventional cargoes may well be containerised, but there will be 

thresholds beyond which dedicated bulk transport services will be used. 

The approach that has been tested involves using the disaggregated (CN8) COMEXT data and 

applying lookup tables developed by MDSTransmodal. 

Commodity Conversion Table 

Commodity Category Handling Mode of Stowage Containerisation 

CN8 NSTR SITC Description Haz Chill Appearance Per 

TEU 

4,031,099 139 2231 Yoghurt 0 1 General Cargo 17 22 100 100 95 100 

8,051,009 31 5711 Oranges 0 1 Semi Bulk 10 17 100 100 30 30 

15,191,100 182 43131 Fatty Acids 0 0 Liquid Bulk 12 22 90 90 0 90 

27,071,010 831 33522 Benzole 1 0 Dry Bulk 18 22 0 0 0 0 

27,090,000 310 33300 Petroleum Oils 1 0 Crude Oil 18 22 0 0 0 0 

44,103,200 976 63422 Particle Board 0 0 Semi Bulk 10 20 80 80 15 15 

85,281,099 931 76110 Colour TVs 0 0 General Cargo 6 11 100 100 100 100 

87,033,219 910 78120 Motor Vehicles 0 0 Vehicles 4 8 15 15 15 15 

Per 

FEU 

Intra 

Exp 

Intra 

Imp 

Extra 

Exp 

Extra 

Imp 

The table shows the key data fields for a diverse range of commodities. The trade data is linked 

through the index field (CN8) containing the eight digit product code, the maximum degree of 

product detail possible within multicountry trade data. Conversions can be made to NSTR and 

SITC coding systems, and the description at the five digit SITC level is also shown. The 

handling factors for hazardous cargo (HAZ) and temperature controlled (CHILL) is a simple 

boolean variable. Then the stowage factors are shown, giving conversion ratios for tonnes into 

unit loads: per 20 foot unit (TEU) and per 40 foot unit (FEU). From these factors it is possible 

to convert to lorry loads. Note that 1 FEU does not necessarily equal 2 TEUs, since many 

commodities ‘weighout’ before they ‘cubeout’, i.e. that they reach the maximum permitted 

weight for road haulage before they are full. 

Finally, the rates of containerisation are given. These are based partially on trade data records, 

but they have been simplified. Different rates are apparent for the simplified corridors: intra 

and extra EU, and import and export. Intra EU imports and exports are not differentiated 

because it would lead to ambiguities, but extra and intra flows will be different, and extra 

imports will be different from extra exports. 

Applying these containerisation factors will lead to an indicator of trade which needs to be 

defined carefully. Note that they will not show the total number of TEUs or container units 

handled in Europe, as they do not take into account transshipment or empty returns. It will 

show the estimated volume of containerised trade expressed as tonnes or TEU for a given 

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European country. This is therefore an indirect measure of port traffic, but a direct measure of 

the underlying demand for transport services. 

The translation from trade data to the required quantities has been carried out within the ETIS 

pilot using a simple Microsoft Access SQL query. Access has also been used to aggregate 

across the NST commodity definition. 

Contrary to international trade, for domestic transport less detail is available concerning the 

commodity type (at most information is available on NSTR2 digit level instead of the CN 8 

digit level). As a consequence the cargo characteristics and information about the number of 

vehicles have to be determined in a different way. Available information from Eurostat and 

national statistical offices is collected as much as possible. This data is used to estimate average 

figures that are used to estimate cargo characteristics and transport unit information for 

domestic transport. 

Method 6: Estimation of transport performance information 

Transport volume information, expressed in weight of the goods (tonnes) and expressed in value 

of the goods (euro), is included in the data right from the first step in the method. For transport 

performance information the variable number of TEUs is included from the first step of the 

method while information about the number of vehicles is included in the data in method 5. 

Transport performance information expressed in tonnekilometres, vehiclekilometres and TEUkilometres 

still has to be calculated. 

In order to be able to calculate these variables information is needed about the transport distance 

between regions (on NUTS2 level) by transport mode. Currently this data is available form 

transport networks available at NEA (from NEAC) that have also been used in the TENSTAC 

project. After completion of ETIS WP5 ‘European transport network data’ this data will be 

available from WP5. 

For this calculation it is not only necessary to have distance information by mode between the 

origin and destination region, but also between transhipment locations and between 

origin/destination regions and transhipment locations. A special procedure will be applied for 

the intraregional transport distances. For intraregional transport no distance can be determined 

based on a transport network since the origin and the destination are the same. A procedure will 

be applied in which the distance for intraregional transport will be determined by region based 

on a number of characteristics of the region (such as the area of a region and/or economic 

activity of a region). 

Once the transport distance data is available in the requested format, the transport performance 

information is calculated by multiplying the distance by the relevant variables. 

Validation and modification of results – use of CAFT and national sources 

Finally, when the final freight O/D matrix has been constructed, the results will be validated by 

comparing the results with CAFT data and with data from national sources (regional 

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distribution, loading factor, containerisation rate, etc.). In case this comparison shows large 

differences, the results will be modified in such a way that they match the data used for the 

validation. 

6.3 Lessons learned and changes compared to the original planned methodology 

In general the methodology has been applied according to plan. Some minor changes have been 

made that are listed below. 

Intermodal transport 

For intermodal transport not enough information is available to include it in the freight O/D 

matrix. Because estimation methods are too time consuming to apply it has been decided to 

leave intermodal transport out of the database. 

Different method for the accession countries 

For the accession countries a different method will be applied. Available data from – amongst 

others – the Intermoda project, NEAC and TENSTAC – will be used for these countries. Once 

the accession countries have become member of the EU and an update of the ETIS reference 

database will be made in the future, the same methodology can be applied for the accession 

countries as it is now applied for the current EU countries. 

Maritime transport data 

It appears that more maritime transport data is available than expected at first hand. From 

national statistics data is collected about maritime import and export flows by unloading/loading 

port in the country. Besides, Eurostat collects detailed porttoport data. 

Continuation 

For the construction of a complete freight O/D matrix that will be part of the ETIS reference 

database, the tested methods will be applied. 

6.4 Output of the testing phase 

The output of the testing phase consists of a freight O/D transport chain database and a transport 

database. The transport chain database (origin and destination of the goods) describes trade 

flows containing the following variables: 

· Origin region or country (NUTS2 or ETIS country classification) 

· First transhipment location (NUTS2 classification) 

· Second transhipment location (NUTS2 classification) 

· Destination region or country (NUTS2 or ETIS country classification) 


· Mode between transhipment locations 

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· Indicator hazardous goods 

· Indicator conditioned goods 

· Manifestation of the goods 



· Volume of unitised goods (in tonnes) 

· Number of TEUs 




The transport database describes transport flows (origin and destination of the transport unit) 

containing the following variables: 



· Transport mode 


· Indicator hazardous goods 

· Indicator conditioned goods 

· Manifestation of the goods 

· Transport distance 



· Tonnekilometres 

· Number of transport units 

· Vehicle/vesselkilometres 

· Volume of unitised goods (in tonnes) 

· Number of TEUs 

· TEUkilometres 




6.5 Interpretation of the output data for the users 

As becomes clear from the section above, the results include very detailed data that are partly 

estimated. The more detailed the (estimated) data, the lower the reliability of the data. On the 

lowest level of detail, the results are very detailed. Consider for instance trade consisting of 

three transport legs: from region Madrid in Spain by road to port region Pais Vasco in Spain, 

then by sea to region ZuidHolland in the Netherlands, and finally by inland waterways to 

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region Ruhrgebiet in Germany. The goods are characterised by commodity group and 

characteristics such as manifestation, conditioned, hazardous and/or unitised. The volume of the 

goods is expressed in tonnes, value, tonnekilometres, number of vehicles/vessels, 

vehicle/vesselkilometres, number of TEUs and TEUkilometres. Because part of the 

information is estimated, on this level of detail the results should be interpreted in a proper way 

by transport experts. As the data is aggregated to a less detailed level, the results become more 

reliable and can be easier understand and interpreted. 

Detailed data can only be interpreted in the proper way by transport experts, in order to avoid 

problems with the interpretation of the results common users should only get access to 

aggregated OD data. 

The most detailed OD data can only be used by experts who are trained on the specific 

characteristics of the database. Developing a training program after the project might be an 

option. More aggregate submatrices will be developed for the final product that are easier to 

understand and interpreted by a broader audience. 

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7 OBSERVATIONS UP TO PHASE 2 


This chapter describes the observations up to phase 2 of ETIS reference database on legal 

aspects and organisational aspects. These descriptions will be extended along the course of the 

project based on experiences. 

7.2 Legal aspects and organisational aspects 

EUROSTAT has provided data to ETIS free of charge for use within the project. No 

arrangements have been made yet on future use of the data by third parties outside the 

Commission. For now the data have to be considered restricted. 

Some data available at EUROSTAT (for instance international interregional road transport data) 

cannot be provided due to the statistical law or because of confidentiality reasons. For other data 

available at EUROSTAT (for instance the porttoport data) a license has to be signed in which 

it is expressed that the data will only be used in this project. 

The conditions of data delivery from national statistical offices also differ from country to 

country. For some countries the data can be downloaded from the internet without any 

restrictions (for instance trade data of Spain), the only restriction is that the statistical office 

should be mentioned as source (for instance transport data in the Netherlands), a price has to be 

paid and a contract has to be signed for restricted use (for instance transport data in Germany) or 

in addition an official letter from the European Commission is required (for instance transport 

data France). 

As has also been discussed at the second open conference of ETIS it is recommended that a 

discussion is opened on how this data collected can be streamlined in the future as a follow up 

of the current ETIS projects. Should this be done through EUROSTAT or is a less formalised 

approach more appropriate. In any case it should be attempted to come to long lasting 

agreements on data delivery to avoid time consuming data collection procedures. 

7.3 Other remarks 

It has been observed in chapter 6 that the resulting OD matrix is very detailed which requires 

caution in its use. In case the most detailed level is used the reliability of the figures is in some 

cases relatively low due to the fact that some elements have been estimated. The user therefore 

has to be guided. One option is to provide only submatrices that do not contain all the variables 

in one matrix, to be used by the common users. The complete matrix can be used by expert 

users only which need to be trained to make the proper selections and interpretations. 

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ANNEX A: LIST OF INDICATORS CONSIDERED 

IN WP 3



Mode Spatial scope Forecast Details to be provided 

Type of data 

required 

Data availability 

ref. 

Definition 

Measurement 

unit 

Road 

Rail 

Air 

Inland w. 

Sea 

Intermodal 

Network 

OD 

Zone 

EU 

AC 

1.1.4 

Breakdown of journeys by 

origin/destination, proportion 

of long distance traffic using 

the TEN 

WP:3,4,5 

Number of 

percentage of 

total demand 

x TEN Y 

OD Matrices at the 

national level, 

roadside interviews, 

licence plate camera 

information (foreign 

plates). 

* * 

1.5.1 

2.1.2 

Road freight volumes on 

TEN by cargo type 

WP: 3,5 

Traffic volumes on the Trans 

European rail network, by 

type (passenger / freight), 

including non fulfilled 

demand 

Freight vehiclekm 

Tonne km 

Number of trucks 

Number of trainkm 

by train type 

Passengerkm or 

tonnekm 

x TEN Y 

x TEN Y 

Journey type 

(domestic/international) 

Annual, weekly, daily, 

peaks 

Liquids, dry, bulk, 

general cargo, containers 

Hazardous, nonhazardous 

National/international 

journey 

Train type (high 

speed,…) 

Traffic surveys * * 

Rail traffic and 

passenger surveys 

Statistics on 

unfulfilled demand 

** ** 

WP: 3,4,5 

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D5 Annex WP 3: DATABASE METHODOLOGY AND DATABASE USER MANUAL – FREIGHT TRANSPORT DEMAND 


Type of data 

required 


ref. 

2.5.1 

Definition 

Freight volumes on TEN by 

train type 


Measurement 

unit 

Freight trainkm 

Tonne km 

(cross section 

volumes) 

Road 

Rail 

Air 

Inland w. 

Sea 

Intermodal 

Network 

OD 

Zone 

x TEN Y 

Journey type 

(domestic/international) 

Train type (unitised, 

dedicated bulk, mixed 

bulk) 

Cargo type (liquids, dry, 

bulk, general cargo, 

containers) 

Risk level (hazardous, 

nonhazardous) 

EU 

AC 

Rail freight statistics ** ** 

4.1.2 

Freight volumes on the inland 

waterway network 


Tonnes, TEU 

Tonnekm, TEUkm 

x TEN Y 

Cargo type (Liquids, dry, 

bulk, general cargo, 

containers) 

Hazardous, nonhazardous 

Freight demand 

surveys 

* * 

5.1.1 

Port throughput (passengers, 

freight) 


Number of units 

(tonnes, 

passengers, and 

vehicles), using 

an AADT type 

calculation 

Number of ship 

departures 

(AADT type 

calculation), by 

x 

x 

By port 

Country 

Y 

National, European, 

Intercontinental journey. 

Freight, Passengers, 

vehicle types 

Port statistics by 

categories listed above 

** * 

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Type of data 

required 


ref. 

Definition 

Measurement 

unit 

ship type 

Number of 

containers (TEU) 

Peak period 

measure 

Road 

Rail 

Air 

Inland w. 

Sea 

Intermodal 

Network 

OD 

Zone 

EU 

AC 

4.1.3 

I/C factor of bridges and locks 

(intensity versus capacity) 

(waiting time) 


If I/C factor is 

above 0,5 / 0,6 

waiting time 

grows to 

unacceptable 

figures 

x x Y 

European 

classification system 

Design documentation, 

infrastructure 

characteristics 

** ? 

6.1.1 

Traffic volumes served at the 

terminal 


Passengers 

Tonnes 

TEU 

x TEN Y 

Terminal type (road/rail, 

seaport/rail,….) 

Cargo type 

Terminal statistics * * 

1.3.1 

Estimated / measured energy 

consumption on roads 


toe per vehiclekm 

x 

By 

road 

section 

Country 

Y 

Traffic volumes, 

estimated energy 

consumption data 

*** 

(country) 

* (by road 

section) 

** 

(country) 

* (by road 

section) 

2.3.1 

Estimated/measured energy 

consumption on railways 


toe ( tonnes oil 

equivalent) per 

trainkm 

x 

By rail 

section 

Country 

Y 

Number of trainkm 

Estimated energy 

consumption data 

*** ** 

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Type of data 

required 


ref. 

4.2.1 

1.3.2 

2.3.2 

4.2.2 

Definition 

Estimated/Measured Energy 

Consumption on waterways 


Estimated /measured road 

transport emissions 

+corresponding marginal unit 

cost 


Estimated /measured rail 

transport emissions 


cost 

WP: 3,4,5,8 

Estimated /measured Inland 

waterway transport emissions 


cost 


Measurement 

unit 

toe (tonnes oil 

equivalent) per 

tonnekm 

Tonnes of 

pollutant per 

million vehiclekm 

(CO2, NOx, 

VOC, SOx) 

Tonnes of 

pollutant per 

million trainkm 

(CO2, NOx, 

VOC, SOx) 

Tonnes of 

pollutant per unit 

of traffic volume 

(million tonnekm) 

Road 

x 

Rail 

x 

Air 

Inland w. 

x 

x 

Sea 

Intermodal 

Network 

Main 

corrido 

rs 

By 

road 

section 

By rail 

section 

By 

waterw 

ay 

OD 

Zone 

Country 

Country 

Country 

Y 

Y 

Y 

Y 

Traffic volumes and 

energy consumption 

data 

Traffic volumes, 

emission/pollution 

data 



data 

Percentage of 

networkkm electrified 

Percentage of trainkm 

electrically hauled 



data. (differentiation to 

inland waterways 

EU 

AC 

*** ** 

** 

(country) 

* (by road 

section) 

*** ** 

*** ** 

** 

(country) 

* (by road 

section) 

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Type of data 

required 


ref. 

1.3.3 

Definition 

Noise levels generated by 

road transport+corresponding 

marginal unit cost 


Measurement 

unit 

Exposure to over 

a specified noise 

level in dB 

Road 

x 

Rail 

Air 

Inland w. 

Sea 

Intermodal 

Network 

By 

road 

section 

OD 

Zone 

Y 

Traffic volumes, noise 

level data 

EU AC 

* (*) 

2.3.3 

Noise levels generated by rail 

transport +corresponding 

marginal unit cost 


Exposure to over 

a specified noise 

level (L dn dB) 

x 

By rail 

section 

Y 

Traffic volumes, noise 

level data 

* (*) 

3.2.2 

Amount of investment made 

in the development and 

maintenance of air traffic 

control 

Million Euros 

x 

by 

airport 

Y 

National infrastructure 

programmes 

* * 


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ANNEX B: INDICATOR COMPILATION 

TEMPLATES



Indicator Compilation Template No. 1 

Ref. 1.1.4 

Definition 

ETIS Glossary 

Breakdown of journeys by origin/destination (1.1.4a), proportion of long distance traffic using the TEN (1.1.4b) 

Journey: A movement of vehicle or vessel from a specified point of origin to a specified point of destination. 

Computation Method (Formula) 

1.1.4a 

T(i,j) 

1.1.4b 

V 

l 

( n ) 

R ( n ) = or 

V ( n ) 

R = 

å 

i , j Î L 

å 

i , j Î EU 

T ( i , j ) 

T ( i , j ) 

Variable definition 

Variable computation method 

Directly from a database or agreed 

classifications/nomenclatures 

Output of the model 

Method variable V1 

T(i,j) – total number of journeys 

between origin i and destination j 

Trip generation model (OD matrix for 

passenger and freight movements) 

Method variable V2 i,j – NUTS2 zones NUTS2 zones database for Europe 



n specific road link of TEN 

network 

R(n) – proportion of longdistance 

vehicles on the road link (n) of the 

TEN network 

GETIS networks database for Europe 

Trip generation and assignment model 

(passenger and freight) 

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V(n) – number of vehicles on the 

road link (n) of the TEN network 

Derived from traffic counts on the network 

(AADT) (for parts of the network) 




V l (n) number of long distance 

vehicles on the road link (n) of the 

TEN network 



Method variable V7 l – distance category (long) The distance categories can be agreed 

(for example, long distance journey – exceeding 

300 km) 

Method variable V8 L set of long distance OD relations NUTS2 zone combinations with a distance longer 

than the minimum threshold 


EU – total set of OD relations in the 

EU 

All NUTS2 zone combinations 

Remarks concerning method 

variable computation 

The same trip generation and assignment model can produce a complete variable set necessary to compute the indicator. This 

variable set can be split into variable set for passengers and variable set for freight. 

Model required to compute the 

method variables (listed above) 

Definition 

Trip generation and assignment model (passenger and freight) 

Model (component) analog 

For instance: VACLAV, SCENES, NEAC 

Model component C1 OD freight matrices (annual) annual number of freight vehicles that traveled between each OD 

pair, annual tons transported between each OD pair 

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Model component C2 OD passenger matrices (annual) annual number of passengers and passenger vehicles (cars and 

buses) that traveled between each OD pair 

Model component C3 Distance matrices Distance for each OD pair per mode 

Model component C4 Networks and their attributes GETIS networks 

Model component C5 

NUTS2 zones 

Model component C6 Traffic counts Vehicle counts on links 

Model component C7 Traffic assignment matrices Number of vehicles (long distance) per link per mode 

Model component C8 Survey data household survey, travel agencies survey, bus operators survey, 

roadside interviews 


National and international trade, transport and socialeconomic 

data 

Remarks concerning the models More specific description of the reference models can be find in the material prepared by AJIEurope 

(Tables_Final_Report_version_2000.xls) 

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Ref. 1.5.1 

Definition 



Road freight volumes on TEN by cargo type 

Road freight volumes: In Tonne km A movement of a road vehicle from a specified point of origin to a specified point of 

destination. 

Cargo type to be defined depending on data availability 

P(c) c = 1, 2, ……n 

P(c) = å T ij ( c ) * D ij ( c ) 

i , j 







P(c) road freight volumes of cargo 

type c on TEN (in tonkm) 

OD matrix model of tons divided by 

cargo type + estimated distance matrix 


c – specific cargo type 

(c= 1, 2, …,n) 

Cargo type classification (to be defined) 

Method variable V3 i, j – origin i and destination j 

(NUTS2 zones) 

NUTS2 zones database for Europe 

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Method variable V4 Tij (c) 

Method variable V5 Dij (c) 

total tons of cargo type c 

transported between origin i and 

destination j 

total distance covered by 

cargo type c transported between 

origin i and destination j 

OD matrix model of tons per cargo 

type 

estimated distance matrix 



Definition 




Freight OD matrix model 

For instance SCENES, NEAC 

Model component C1 OD freight matrix Annual total freight (number of tones) transported between OD 

(domestic, international) 


Estimated breakdown of freight into cargo types generated by 

each zone 

Estimates obtained from trade and transport statistics (domestic 

and international), traffic survey 

Model component C3 Estimated breakdown of freight into cargo types Estimates obtained from trade and transport statistics (domestic 

and international), traffic survey 



cargo typebased OD freight matrices 

NUTS2 zones 

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Model component C6 Distance matrices TENbased road distances between NUTS2 zones and internal 

distances per zone 

Model component C7 Traffic survey data Traffic counts by freight vehicle category on TEN links 


National and international trade and transport data 

Remarks concerning the models More specific description of the reference models can be find in the material prepared by AJIEurope 

(Tables_Final_Report_version_2000.xls) 

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Ref. 2.1.2 

Definition 


Traffic volumes on the TransEuropean rail network, by type (passenger 2.1.2a / freight 2.1.2b), including non fulfilled demand 

Rail passenger kilometer: Unit of measure representing the transport of one rail passenger by rail over a distance of one kilometer. 

Tonnekm by rail: Unit of measure of goods transport which represents the transport of one tonne of goods by rail over a distance of 

one kilometer. 


(2.1.2a) 

(2.1.2b) 

P(m) 

P(f) 

P(m) = å N 

i , j 

P(f) = å T 

i , j 

ij 

ij 

* D ij ( m ) 

* D ij ( f ) 






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P(m) annual passenger traffic 

volumes (passengerkm) on the 

TransEuropean rail network 

Trip generation and modal split model 

(aggregated figure of passengerkm on 

TENrail) 

Method variable V2 P(f) annual freight traffic 

volumes (tonkm) on the Trans 

European rail network 


(aggregated figure of tkm on TENrail) 

Method variable V3 m, f – traffic type (passenger/freight) Agreed traffic type categories (passenger/freight) 


i,j – NUTS3 zones (for passenger) 

i,j – NUTS2 zones (for freight) 

NUTS3 database for Europe 

NUTS2 database for Europe 


N ij annual number of passengers 

transported on TransEuropean rail 

between origin zone i and destination 

zone j 


(OD matrices on NUTS3 level) 

Method variable V6 Dij ( m ) 

total average distance 

between zone i and zone j on TEN 

rail (passenger) 

Calculation of average distances between NUTS3 

zones reachable on TENrail (GETISrail) 


T ij annual freight (tons) 

transported between zone i and zone 

j on TENrail 

OD matrices on NUTS2 level 

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Method variable V8 Dij ( f ) 

total average distance 

between zone i and zone j on TEN 

rail (freight) 

Calculation of average distances between NUTS2 

zones reachable on TENrail (GETISrail) 



Special attention to the following aspects: nonfulfilled demand; occupancy in relation to capacity and in line with this empty train 

or wagons. 



Definition 

Trip generation and modal split model (passenger) 

OD matrix model 


For instance VACLAV (passenger) 

For instance NEAC, SCENES (freight) 

Model component C1 OD passenger matrix (annual) Number of passengers carried or passenger trips made between 

(and within) NUTS3 zones on TENrail 

Model component C2 OD freight matrix (annual) Freight carried between OD zones on TENrail 

Model component C3 Distance matrices Average Distances between OD zones calculated for TENrail 

(passenger/freight) 

Model component C4 Rail passenger services matrices Times schedules, routes, travel times of passenger trains serving 

TENrail obtained from operators 


OD matrices showing LOS for passenger transportation by rail 

(total travel time by rail, number of transfers, waiting times etc) 

in relation to other modes (road) 

Rail services traffic allocation over TEN network; 

Generalised costs for rail and other modes (road) per OD pair 

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Model component C6 OD matrices showing transport chains for freight Transport chain information for freight flows between NUTS2 

zones (including transshipment pointszones) 

Model component C7 rail networks and their attributes GETIS networks 



Ticketing information, trade and transport statistics 

NUTS2/NUTS3 zones 

Remarks concerning the models 

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Ref. 2.5.1 

Definition 



Freight volumes on TEN by train type 

Train type: Goods train: Train for the carriage of goods composed of one or more wagons and, possibly, vans moving either empty 

or under load. 

F(t) = P ( t , r , c ) / L ( t , r , c ) 

( 2.5.1a) 

P(t, c, r) 

= å T ij ( t , c , r ) * D ij ( t , c , r ) 

i , j 

(2.5.1b) 







F(t) – Freight trainkm traveled 

annually by train type t on TENrail 

Freight train service schedules and routes 

database; GETISrail network 

Method variable V2 t – train type Agreed train types: unitized, dedicated bulk, 

mixed bulk 

Method variable V3 i,j – NUTS2 zones NUTS2 zones database for Europe 

Method variable V4 L(t,r,c) Loading per train in (tonnes per train) 

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Dij (t) Average distance of trains 

of train type t between zone I and 

zone j on TENrail 

P(t,c,r) – freight volumes on TEN by 

train type t, cargo type c and risk 

level r 

Freight train served routes database; GETISrail 

network 

OD matrices model on NUTS2 level 

Method variable V7 c – cargo type Agreed cargo types: liquid, dry, bulk, general 

cargo, (container possibly separate variable 

separate ) 

Method variable V8 r – risk level Agreed risk levels : hazardous, nonhazardous 

Method variable V9 T ij ( t , c , r ) total annual tons of 

cargo type c with risk level r 

transported by train type t on TENrail 

between zone I and zone y. 


D ij ( t , c , r ) average distance 

between zone I and zone j on TENrail 

accessible for trains of train type 

t carrying cargo of cargo type c of 

risk level r. 

Freight train routes database; GETIS network (rail 

+ terminals) 

OD matrix model on NUTS2 level 



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Definition 




OD matrix model 

For instance NEAC or SCENES 

Model component C1 OD freight matrix (annual) Freight carried between OD zones on TENrail 

Model component C2 Distance matrices Average Distances between OD zones calculated for TENrail 

per train type (freight). 

[P.S. It is possible that different train types can be directed to take different 

routes (i.d. resulting in different distances between zone i and zone j)]. 

Average distances between OD zones reachable by other modes 

(competing) 

Model component C3 Rail freight train services matrices Times schedules, routes, travel times of freight trains on TENrail 

obtained from operators 

Model component C4 Modal split matrices Generalised costs for rail and other modes per OD pair 

Model component C5 OD matrices showing transport chains for freight Transport chain information for freight flows between NUTS2 

zones (including transshipment pointszones) 


rail networks and their attributes, including the information 

whether the rail link is allowed for transportation of hazardeous 

goods (i.e, nuclear waste) 

GETIS networks (rail) 

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Model component C7 Terminals networks GETIS networks (terminals) 



trade and transport statistics 

NUTS2 zones 


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Ref. 4.1.2 

Definition 


Freight volumes on the inland waterway network 

Freight volume. Unit of measure representing the movement of one tonne available in an IWT freight vessel when performing the services for which it is 

primarily intended over one kilometer. 


P(f) 

= 

å 

i , j 

T ij * D ij 







P(f) – annual freight volumes (tonkm) on the 

TransEuropean inland waterways network 

aggregated figure of tkm on TENinland 

waterways as computed in the method 

Method variable V2 i,j – NUTS2 zones having TENiww NUTS2 zones database for Europe 


T ij 

annual freight (tons) transported 

between zone i and zone j on TENiww 

OD matrices and assignment model on NUTS2 

level 


D ij total average distance between zone i 

and zone j on TEN iww 

GETIS networks (iww) 

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Remarks concerning method variable 

computation 

Model required to compute the method 

variables (listed above) 

Definition 

OD matrix and assignment model (freight) 


for instance, SCENES, NEAC, VACLAV 

Model component C1 OD freight matrices (annual) Freight carried between OD zones on TENiww 

Model component C2 Distance matrices Average distances between OD zones reachable by TENiww and other 

competing modes 

Model component C3 Speed matrices Average speeds per competing mode between relevant NUTS2 zones 

Model component C4 Generalised costs matrices Generalized costs per competing mode for each OD 

Model component C5 OD matrices showing transport chains for freight within Europe Transport chain information for freight flows between all NUTS2 zones pairs 

Model component C6 iww networks GETIS networks 



NUTS2 zones 

Trade and transport statistics 


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Ref. 5.1.1 

Definition 


Computation 

(Formula) 

Method 

Port throughput (passengers, freight) 

Port throughput (passengers, freight): Amount of the flow of traffic through a port (passengers, gross weight of goods: this includes the 

tonnage of goods carried, including packaging but excluding the tare weight of transport units. ) at a given time 

(5.1.1a ) N e = å 

Ne ( p ) 

p 

N d = å 

p 

Nd ( p ) 

(5.1.1b) 

WL 

WU 

= 

å 

p 

= å 

p 

W L ( p ) 

W U ( p ) 







N e annual number of passengers 

embarked at ports 

Aggregated port statistics 

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N d – annual number of passengers 

disembarked at ports 


Method variable V3 p – specific port List of TENports 



N e (p)– annual number of passengers 

embarked at port p 

N d (p)– annual number of passengers 

disembarked at port p 

Port statistics 

Port statistics 



W L – gross weight of goods loaded 

at ports including packaging but 

excluding the tare weight of transport 

units (annual) 

W U – gross weight of goods 

unloaded including packaging but 

excluding the tare weight of transport 

units (annual) 




W L (p) – gross weight of goods 

loaded at port p including packaging 

but excluding the tare weight of 

transport units (annual) 

Derived from port statistics 

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W U (p) gross weight of goods 

unloaded at port p including 

packaging but excluding the tare 

weight of transport units (annual) 

Derived from port statistics 

Remarks concerning 

method variable 

computation 

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Ref. 4.1.3 

Definition 



I/C factor of bridges and locks (intensity versus capacity) (waiting time) 

I/C factor: relationship between intensity (actual traffic of ships) and capacity (theoretical number of ships per week) 

S(k)=I(k)/C(k) k= 1,…,n 

W L (k) 

W b (k) 

k=1,…,n 

k=1,…,n 









S(k)Service level of bridge or lock k 

I(k) Intensity in terms of number of 

ships per week at bridge or lock k 

C(k) Capacity in terms of theoretical 

number of ships per week of bridge 

or lock k 

Assignment model, OD data model 

Inland navigation capacity model 

Method variable V4 W L (k) waiting time at lock k Inland navigation waiting time model 

Method variable V5 W b (k) waiting time at bridge k Inland navigation waiting time model 

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Definition 

Inland navigation waiting time model 

Assignment model, OD model 

Inland navigation capacity model 

for instance, SCENES, NEAC 


Model component C1 OD freight matrix (annual) by vessel type Freight carried between OD zones on TENiww 


OD matrices showing transport chains for freight between 

Europe 

Transport chain information for freight flows between NUTS2 

zones pairs in the catchment area 

Model component C3 Commercial speed matrices Average commercial speeds per competing mode between 

relevant NUTS2 zones 

Model component C4 iww networks (including ports, locks and bridges) GETIS networks with relevant attributes 

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NUTS2 zones 

Model component C6 Inventory of locks and bridges including technical 

specifications: depth and width at entrance and exit, entrance 

time, operation time, number of locks, length, opening times. 





Traffic survey data 

Port statistics, trade and transport statistics 

I/C factor by lock and bridge 

OD showing empty vessel movements 


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Ref. 6.1.1 

Definition 



Traffic volumes served at the terminal 

Traffic volume: Number of passengers (embarked/disembarked) and freight (tonne) (loaded/unloaded) 

(6.1.1a ) N = å ( N 

e 

( p ) + N 

d 

( p ) - N 

t 

( p )) 

p 

Ne = å 

p 

N d = å 

p 

N t 

= å 

p 

Ne 

( p ) 

N t 

Nd ( p ) 

( p ) 

(6.1.1b) W(p) = W ( p ) + W ( p ) W ( p ) 

L U 

- 

W = W 

L 

+ W 

U 

- W 

t 

WL = å W L ( p ) 

p 

WU = å W U ( p ) 

p 

W t 

= å W 

t 

( p ) 

p 

t 

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N annual number of passengers at 

terminals 

N d – annual number of passengers 

disembarked at terminals 

N e annual number of passengers 

embarked at terminals 

N t – annual number of passengers 

transiting at terminals 

Aggregated terminal statistics 




Method variable V5 p – specific terminal List of TEN terminal 





N (p) annual number of passengers 

at terminal p 

N d (p)– annual number of passengers 

disembarked at terminal p 

N e (p) annual number of 

passengers embarked at terminal p 

N t (p) – annual number of passengers 

transiting at terminal p 





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W – gross weight of goods at 

terminal including packaging but 

excluding the tare weight of 


W U gross weight of goods 

unloaded including packaging but 







W L – gross weight of goods loaded 

at terminal including packaging but 



W t gross weight of goods 

transiting including packaging but 







W(p) – gross weight of goods at 

port p including packaging but 



W U (p) gross weight of goods 

unloaded at port p including 


Derived from terminal statistics 


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W L (p) – gross weight of goods 

loaded at port p including packaging 

but excluding the tare weight of 


W t (p) gross weight of goods 

transiting at port p including 







Definition 




The indicator can be directly calculated from terminal statistics 

depending on availability. Models might be used where needed. 

Model component C1 

Model component C2 


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Ref. 1.3.1 

Definition 

Estimated / measured energy consumption on roads 



toe per vehiclekm for road transport 

Consumption calculation examples from COMMUTE model, Commute software report, page 8 14 

PASSENGER transport ( see specific formulas in Annex I): 

E total = E hot + E cold 

The consumption equations are similar to emissions equations, E can be understood as consumption cp. ref 

template 1.3.2. The factors can be derived from HBEFA Hand Book Emission factors for road Transport 

Hot running consumption 

Ehot= c*m 

m 

= n × l 

The hot consumption factor e has to be corrected because of the emission / consumption relevant influences and is calculated from 

an equation having the form: 

x 

å 

e c = ( ps × a s ) × ( pl × a l ) × e 

i, v , k i, v , s i, v , k , s 

i, v , u i, v , k , u i, v , k 

s = 1 u = 1 

z 

å 

In the case of passenger cars, where load is considered not to affect emissions and consumption, this equation is further simplified: 

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x 

å 

e c = ( ps × a s ) × e 

i, v , k i, v , s i, v , k , s i, v , k 

s = 1 

For the calculation of the corrected hot consumption of the vehicle fleet, the equation for the basic hot emissions has to be 

converted to: 

i = categories 

æ 

EC = å ç n × l × p × ec 

è 

w 

å ( ) 

hot , k i i i , v i , v , k 

i = 1 

v = 1 

ö 

÷ 

ø 

Effectively, the product n i .l i in the above equation corresponds to “vehiclekilometre” (number of vehicles by travel distance) for 

each vehicle category i. 

For gasoline (with and without catalyst) and diesel cars (without catalyst), the formula for excess consumption) is in the following 

form: 

excess consumption= w×[f(V)+g(T)1]×h(d) 

d = d / d c 

engine start temperature (in °C) for starts at an intermediate temperature 

It is assumed that the excess emission of diesel cars with oxidation catalyst is proportional to the excess emission of diesel cars 

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without catalyst, according to the following equation: 

excess consumption for diesel cars with catalyst 

excess consumption for diesel cars without catalyst 

= a 

Cold correction 

E c = 

å 

i 

( i ) 

æ cm s, v 

ç tf i × × w i × 

è 100 

å å å 

j 

k 

m 

é p × p × p 

ê 

ë 10 6 × d 

j k m 

m 

d m 

× ( f ( V j ) + g ( T k ) - 1 ) × h 

æ ö ù ö 

ç ÷ ú ÷ 

è d c (V j ) ø û ø 

the following parameters are model internal data: 

cm (s, vi) percentage of mileage recorded under cold start or intermediate temperature 

conditions for season s and overall speed vi of vehicle type i (%) 

wi reference excess emission for vehicle type i (g) 

j speed class with a cold engine 

k class of startup engine temperature 

m trip length class 

pj percentage of the trips travelled at speed j with a cold engine, for the overall 

average speed considered (%) 

pk percentage of the trips travelled with a startup engine temperature Tk (%) 

pm percentage of trips started with a cold engine and distance dm, for speed Vj with a cold engine (%) 

dm average distance of the trips under cold start conditions of class m (km) 

Vj average speed with a cold engine corresponding to class j (km/h) 

Tk average startup temperature of class k (°C) 

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f, g, h and dc are provided as coefficients relevant for calculating cold emissions 

Examples from e.g. UNITE : 

corresponding unit costs 

Examples from RECORDIT: 

FREIGHT transport including marginal costs 





Output of 

the model 








E total= Total energy consumption 

E hot= Hot running consumption 

e= the hot consumption factor (in g/km) 

m= the activity, in distance travelled per time unit 

(usually in km/year) 

n= is the number of vehicles in each category 

l= is the average distance travelled by the average 

vehicle of each category over the time unit (in km/year) 

ec= the corrected hot consumption factor for the vehicle fleet, in 

consideration of the influence of gradient and in the case of 

catalyst vehicles of mileage 

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i=the number of vehicle categories 

v=the road type, practically signifying the mean speed 

k=the number of pollutants 

s= the gradient class 

x= the number of gradient classes 

u=the load class 

ps= the percentage of annual distance travelled at gradient class 

αs= the correction factor for gradient class s 

pl=the percentage of annual distance travelled at load 

class u 

αl= the correction factor for load class u 

EC= the corrected hot consumption of the vehicle fleet 

n= the number of vehicles 

l= the average annual distance travelled 

w= number of road types (urban, nonurban, motorway) 

p= the percentage of annual distance travelled 

excess consumption for a trip, expressed in g 

ω= reference excess consumption (at 20 °C and 20 km/h) 

V= means speed during the cold period (in km/h) 

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T= ambient temperature (in °C) for cold starts 

δ= undimensioned distance 

d= travelled distance 

d c = cold distance 

E cold= Cold correction consumption 

Ec= traffic excess emissions with a cold engine over 1 km 

for a given pollutant (in g) 

tfi= traffic flow for the studied vehicle type i (in km · veh) 

vi= traffic overall average speed for the studied vehicle type 

i (km/h) 

s,= season (winter, summer, middle) 

Road type* 

Road gradient distribution* 

Method variable V37 Temperature* Model default from COMMUTE can be used if case 

specific data not available 

Method variable V38 Traffic amount, m= total annual mileage of gasoline 

powered vehicles of category j* 

Method variable V39 Average trip length* Updated model default from COMMUTE 

Method variable V40 Average speed* Updated model default from COMMUTE can be used 

if case specific data not available 

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Method variable V41 Average load* Updated model default from COMMUTE can be used 

if case specific data not available 

Method variable V42 Average occupancy* Updated model default from COMMUTE 

Method variable V43 Fleet composition* Updated model default from COMMUTE 


Type of loading unit (Swap Body Class A or 20 feet 

container)* 

Method variable V45 Emission/ consumption factors COMMUTE/ MEET 

Method variable V46 Emission Standard (Euro I, II, III, IV) Old vehicles? 

Method variable V47 Loading factor (Loaded vkm/total vkm)* WP 3 


Chot= Hot running consumption 

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Ref. 2.3.1 

Definition 



Estimated/measured energy consumption of railways 

Energy Consumption of rail within a link or area divided between train characteristics of freight and passenger transport 

COMMUTE model for passenger transport 

RECORDIT model for freight transport 

COMMUTE: 

where: 

v 

' 

E ( N + 1 ) 2 

max 

D h 

@ 

+ . . g 

L 

B + B v + 

ave B v 2 + 

0 1 2 ave 

2 

L 

g 

gravitational constant 



Method variable V1 E’ the energy consumption in 

KJ/tonnekm* 

Method variable V2 L trip length (km)* 

Method variable V3 Dh change in height (gradient) (m)* 




Energy consumption in KJ/tonnekm 

Method variable V4 N number of intermediate stops* default values from COMMUTE 

Method variable V5 V ave average speed (m/s)* default values from COMMUTE 

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Method variable V6 V max maximum speed (m/s) * default values from COMMUTE 

Method variable V7 B0 constant equating to rolling 

resistance 

Method variable V8 B1 constant equating to friction 

resistance 

Method variable V9 B2 constant equating to cross 


sectional aerodynamic resistance 

Capacity (max. number of loading 

units)* 

default values from COMMUTE 









Loading factor (number of loading units 

carried/capacity)* 

Loading Unit Weights* 

Number of stops 

Number of wagons* 

Type of loading unit* 

Average segment speed* 

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Ref. 4.2.1 

Definition 



Estimated/ measured energy consumption on waterways 

Energy Consumption of rail within a link or area divided between ship categories 

COMMUTE 

LINKFUELCONSUMPTION Link , FuelType = 

æ SHIPMOVEMENTS TypeShip , Sizeclass * FUELCONSUMPTION TypeShip , Sizeclass , FuelType * LINKLENGTH ö Link 

å ç 

OPERATINGSPEED 

TypeShip , Sizeclass è 

TypeShip , Sizeclass * 1, 

852 

÷ 

ø 

( * * 

) 

PORTFUELCONSUMPTION Node , FuelType = å PORTCALLS TypeShip , Sizeclass FUELCONSUMPTIONPORT TypeShip , Sizeclass , FuelType PORTSTAYTIME Node , TypeShip , Sizeclass 

TypeShip , Sizeclass 

Fuel Consumption= C jk = K * Gross Tonnage + L [ton/day] 

Remark 

Inland waterways data poorly 

available 




Cjk fuel consumption at full power 

(t/day) as function of gross tonnage 

(GT) 




Energy consumption in KJ/tonnekm 

Method variable V2 j fuel type Energy consumption in KJ/tonnekm 

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k ship class 

Method variable V4 K variable from MEET table C) 

depending on ship type 

Method variable V5 L variable from MEET table C) 

depending on ship type 


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Ref. 1.3.2 

Definition 

Estimated/ measured road transport emissions + corresponding marginal cost 


Transport emissions are estimated for different vehicle composition on a link type divided by passenger and freight transport. 

Computation Method (Formula) Emission calculation examples from COMMUTE model, Commute software report, page 8 14 

PASSENGER transport ( see specific formulas in Annex I): 

E total = E hot + E cold + E evap 

(E evap only for VOC) 

The factors can be derived from HBEFA Hand Book Emission factors for road Transport 

Hot running emissions 

E 

hot 

= e × m 

m 

= n × l 

The hot emission/consumption factor e has to be corrected because of the emission / consumption relevant influences and is 

calculated from an equation having the form: 

x 

å 

e c = ( ps × a s ) × ( pl × a l ) × e 

i, v , k i, v , s i, v , k , s 

i, v , u i, v , k , u i, v , k 

s = 1 u = 1 

z 

å 

In the case of passenger cars, where load is considered not to affect emissions and consumption, this equation is further simplified: 

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x 

å 

e c = ( ps × a s ) × e 

i, v , k i, v , s i, v , k , s i, v , k 

s = 1 

For the calculation of the corrected hot emissions / consumption of the vehicle fleet, the equation for the basic hot emissions has to 

be converted to: 

i = categories 

æ 

EC = å ç n × l × p × ec 

è 

w 

å ( ) 

hot , k i i i , v i , v , k 

i = 1 

v = 1 

ö 

÷ 

ø 

Effectively, the product n i .l i in the above equation corresponds to “vehiclekilometre” (number of vehicles by travel distance) for 

each vehicle category i. 

For gasoline (with and without catalyst) and diesel cars (without catalyst), the formula for excess emission (or excess consumption) 

is in the following form: 

excess emissions= w×[f(V)+g(T)1]×h(d) 

engine start temperature (in °C) for starts at an intermediate temperature 

d = d / d c 

It is assumed that the excess emission of diesel cars with oxidation catalyst is proportional to the excess emission of diesel cars 

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without catalyst, according to the following equation: 

excess emission for diesel cars with catalyst 

excess emission for diesel cars without catalyst 

= a 

Cold correction 

E c = 

å 

i 

( i ) 

æ cm s, v 

ç tf i × × w i × 

è 100 

å å å 

j 

k 

m 

é p × p × p 

ê 

ë 10 6 × d 

j k m 

m 

d m 

× ( f ( V j ) + g ( T k ) - 1 ) × h 

æ ö ù ö 

ç ÷ ú ÷ 

è d c (V j ) ø û ø 

the following parameters are model internal data: 

cm (s, vi) percentage of mileage recorded under cold start or intermediate temperature 

conditions for season s and overall speed vi of vehicle type i (%) 

wi reference excess emission for vehicle type i (g) 

j speed class with a cold engine 

k class of startup engine temperature 

m trip length class 

pj percentage of the trips travelled at speed j with a cold engine, for the overall 

average speed considered (%) 

pk percentage of the trips travelled with a startup engine temperature Tk (%) 

pm percentage of trips started with a cold engine and distance dm, for speed Vj with a cold engine (%) 

dm average distance of the trips under cold start conditions of class m (km) 

Vj average speed with a cold engine corresponding to class j (km/h) 

Tk average startup temperature of class k (°C) 

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f, g, h and dc are provided as coefficients relevant for calculating cold emissions 

Evaporative correction 

E evap, VOC, j = 365. a j (e d + S c + S fi ) + R 

and: 

x = v j / (365. l trip ) 

S c = (lq) (p . x . e s, hot + w . x . e s, warm ) 

S fi = q . e fi . x 

R = m j (p . e r, hot + w . e r, warm ) 

l trip 

average trip length (km) 

Examples from e.g. UNITE : 

corresponding unit costs 

Examples from RECORDIT: 

FREIGHT transport including marginal costs 

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Output of 

the model 













E total= Total emissions 

E hot= Hot running emissions 

e= the hot emission factor (in g/km) 

m= the activity, in distance travelled per time unit 

(usually in km/year) 

n= is the number of vehicles in each category 

l= is the average distance travelled by the average 

vehicle of each category over the time unit (in 

km/year) 

ec= the corrected hot emission / consumption factor for the 

vehicle fleet, in consideration of the influence of gradient 

and in the case of catalyst vehicles of mileage 

i=the number of vehicle categories 

v=the road type, practically signifying the mean 

speed 

k=the number of pollutants 

s= the gradient class 

x= the number of gradient classes 

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u=the load class 

ps= the percentage of annual distance travelled at gradient 

class s 

αs= the correction factor for gradient class s 

pl=the percentage of annual distance travelled at 

load class u 

αl= the correction factor for load class u 

EC= the corrected hot emission/consumption of 

the vehicle fleet 

n= the number of vehicles 

l= the average annual distance travelled 

w= number of road types (urban, nonurban, 

motorway) 

p= the percentage of annual distance travelled 

excess emission for a trip, expressed in g 

ω= reference excess emission (at 20 °C and 20 km/h) 

V= means speed during the cold period (in km/h) 

T= ambient temperature (in °C) for cold starts 

δ= undimensioned distance 

d= travelled distance 

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d c = cold distance 


E cold= Cold correction emissions 

Method variable V31 Ec= traffic excess emissions with a cold engine over 1 

km for a given pollutant (in g) 

Method variable V32 tfi= traffic flow for the studied vehicle type i (in km · 

veh) 






vi= traffic overall average speed for the studied 

vehicle type i (km/h) 

s,= season (winter, summer, middle) 

E evap= Evaporative correction emissions 

E evap, VOC, j = VOC emissions due to evaporative losses 

caused by vehicle category j 

a j = number of gasoline vehicles of category j 

Method variable V38 e d = mean emission factor for diurnal losses of 

gasoline powered vehicles equipped with metal tanks, 

depending on average monthly ambient temperature, 




temperature variation and fuel volatility (RVP) 

S c = average hot and warm soak emission factor of 

gasoline powered vehicles equipped with carburettor 

S fi = average hot and warm soak emission factor of 

gasoline powered vehicles equipped with fuel 

injection 

R= hot and warm running losses 

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q= fraction of gasoline powered vehicles with fuel 

injection 

p= fraction of trips finished with hot engine 

(dependent on average monthly ambient temperature) 

x= mean number of trips of a vehicle per day, average 

over the year 

w= fraction of trips finished with cold or warm engine 

(shorter trips) or with catalyst below its lightoff 

temperature 

Road type* 

Road gradient distribution* 

Method variable V49 Temperature* Model default from COMMUTE can be used if case specific 

data not available 


Traffic amount, m= total annual mileage of gasoline 

powered vehicles of category j* 

Method variable V51 Average trip length* Updated model default from COMMUTE 

Method variable V52 Average speed* Updated model default from COMMUTE can be used if case 


Method variable V53 Average load* Updated model default from COMMUTE can be used if case 


Method variable V54 Average occupancy* Updated model default from COMMUTE 

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Method variable V55 Fleet composition* Updated model default from COMMUTE 


Type of loading unit (Swap Body Class A or 20 feet 

container)* 

Method variable V57 Emission/ consumption factors COMMUTE/ MEET 

Method variable V58 Emission Standard (Euro I, II, III, IV) Old vehicles? 

Method variable V59 Loading factor (Loaded vkm/total vkm)* WP 3 


Chot= Hot running consumption 

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Ref. 2.3.2 

Definition 



Estimated/measured rail transport emissions + corresponding unit cost 

Emissions of rail transport within a link or area 

COMMUTE model for passenger transport 

The emissions are calculated from energy consumption according to MEET tables. (COMMUTE software report p. 1920 ) 

Energy consumption equation is shown in Indicator Compilation Table 2.3.1 

E= emission factor * Energy Consumption 

The sulphur dioxide emissions from diesel combustion are heavily dependent on the sulphur content of the fuel. The fuel specific 

sulphur emission (FSSO2) may be derived from the following expression: 

FSSO2 = 20 x %S 

where %S is the mass weight percent of sulphur in fuel. The value in the above table indicates a range of fuel sulphur content from 

0.05 to 0.5%. 

RECORDIT model for freight transport 




FSSO2= sulphur emissions 




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%S = the mass weight percent of 

sulphur in fuel 

Method variable V3 Electrical power generation – 

European average/Country specific 

Updated tables from MEET/COMMUTE 

Method variable V4 Diesel railway emissions Updated MEET/COMMUTE database 

Method variable V5 Degree of electrification Updated MEET/COMMUTE database 

Method variable V6 Emission factors MEET/COMMUTE 

Method variable V7 Consumption factors MEET/COMMUTE 

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Ref. 4.2.2 

Definition 

Estimated/measured Inland waterway transport emissions 


Estimated waterway transport emissions divided between ship categories, links or ports 

Computation Method The COMMUTE equations are for sea. The port emissions for inland waterways could be assessed similarly. 

(Formula) 

Emissions, except SO 2 

LINKEMISSIONS 

Link , Emissiontype 

= 

æ SHIPMOVEMENTS , * FUELCONSUMPTION , * EMISSIONFACTOR , , * LINKLENGTH 

å ç 

è 

OPERATINGSPEED TypeShip , Sizeclass * 1, 

852 


TypeShip Sizeclass TypeShip Sizeclass TypeShip Sizeclass Emissiontype Link 

ö 

÷ 

ø 

( * * * 

) 

PORTEMISSIONS = å PORTCALLS FUELCONSUMPTIONPORT EMISSIONFACTOR PORTSTAYTIME 

Emissions SO2 

LINKEMISSIONS 

Node , Emissiontype TypeShip , Sizeclass TypeShip , Sizeclass TypeShip , Sizeclass , MarineDiesel Node , TypeShip , Sizeclass 

Link , SO 2 

= 

å 



å ( ) 

æ SHIPMOVEMENTS TypeShip , Sizeclass 

ç 

* FUELCONSUMPTION TypeShip , Sizeclass , Fueltype * SULPHURCONTENT TypeShip , Sizeclass , Fueltype * 20 * LINKLENGTH 

ç 

FuelType 

OPERATINGSPEED TypeShip , Sizeclass * 1, 

852 

ç 

è 

( * * * * 

) 

PORTEMISSIONS 

2 

= å PORTCALLS FUELCONSUMPTIONPORT SULPHURCONTENT 20 PORTSTAYTIME 

Node , SO TypeShip , Sizeclass TypeShip , Sizeclass , FuelType TypeShip , Sizeclass , FuelType Node , TypeShip , Sizeclass 


Link 

ö 

÷ 

÷ 

÷ 

ø 

remark 

+ corresponding marginal cost from reference study, for example UNITE 

Arial emissions could be considered 



Directly from a database or 

agreed 



Method variable V1 LINK EMISSIONS emissions across link Energy consumption in 

tonnekm 

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Method variable V2 PORT EMISSIONS emissions at ports Energy consumption in 

tonnekm 


Ship movements ships moving between a link of ship type and class 

* 

Method variable V4 Type ship specific type of ship default values from COMMUTE 

Method variable V5 Size class – specific size of ship default values from COMMUTE 

Method variable V6 Fuel type fuel type used default values from COMMUTE 



Fuel consumption of a ship– regarding type of ship, size class and fuel 

type 

Capacity (max. number of loading units)* 

Method variable V9 Loading factor (number of loading units 

carried/capacity) * 

Method variable V10 port calls – number of port calls * 


Method variable V11 Emission factor default values from 

COMMUTE/MEET 

Method variable V12 Sulphur content default values from 

COMMUTE/MEET 

Remarks 

The most important environmental effects in waterways are accident risks. In ports the most important are noise, CO2, NOx, PM small, 

VOC. 

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Ref. 1.3.3 

Definition 



Noise levels generated by road transport + corresponding marginal unit 

Noise generate by a link in Watts/ Exposure to over a specific noise level in dB 

A general approach has been defined in COMMUTE. The default reference conditions are as follows; 

· average vehicle speed, v 75 km/hr 

· percentage heavy vehicles, p 0% 

· percentage uphill gradient, g 0% 

The formula used to derive the combined average vehicle speed and percentage heavy vehicles correction is as follows; 

· correction (multiply by): (v+40+500/v)^ 3.3 x (1 + 5 p/v)/7604424 

The formula used to derive the percentage uphill gradient correction is as follows; 

· correction (multiply by): 10^ (0.03g) 

The formula to calculate average sound intensity in watts/m 2 at any given radial distance, r, and ignoring excess attenuation is as follows. For road traffic noise 

the standard reference radial distance, d, is 10 m from the side of the carriageway, which is 13.5 m from the centreline of vehicles moving along the road; 

· sound intensity = sound power per metre/(p x d) 

· sound pressure = square root (sound intensity x 415) 

· sound pressure level = 20 x log(sound pressure/0.00002) 

· link sound power= number of vehicles*corrected source sound power 

· corrected sound power= reference sound power per vehicle per hour*heavy vehicles correction factor* uphill gradient correction factor 

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+ Corresponding unit cost from UNITE or other reference study? 

RECORDIT 

for freight transport 



Method variable V1 v vehicle speed * 

Method variable V2 p percentage of heavy vehicles * 





reference sound power per hour 

Method variable V4 number of vehicles per hour * 

Method variable V5 percentage of uphill gradient * 

Method variable V6 Noise factors for reference traffic conditions Defaults from COMMUTE 

Method variable V8 sound intensity Defaults from COMMUTE 

Method variable V9 sound pressure Defaults from COMMUTE 

Method variable V10 link sound power Defaults from COMMUTE 

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Ref. 2.3.3 

Definition 



Noise levels generated by rail transport + corresponding unit cost 

Noise generate by a link in Watt/ Exposure to over a specific noise level in dB 

COMMUTE 

· sound intensity = sound power per metre/(p x d) 

· sound pressure = square root (sound intensity x 415) 

· sound pressure level = 20 x log(sound pressure/0.00002) 

· link sound power= number of vehicles*corrected source sound power 

· corrected sound power= reference sound power per vehicle per hour*tread break correction factor* disc brake correction factor 

+ Corresponding unit cost from UNITE? 

RECORDIT 

for freight transport 






Method variable V1 n. of disc braked passenger trains * 

Method variable V2 p percentage of freight trains * 


reference sound power per hour 

Method variable V4 number of diesel and electric locomotives per hour * 

Method variable V5 speed * 

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ANNEX C: EXPERIENCE FROM CONCERTED 

ACTION ON SHORT SEA SHIPPING 

PROJECT



Introduction 

MDSTransmodal were retained as subcontractors within the Concerted Action on Shortsea 

Shipping contract to collect and process statistics that would form part of a detailed regional 

traffic matrix for freight flows between EU member states and the rest of the world. Recent 

studies have highlighted the importance of accurate OD matrices within transport modelling and 

transport policy applications, and the absence of a single reliable source that takes advantage of 

the regional statistics that are compiled at national levels. 

The overriding objectives of this work were therefore to: 

· Obtain national sources of regional traffic, 

· Process them into a common format, and to 

· Combine them to create regionregion flows. 

Scope 

There is no ideal set of definitions for a project of this nature, so an attempt has been made to 

balance the need for detail with the need for robustness. 

The geographical coverage has been designed to address the needs of projects related to short 

sea shipping. This includes trade between EU and EFTA member countries, and also flows 

between other nearby countries such as Eastern Europe, and the nonEuropean Mediterranean. 

Within member states, it has been necessary to design the zone structures so that they follow 

existing regional boundaries used by the national governments. In general, it has been possible 

to follow the NUTS (Nomenclature of Territorial Units for Statistics) system designed by the 

Statistical Office of the European Communities. However, different levels of precision have 

been applied in different countries. 

This amounts to a "central" matrix of 128 European (EU, Norway and Switzerland) zones, 

surrounded by external zones defined either as countries or groups of countries. 

As far as commodity detail is concerned, the SITC (Standard International Trade Classification) 

system has been used, as this provides a hierarchical framework that works well at the national 

level (where there is a detailed record of the commodity split) and regional levels (where the use 

of sample data may require a more aggregated approach to commodity detail). 

One particular advantage of the SITC system is that at the 2digit level (65 different commodity 

definitions) there is enough detail to be able to identify the main handling characteristics of 

specific goods, particularly manufactures, without the need for several hundred definitions. 

The alternative NST system is commonly used in Europe, but it has proved possible to convert 

from NST to SITC wherever the need has arisen. 

SubDivision of Work 

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To take advantage of the different areas of specialisation between the team members, the work 

has been subdivided. MDSTransmodal were responsible for obtaining and processing data 

from: 

· France 

· Spain, and 

· The United Kingdom 

In addition, MDSTransmodal was required to produce a database of countrycountrycommodity 

totals based on the Eurostat Comext (Trade Statistics) database. The database was 

enhanced by the estimation of the unitised/nonunitised split for each trade flow. This is 

fundamental for understanding the demand for specific types of transport e.g. trailers, 

containers, general cargo, liquid bulk, dry bulk etc. 

Finally, MDSTransmodal was supplied with a German regional database, which was combined 

with the regional data from the other listed countries to produce a regionregion matrix covering 

France, Spain, the UK and Germany. 

Data Collection 

The success of this project depends to large extent on the type of data readily available from the 

member states. Although it is technically possible to construct synthetic matrices purely on the 

basis of measures of economic activity within specific regions, the probability of error is severe. 

It is therefore critical to be able to analyse trip end totals at a regional level. This provides the 

necessary input for understanding which industries are located where, and their relative 

importance. 

For all three countries within the MDSTransmodal remit, regional data has been successfully 

obtained. 

· France 

The French source is the DNSCE (Customs and Excise) database of external trade. This 

conveniently classifies trade movements by country of origin/destination, commodity (NST), 

French Department (NUTS3), and volume (weight and value). The data used within this study 

is for 1997 full year, and is essentially a complete record of regional imports and exports. 

The data was processed to remove superfluous data fields, and to compress the commodity 

definition from NST4 Digit to SITC2Digit, using a standard correlation table. The country 

codes were the EC standard Comext codes. 

The main addition was therefore to convert the total tonnes (as given) into unitised and nonunitised 

tonnes, using lookup tables detailed for each partner country and commodity, derived 

from MDSTransmodal's trade data archive. The main problem was to deal with country and 

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commodity combinations that did not match with any records in the MDSTransmodal lookup 

tables. For these, an iterative process was developed so that mode factors could be obtained 

from similar countries or similar commodities or both. 

Problems also arise from the inclusion of "Departements d'outremer" or "DOM", i.e. the West 

Indies as regions of France. Users of the database need to be aware that flows into these 

departements do not represent short sea shipping. 

The NUTS3 Departements were aggregated to NUTS2 Regions, reducing the total number of 

subnational zones from 100 (96 excluding DOM) to 21 mainland regions plus Corse, 

Guadeloupe, Martinique, Guyane, and Reunion. 

· Spain 

The situation regarding Spain is somewhat similar to France. Again, the source was a Customs 

& Excise database, containing regional detail for 50 zones in Spain ("Provincias"). This is 

equivalent to NUTS3. The database was dispatched on seven magnetic tapes, and amounted to 

over 700 megabytes of data. 

A database program was developed by MDSTransmodal to read the files extracted from the 

tapes, and to compress the data by lifting out the key fields of data. The commodity 

classification system used was the standard 8 digit Harmonised System, as used within Comext 

as well as the majority of countries worldwide. Again, correlation tables were used to convert 

this to 2 digit SITC. 

Country codes were again based on the standard Eurostat practice. 

As before, the estimated unitised/nonunitised split was introduced, using the factors already 

calculated by MDSTransmodal at the national level. 

The main problem has been the need to compress vast quantities of data to a few megabytes, 

and the relative unreliabity of using tapes as a storage media. As with the French database, 

offshore regions such as the Canarias, Baleares, Ceuta and Melilla have required special 

treatment. 

The 52 NUTS3 Provincias (including Ceuta and Melilla) have been aggregated to 15 mainland 

NUTS2 Communidades Autonomas plus Baleares, Canarias, and CeutayMelilla. 

· The UK 

The UK experience is somewhat different to Spain and France. UK trade statistics have never 

recorded regional data such as UK country of origin or destination. The main source of regional 

data has been the Origin and Destination of International Transport (ODIT) survey, carried out 

by the UK's Department of the Environment, Transport and the Regions (DETR). This is based 

upon a sampling technique, and is only carried out every five years (1986, 1991, 1996). 

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Ideally, it would have been possible to use just the 1996 survey, which was partly funded by the 

STEMM project (DGVII), but experience has shown that this survey contains deficiencies. In 

order to complete the freight modelling work within the STEMM project, the survey data was 

enhanced by contributing data from other sources such as the 1996 International Road Haulage 

Survey (IRHS) and the Railfreight Distribution database. Finally it was grossed up using UK 

trade data for 1996, and given greater regional strength by incorporating parts of the 1991 

ODIT, which had a stronger methodology. (See STEMM Report). 

The ODIT source is essentially a record of unitised trade flows only as the consolidation of 

bulks in port silos and tanks makes it very difficult to match origins to destinations using a 

questionnaire approach. It was not considered sensible to estimate the inland origins and 

destinations of such commodities. 

Regional data is collected at the county level (NUTS3). Problems have been encountered in 

mixing basic data from different years, as a series of changes have occurred in the definition of 

county boundaries. However, in aggregating to ten NUTS1 standard regions, (excluding 

Northern Ireland) these inconsistencies have been removed. 

Building RegionRegion Matrices: The Problem 

The final stage has been to tackle one of the classic transport problems which is how to convert 

data of the form: 

Region R1 (belonging to Country C1) to Country C2: T1 tonnes. 

Region R2 (belonging to Country C2) to Country C1: T2 tonnes. 

Into: 

Region R1(in C1) to Region R2 (in C2) : T3 tonnes. 

The problem can be observed as an origindestination matrix where the row and column totals 

are known, but the individual cells are unknown. 

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For example: 

Orig/ Dest Dest1 Dest2 Dest3 Total 

Orig1 100 

Orig2 200 

Orig3 300 

Total 150 250 200 600 

For this study, the problem can be seen as a series of large matrices, one for each commodity. 

The origins and destinations are a mixture of countries and regions. Country to/from country 

and country to/from region flows will be known, but subsets of the matrices will appear as 

above, e.g. France to/from Spain. 

For example, Iron and Steel Trade, 

Orig/ Dest Champagne Picardie Hte Normandie Total 

Galicia 100 

Asturias 200 

Cantabria 300 

Total 150 250 200 600 

There are various iterative procedures for finding "solutions" to these problems, but these are 

essentially computer algorithms that have too many degrees of freedom to reach conclusive 

results. They fit but they are not necessarily right. 

It is possible to "seed" the matrix by filling it with particular values before the iteration solves it. 

The seeding biases the results, so if the seeding is performed according to a valid theory of what 

the matrix represents, it should improve the result. 

One possibility is the socalled "Gravity Model" which takes into account the distance between 

any two regions in the matrix. By introducing seeded values inversely proportional to the 

distance (interpreted as the attraction between cells) between the regions it ought to be possible 

to improve accuracy. Further accuracy might be achieved by extending the analysis to using 

Generalised Cost instead of pure distance, and other factors such as language compatibility, 

common currency, joint membership of trade bloc, and so on. 

Many sophisticated procedures can be hypothesized, but without any base matrices to test the 

results against, it is difficult to judge their validity. In these circumstances, if the base matrix 

were known, there would be no practical reason for trying to estimate it. This is the problem. 

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Building RegionRegion Matrices: A Solution 

The solution employed by MDSTransmodal has been to build a Gravity model for seeding the 

unknown cells in the matrix, and to optimise this for each commodity, using an optimisation 

algorithm known as an "amoeba". The optimisation was conducted on a countrycountry matrix 

where all the genuine values for the cells were known in advance. The parameters were stored, 

and then reused when the national data was broken down into regions. 

Thus, the problem was explored at a national level, and then an optimum solution was 

transplanted to a regional level. Naturally, the regional results produced could not be tested, but 

at least the matrix could be seeded with values known to produce vastly improved results at a 

more aggregated level. Furthermore, the pattern of parameter values for the gravity model used 

to implement the seeding, could be used to categorize specific commodities in terms of their 

propensity to be widely or narrowly distributed. 

Step 1: Selecting the Seeding Model 

The selection process was driven mainly by practicality. It was decided that for simplicity, just 3 

parameters would be considered: 

1. A measure of the cost in terms of distance between the 2 regions 

2. The total exports of the exporting region 

3. The total imports of the importing region 

Volume indicators such as population and GDP tended to be highly correlated with the total 

exports/imports. 

A simple formula based on physical common sense could be: 

T = d 

e 

n - md 

EI 

where T= trade, d= measure of cost in terms of distance, E= total exports of exporting region, I= 

total imports of importing region. The n and m are variables. 

Step 2: Optimising the Parameters 

After running this formula, a furnessing algorithm is run to ‘massage’ these ‘seed’ values to the 

known totals. The furnessing algorithm finds the existing total of one column and compares this 

to the required column total. All values in this column are then scaled up accordingly such that 

the new column total equals the required column total. This is done separately for all columns. 

This process is then repeated for the rows. If this combined column and row process is repeated 

several times, gradually the cell values become stable and converge. 

It was noted that the column totals (E) are redundant because the furness scaling cancels them 

out immediately but for the formula to have a sensible physical representation, they were still 

included. 

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The objective of this program is to find the optimum values of the variables so as to create a 

formula that will predict the main body of the trade table as accurately as possible. 

There has to be a method of evaluating the accuracy of the formula. There is some existing real 

trade data where the body of the table is already known. By comparing the calculated cell values 

to the known values in this dataset and adding up all the absolute differences between 

calculated and known values, an overall measure of the error is obtained. 

The variables ‘n’ and ‘m’ have to be varied so as to minimize this total error. This is done using 

the “amoeba” function. 

This final error can then be compared to the error if there had been no initial seeding (only the 

furnessing) and a percent improvement found. 

There is data for many different commodities so this process can be run for all of them, 

obtaining different variable values depending on the pattern of trade for each particular 

commodity. 

The optimized values for ‘m’ and ‘n’ for each SITC commodity are: 

Results 

SITC no. and name n m Error After 

Seeding 

Error Before 

Seeding %better 

00 Animals 4.72864 0.030949 663,155 850,538 22 

01 Meat 0.429564 0.001106 2,287,460 3,214,260 29 

02 Dairy 0.55379 0.003387 4,077,450 8,069,130 49 

03 Fish 1.04392 0.000825 1,381,200 2,204,350 37 

04 Cereals 2.86474 0.000378 2,567,290 4,326,630 41 

05 Fruit/Veg 0.738579 0.000339 8,730,710 12,457,600 30 

06 Sugar 0.637274 0.002179 2,196,140 3,458,890 37 

07 Coffee/Tea 1.6522 0.000228 829,043 1,126,480 26 

08 Animal Feed 2.0288 0.000598 2,582,060 4,520,360 43 

09 Misc Edibles 1.07391 0.000701 1,057,500 1,742,980 39 

11 Beverage 0.110612 0.055329 10,267,100 20,839,500 51 

12 Tobacco 1.637 0.001496 274,518 291,650 6 

21 Hides Raw 1.6443 0.000575 365,381 450,471 19 

22 Oil Seed 1.54893 0.005966 16,746 28,214 41 

23 Cr Rubber 0.364645 0.001242 517,973 660,432 22 

24 Wood 4.25486 0.001628 332,351 1,076,000 69 

25 Pulp 0.337764 0.005893 785,465 2,029,800 61 

26 Textiles 1.3155 7.51E05 716,393 1,049,480 32 

27 Crude Ferts 4.05698 0.001776 6,818,050 10,439,800 35 

28 Ore/Scrap 0.752897 0.001408 1,170,690 1,813,910 35 

29 Oth Crude Mats 0.878426 0.001608 1,899,030 2,592,650 27 

32 Coal/Coke 2.8805 0.000712 783,348 1,871,700 58 

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SITC no. and name n m Error After 

Seeding 

Error Before 

Seeding %better 

33 Petroleum 4.18336 0.000854 335,128 551,877 39 

41 Animal Fats 1.6663 0.001616 245,791 389,645 37 

42 Veg Fats 0.180219 0.004108 948,911 1,621,800 41 

43 Other Oils 1.33137 0.000556 378,736 506,854 25 

51 Organic Chem 0.176975 0.000949 3,615,750 4,391,430 18 

52 Inorganic Chem 0.545743 0.001132 8,905,440 12,919,100 31 

53 Dyes 1.3526 0.000245 1,692,210 2,630,380 36 

54 Pharmaceuticals 0.04056 0.000915 332,371 438,903 24 

55 Essential Oils 1.18802 0.000254 1,714,550 2,565,630 33 

56 Other Fertilisers 0.298037 0.001676 3,261,300 4,948,300 34 

57 Prim Plastics 0.370048 0.000889 5,892,760 9,592,560 39 

58 Other Plastics 1.01699 0.000331 1,333,080 1,886,520 29 

59 Chemical Materials 1.78155 0.000152 4,747,050 7,263,560 35 

61 Leather 2.85753 0.001231 82,734 108,033 23 

62 Rubber Manuf. 0.759114 0.000127 977,820 1,174,680 17 

63 Wood Manuf. 2.09861 6.79E05 2,266,900 4,268,710 47 

64 Paper 1.15295 0.000397 5,180,710 7,184,460 28 

65 Textiles 0.749137 0.000149 1,657,590 2,222,400 25 

66 Non Metallic Manuf 2.57571 0.000317 11,136,100 22,532,500 51 

67 Steel 1.14903 0.000178 3,901,870 5,928,690 34 

68 Non Ferrous Metals 1.15747 0.000115 3,705,220 5,077,670 27 

69 Other Metal Manuf. 2.44048 0.000498 2,992,420 5,806,260 48 

71 Power Machinery 0.531799 0.001082 1,149,270 1,236,270 7 

72 Specialise Machinery 1.11267 4.78E06 866,170 1,284,450 33 

73 Metal Machinery 1.22889 0.000244 197,304 236,321 17 

74 General Industrial Mc 0.668388 0.000239 1,867,980 2,319,570 19 

75 Office Machinery 0.064217 0.000876 330,987 391,512 15 

76 Telecom 0.566591 0.000382 532,228 614,457 13 

77 Electrical Mc 0.72881 0.000168 2,315,210 2,639,640 12 

78 Road Vehicles 0.089321 0.000762 4,156,750 4,607,230 10 

79 Oth Transport Equip 1.17526 0.000567 98,373 130,445 25 

81 Prefabs 2.88769 0.00085 772,798 1,202,500 36 

82 Furniture 1.89748 0.000158 1,397,840 2,228,640 37 

83 Travel Goods 1.5887 0.000142 60,105 69,651 14 

84 Clothes 1.3858 0.00024 431,867 521,253 17 

85 Footwear 0.456656 0.000104 184,464 196,684 6 

87 Scientific Machinery 0.274025 0.000514 161,173 181,466 11 

88 Photographic Mc. 0.421964 0.000564 136,003 165,466 18 

89 Miscellaneous Manuf 1.31818 0.00017 2,686,140 4,202,180 36 

90 Others 27.7121 0.021775 52,801 68,881 23 

Total 1.22196 9.08E05 101,163,000 179,847,000 44 

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Step 3: Appraising the Results 

This data is represented below in graphical form: m versus n. 

The more negative n is, the quicker the function goes to zero because of the d to the power of n 

term. The more positive m is the quicker the function goes to zero because of the exponential 

term. The difference is in the shape of the curve: 

· An exponential curve behaves sensibly near zero starting at 1 and gradually decreasing. 

Then at high distance it becomes very small. 

· A powern (n negative) curve starts at infinity (at distance = zero) and quickly comes 

down but is less active than the exponential term at higher distances. 

If n is positive, the seeding graph of trade vs distance initially goes up from zero (for small d 

while the exponential term is negligible). As the exponential term catches up and becomes 

dominant at larger d, trade falls back down again to approach zero at high distances. 

A large positive n keeps the exponential term negligible until higher distance so the graph 

continues to rise for longer. A large positive m allows the exponential term to quickly dominate, 

keeping the peak close to zero distance. 

The variable m should not be negative. Where it is, n should always be negative and the 

exponential term does not become dominant until beyond the fitted points. Although this may fit 

the data for the distances fitted, it is unrealistic for larger distances. Negative m should only 

result where the data is very sparse. 

Most commodities fit into the n negative, m positive position in mn space. The actual position 

depends on the shape of the trade vs distance curve. A highly negative n shows that the graph 

quickly descends within relatively small distances without giving much information as to trade 

at large distances. A large m shows that there is very little trade at large distances without 

giving much information about the small distance trade. 

So on the graphs below; negative n is a measure of the speed of descent of the curve at short 

distances. Positive m is a measure of the speed of descent of the curve at larger distances. 

The apparently anomalous value for ‘90 Others’ is not necessarily wrong it is just based on 

very sparse data and is the best estimate. 

11 Beverage has a high value of m. As it’s n is not very negative, this shows that at small 

distances, there is little dependence of trade on distance but at high distances, the trade quickly 

decreases. 

00 Animals again suffers from sparse data. This point suggests that the trade has a nonzerodistance 

peak before descending rapidly at high distance. 

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Step 4: Application 

New software was created to apply the results of this Gravity model to the actual trade 

databases, at the regional level. The software used the basic countrycountrycommodity 

database which originated from Eurostat. 

Where regions for neither country were known, the Eurostat record was passed straight into the 

output database. 

Where regions for one country were known (France, Spain, UK and Germany) the national 

totals were split according to the proportions recorded in the regional databases, and grossed up 

to the Eurostat figure for consistency. 

Where regions were known in both reporting and partner countries. The matrix was seeded with 

values from the optimisation process and the furnessing technique was applied. 

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Plotted Values for ‘n’ and ‘m’ 

0.06 

Commodities 

MT11 Beverage 

0.05 

0.04 

0.03 

MT00 Animals 

m 

MT90 Others 

0.02 

0.01 

0 

MT22 Oil MT25 Seed Pulp 

MT42 Veg Fats 

MT02 Dairy 

MT06 Sugar 

MT24 Wood MT41 MT29 MT28 

MT56 Anm Oth 

MT23 

MT03 

MT01 

Ore/Scra Fats Oth C Ma Fert 

MT52 Cr 

Fish 

Meat Rubbe 

MT32 MT08 MT09 

MT07 

MT21 Coal/Cok MT51 

MT05 

Anm 

MT57 MT54 MT71 Inorg 

Misc Org 

Feed 

Prim Pharmac Power 

Edb Chem 

Pla MC 

MT43 

Coff/Tea 

Hides Fruit/Veg Oils Ra 

MT26 MT55 

MT79 MT58 MT53 Textiles Ess Oth Dyes Oils Plas 

MT04 

MT63 MT59 

MT68 MT62 

MT64 MT67 

Cereals 

Wood MT65 

Chem 

Non Rubb 

Paper 

Man Steel 

Mat 

Ferr Man 


MT72 

N MT74 MT75 

Met M M Manf Metal 

Spec 

M 

Gen Off 

MC 

MC Indu 

MC 


MT77 MT78 Oth Telecom Photo Scient Tr 

Elec Road E MC M 

MC 

Veh 

MT82 MT83 

MT89 MT85 MTTotal Furnitur Travel 

Misc Footwear Man 

MT84 Clothes G 

MT33 MT81 Petroleu Prefabs 

MT61 

MT27 Cr Ferts 

MT12 

Leather 

Tobacco 

0.01 

5 0 5 10 15 20 25 30 

n 

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0.006 

Commodities 

MT22 Oil Seed 

MT25 Pulp 

0.005 

0.004 

MT42 Veg Fats 

MT02 Dairy 

0.003 

m 

0.002 

MT06 Sugar 

MT24 Wood 

MT41 Anm Fats 

MT56 Oth Fert 

MT29 Oth C Ma 

MT28 Ore/Scra 

0.001 

0 

MT32 Coal/Cok 

MT03 Fish 

MT23 Cr Rubbe 

MT52 MT01 Inorg Meat Ch 

MT51 

MT54 

Org Chem 

MT57 Prim Pla Pharmac 

MT75 Off MC 

MT64 Paper MT76 Telecom 

MT58 Oth MT05 Plas Fruit/Veg 

MT07 Coff/Tea MT53 MT55 Dyes Ess Oils MT74 Gen Indu 

MT89 MT67 Misc Man Steel MT77 

MT62 MT65 Elec 

Rubb Textile MC 

MT68 Man 

MT26 MTTotal Non Ferr 

Textiles 

MT85 Footwear 

MT72 Spec MC 

MT63 Wood Man 

MT82 MT59 Furnitur MT83 Chem Travel Mat G 

MT84 MT73 Clothes Metal MC 

MT66 N M M M 

MT04 Cereals 

MT69 Met Manf 

MT78 Road Veh 

MT09 Misc Edb 

MT08 Anm MT21 Feed Hides MT43 MT79 Ra Oth Oth Oils Tr E MT88 Photo MC 

MT87 Scient M 

MT71 Power MC 

0.001 

MT33 Petroleu 

MT81 Prefabs 

MT61 Leather 

MT12 Tobacco 

MT27 Cr Ferts 

0.002 

4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 0.5 1 

n 

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0.0012 

Commodities 

0.0011 

MT52 Inorg Ch 

MT01 Meat 

0.001 

MT51 Org Chem 

0.0009 

MT57 Prim Pla 

MT54 Pharmac 

MT75 Off MC 

MT03 Fish 

0.0008 

0.0007 

MT09 Misc Edb 

0.0006 

MT21 Hides Ra 

MT79 Oth Tr E 

MT43 Oth Oils 

MT88 Photo MC 

0.0005 

MT87 Scient M 

m 

0.0004 

MT64 Paper 

MT76 Telecom 

MT58 Oth Plas 

MT05 Fruit/Veg 

0.0003 

MT07 Coff/Tea 

MT55 Ess Oils 

MT53 Dyes 

MT74 Gen Indu 

0.0002 

0.0001 

MT67 Steel 

MT89 Misc Man 

MT68 Non Ferr 

MTTotal 

MT26 Textiles 

MT77 Elec MC 

MT65 Textile 

MT62 Rubb Man 

MT85 Footwear 

0 

MT72 Spec MC 

1E04 

MT83 Travel G 

MT82 Furnitur 

MT59 Chem Mat 

0.0002 

MT84 Clothes MT73 Metal MC 

0.0003 

2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 

n 

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Sample Output Database 

Type 1: No Regional Data 

Direction Rep RepName RepReg Par ParName ParReg Sitc2 UTonnes 

Imp 39 SWITZERLAND DKReg 804 NEW ZEALAND DKReg 84 6 




Imp 39 SWITZERLAND DKReg 807 TUVALU DKReg 3 1 

Imp 39 SWITZERLAND DKReg 810 US OCEANIA DKReg 3 1 

Imp 39 SWITZERLAND DKReg 810 US OCEANIA DKReg 5 6 

Imp 39 SWITZERLAND DKReg 813 PITCAIRN DKReg 5 11 

Imp 39 SWITZERLAND DKReg 813 PITCAIRN DKReg 58 9 

Imp 39 SWITZERLAND DKReg 815 FIJI DKReg 82 4 

Type 2: Regional Data at One End 


Exp 1 FRANCE R210 2 BELGIUM/LUX DKReg 0 446 










Type 3: Regional Data at Both Ends 


Exp 1 FRANCE R241 4 GERMANY R17 4 3558 



Exp 1 FRANCE R241 4 GERMANY R1D 4 70 

Exp 1 FRANCE R241 4 GERMANY R1E 4 115 




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ANNEX D: THEORETICAL ANALYSIS OF 

TRANSPORT CHAIN STRUCTURES 

IN DATABASES; CONSTRAINTS AND 

POSSIBILITIES



Introduction 

As already mentioned one of the major gaps in freight data availability is the freight ODmatrix. 

Within several projects (for instance INFOSTAT, CONERTO, MESUDEMO, INFREDAT) it 

has been identified that it is important to consider a transport chain structure in the freight 

database including transhipment of transport flows between modes and other characteristics. 

Before describing how the freight OD transport chain database should be constructed, the 

difficulties being encountered first are being discussed in a broader context in this chapter. This 

is a general theoretical analysis of transport chain data without looking at availability of sources. 

From the analysis in this chapter questions evolve that need to be answered within ETIS in the 

light of (intermodal) freight transport. First some basic concepts are being discussed after 

which the characteristics of transport chain are described. Eventually some words are being said 

on the level of detail and concluding remarks are given. 

Transport chains 

The transport Chain Concept 

In most databases a strict distinction is made between trade and transport databases. In trade 

databases the commodity flows are described from origin to destination and in transport 

databases the vehicle/vessel flows are described from origin to destination. Databases 

containing both definitions are very rare. Some sources of this type exist for a limited 

geographical area like for instance the trade data of the UK and France. 

Using the demandoriented philosophy we can estimate the future transport flows by estimating 

the future trade flows between regions/countries. An important requirement is that the economic 

trade relation of the commodity is maintained in the database describing the current transport 

flows. Since transport from the producing region/country to the consuming region/country often 

takes place with several modes it also has large advantages to have these possible mode changes 

in the database. Furthermore if a change of modes (or vehicles) occurs it is also interesting to 

have this place of transhipment described. A record structure that includes all these variables 

can be called a ‘transport chain structure’; the good is followed from the place of production via 

transhipment locations to the place of consumption where several modes can be used. 

This transport chain concept has the following characteristics: 

1. It is an element of response to the complex situation of transport 

2. It has been proven in the past to be a practicable concept 

3. It reconciles transport/economical approach 

4. It opens ways for more insights in the transport organisation 

5. It eliminates double counting 

6. It is compatible with current statistics 

Databases that do exist with transport chain information are mostly constructed from several 

sources where on some places estimates have to be made and inconsistencies can exist between 

databases of different countries. Also the level of detail of the transport chain is limited in 

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constructed databases; because of the estimates that have to be made the reliability of the data 

decreases with the increase of the level of detail. 

It therefore can be concluded that a reliable transport chain database with a high level of detail 

can only become available if these data are collected by forms like has been attempted in the 

MYSTIC project and is being tested in some national pilot exercises (France, Netherlands, 

Sweden) of which no final results have been obtained yet. In any other case the level of detail 

has to be reduced to come to a suitable reliability level. 

Restrictions on the Ideal Database 

The issue to be discussed is what the ideal transport chain information to be collected must 

consist. In this chapter the problems to collect the data will be ignored but the focus will only be 

on possibilities and problems of construction and handling (usage) of such a database. An ideal 

situation of the database to be constructed will be shown. The ideal transport database has the 

following general restrictions: 

· Technical restrictions 

· Understandability 

· Policy requirements 

There still are some technical restrictions. If the database becomes large it becomes more 

complex and time consuming to perform manipulations and extractions. If the record structure 

becomes very complex it will take even more effort and time to develop the necessary software. 

Besides this technical side of having a large and complex database we also have the side of 

interpretation of the results from this database. If one wants to know the movements between an 

origin and destination it will be very important to know whether one wants to know the 

movements of the commodities or the vehicles. The difficulty lies in the fact that now also the 

movements between transhipment points should be taken into account, making it more complex 

to make the right restrictions on the database. When having made an extraction from the 

database for a certain origin and destination, what can be seen? Are these the end points of the 

vehicle/vessel trip? Does it mean that the commodities transported have actually been switched 

between modes? Are these endpoints also the origin and destination of the commodities or for 

which part of the total flow does this hold? Much information should be provided to be able to 

make the right interpretation of the figures obtained from the database. 

Also aggregated information, which one is used to, like for instance a modal split figure, 

becomes more complex. How do we define the modalsplit for a country knowing the transport 

chain structure of the database? To be able to provide a modalsplit one needs to make 

assumptions. These assumptions have to be provided together with the figures to be able to 

make the right interpretations. 

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In case different methods are applied based on the different available data sources that can vary 

significantly among the different countries also some diffusion of interpretations can occur. On 

some elements of the database and for some geographical areas more estimation will be 

necessary than for others. It is important that these differences can easily be traced back from 

the results shown from the database to be able to make the right interpretation. 

These situations where assumptions have to be made to be able to generate the database and 

information from this database, and where data can not be shown without the necessary 

information on these assumptions, we could capture under the ‘understandability’ restriction. 

This restrictions requires that the database and the surrounding system is structured in such a 

way that information needed for the interpretation of the information extracted can be easily be 

made available. 

In the first place the transport chain freight database should be a simplification of reality in 

order to be able to understand how and where transport takes place. This means that many 

variables that could be observed have to be left out. Since each analysis of a problem requires 

another level of detail of the database. The policy requirement restriction which are in this case 

the TEN policies, determine which data should be included and which not. This is described in 

section 4.3. 

In the next paragraphs realistic possibilities for the format of the transport chain database will be 

described. 

The ideal Transport Chain 

To get a better view of which variables should be included in the transport chain database, an 

example is presented in figure D.1. 

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Figure D.1: 

Transportation of a computer 

manual 

cables 

mouse 

VAL 

A B C D E F G H I 

RoRo 

container 

In this figure we follow a computer from its place of production in point A to the consumer. The 

computer is transported by truck to point B where it is stuffed in a container together with other 

commodities. This container is transported by truck to point C where the truck goes on a ship to 

be transported to point D (Ro Ro). In point D the truck drives of the ship and goes to point E 

where the container is unloaded from the truck and stripped. The computer now is loaded on 

another truck to be transported to point F. In point F Value Added Logistics (VAL) takes place; 

the computer is put in another box together with additional features like cables, mouse and users 

manual. 

The computer is now transported to point G where it is transhipped on a train to be transported 

to point H. Finally it is transhipped again on a truck to be transported to point I. 

From a transport point of view the following problems relevant for the ETIS reference database 

development project can be identified in this fictional example: 

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1. Who do we define to be the consumer of the computer?; which type of transhipment to 

include? 

2. How to include simultaneous mode use in the database? 

3. How to include loading units (containers, swap bodies) in the database? 

4. Should there be a restriction on the number of links in a transport chain? 

Something that does not occur in the example is the case in which a container is empty. The 

following question can therefore be added: 

5. How to include empty loading units flows in the database? 

These problems will be discussed in the following paragraphs. 

A way to a “pragmatic” approach of transport chains 

Who do we define to be the Consumer of the Computer?; which Type of Transhipment to 

include? 

The first question to be answered is whom we will define to be the customer. Another and more 

general way of looking at this problem is which type of transhipment we should include. If it 

has been decided which transhipment locations to include this implicitly means that also the 

production and consumption locations are defined. 

Several types of transhipment can be identified: 

1. VAL (Value Added Logistics) 

2. Distribution 

3. Wholesale 

4. Change of mode 

5. Entrepot 

In our example VAL and change of mode can be identified. 

In the case of VAL a change of the characteristics of the commodities take place. The elements 

of this commodity that have to be considered the basic products find the end location at the 

VAL location while in fact they are transported in combination with the other basic products to 

another final location of consumption. In the computer example one of the basic products can 

even be given the same name as the final product. 

Distribution is another logistic phenomenon resulting in registration problems. Is the 

distribution centre the end point or just a transhipment location? Furthermore the transport from 

the distribution centre to the customers might be done with routing where different deliveries 

are combined into one trip, resulting in double countings in the database. 

For wholesale there is still another nuance. Here the commodity changes of owner, but is 

eventually transported to another location where it is consumed. 

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Solutions to these problems are outside the scope of this project and will therefore not be 

considered. 

The focus will be mainly on change of mode and where possible on entrepot. Transhipment as a 

result of change of mode is the best known principle. It is also the easiest to include in transport 

chain databases. Entrepot can also be seen as a transhipment action. The difference is the 

duration of the transhipment. It would be an added value to include entrepot in a transport chain 

structure to be able to preserve the relation between the real origin and the destination. 

Including change of mode and entrepot will give a much clearer view on the routes that the 

products follow. 

How to include Simultaneous Mode Use in the Database? 

In figure 5.1 it can be observed that a truck is transported from point C to point D by ship. The 

problem that arises in this case is that in fact two modes are being used: road and sea. According 

to the classical approach three possibilities exist: 

1. Register as road from B to E 

2. Register as road from B to C and as sea from C to D and in another record registration as sea 

from C to D and as road from D to E 

3. Independent registration of flow from B to C as road, C to D as sea and D to E as road 

If 1. is used we loose the information that part of the trip was over sea. If 2. is used we loose the 

information that the cargo was not transhipped but that the truck was also on the ship. In 3 like 

in number 1, the information what type of transhipment takes place is lost. So none of the 

possibilities can cope with this Ro Ro situation and the chain structure. These problems can be 

solved by using the transport chain concept. Another problem that is solved by using transport 

chains is that no double countings can occur when analysing the data. 

The same problems of course occur in case a train is transported by ship or a truck is transported 

by train. All these possible combinations of modes transported by other modes as ‘simultaneous 

mode use’ are defined. 

The registration of simultaneous mode use can be solved in several ways: 

1. Introduce another mode name for the combination of modes 

2. Introduce another variable that indicates whether simultaneous mode use has taken place. 

Possibility 1. has as disadvantage that in case we want to know what is actively transported by 

ship we have to aggregate several modes. 

With possibility 2. this problem is solved. Besides with possibility 2. a strict distinction can be 

made between the active mode and the passive mode that is transported. 

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For instance the variables ‘active mode’ and ‘passive mode’ can be used. Both can have all 

distinct modes as value. 

Table D.1 

Example of Simultaneous Road use Registration 

Active mode Passive mode Meaning 

Sea 

Sea 

Rail 

Rail 

Road 

Rail 

Road 

 

Commodity on a truck transported by a ship 

Commodity on a train transported by a ship 

Commodity on a truck transported by a train 

Commodity transported by train 

The concept of transport chain requires that definitions are clear and well used. 

It can be concluded that simultaneous mode use can be best implemented in a database by using 

a variable for the active mode and one for the passive mode in a transport chain structure. 

How to include loading units in the Database? 

In figure 5.1 it can be observed that between point B and E the computer is transported in a 

container. This container in its turn is transported first by truck then by Ro Ro on a ship and by 

truck again. 

Comparing this problem with the simultaneous mode use problem in the previous chapter it 

looks about the same. Also similar solutions are possible. It is difficult however to say whether 

a container must be defined as a mode, a commodity or something else. 

Since commodities are transported in a container it has some characteristics of a mode. 

However a container can not transport a commodity by itself. It has to be transported by a mode 

like a commodity. A container can also be seen as a package of products which makes it also a 

commodity. 

The most convenient solution is to define a distinct variable to indicate whether transport by 

container has taken place like in table 5.2. 

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Table D.2 

Example of Container Registration 

Mode Container Meaning 

Sea 

Road 

Yes 

No 

Commodity transported over sea in a container 

Commodity transported by road not in a container 

A possible extension to this method is to include also a type of container (large, other) on the 

place where otherwise a ‘yes’ would occur. Still a further extension would be to include in stead 

of a variable for container only, a variable for classification of loading units types. 

Should there be a Restriction on the Number of Links in a Transport Chain? 

Combining all the solutions in the previous paragraphs for the problems encountered in the 

computer example we see that for registering one flow, many variables are needed. If we take a 

look at the transportation of the computer from point A to point F we see that six locations have 

to be registered. One origin, four transhipment locations and a destination. Also five modes (one 

for each link) have to be registered together with the passive modes and container indications. 

This means 21 (= 3 x 5+ 6) variables are necessary for this transport chain. Furthermore 

variables are needed to describe the commodity, the weight and the value. Many transport flows 

will be less complex but there will also be more complex transport flows meaning that even 

more variables are needed. 

A partial solution to reduce the size of the database is to use a dynamic data structure in which 

for every transhipment place a variable is ‘made’ in the software. This means that sophisticated 

software has to be developed. The flow structure however still will be complex and difficult to 

comprehend. 

Another more pragmatic solution is to restrict the number of links. This also has disadvantages 

however, since it is important in case of intermodal transport to describe all transhipment that 

takes place. Furthermore reducing the number of transhipment points in the database also has 

some negative impacts on the reliability of the information to be extracted. To get more insight 

in this a look will be taken at the example in figure 5.4 

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Figure D.4: 

Flow with two Transhipments 

A B C D 

road sea road 

In this figure a transport flow can be seen from point A to point D with transhipment in point B 

and point C. In point B the commodities are transhipped from a truck to a ship and in point C 

from this ship to another truck. In case a record structure is considered where at least two 

transhipment points are possible, all information can be preserved. In case a restriction on the 

record structure is made, which makes it possible to include only one transhipment point part of 

the flow information has to be left out; not only one transhipment point has to be left out but 

also one mode. In the following table all possible registrations in this case are listed. 

Table D.3 

Possible Registrations of the Example in Figure D.4 with one transhipment 

variable 

Record Origin mode 1 transhipment mode 2 Destination 

1 

2 

3 

4 

A 

A 

A 

A 

Road 

Road 

Road 

Sea 

B 

B 

C 

C 

sea 

road 

road 

road 

D 

D 

D 

D 

The four possibilities are the result of two choices: 

1. which transhipment point to chose? 

2. which mode to chose? 

Without additional information on the transhipment points no good answer can be given on 

question 1. It can be recommended however to make one decision rule for the whole database. 

For example priority can be given to certain transhipment points. This means that in case this 

transhipment point is in the chain it will be chosen with higher priority than others. As a result, 

when analysing the total transhipment in for instance a port, the figures of the port with the 

highest priority will be the most complete. This also means that data for the other ports are not 

complete. Using other decision rules implicates that none of the transhipment points will have 

complete data. 

Several options can be chosen for the modes to be included. 

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1. modes at origin and destination 

2. modes before and after transhipment 

The records 2 and 3 result from the first decision rule and 1 and 4 from the second. Using the 

second decision rule has as advantage that it is made visible what type of transhipment has 

taken place. Again, due to the restriction of transhipment point loss of information occurs. 

What we learn from the problems described in this paragraph is that: 

· Including many variables results in a large database 

· restricting the number of transhipment points implies a reduction of information (smaller 

database) 

· restricting the number of transhipment points implies difficult interpretation of 

transhipment information 

· restricting the number of transhipment points implies that clear decision rules have to be 

chosen to avoid confusing results 

How to include empty loading unit flows in the database? 

To be able to analyse loading units movements it is also important to have information about the 

empty loading units transported. How should this type of information be included in the 

database? The question that arises is whether the empty loading units should be considered in 

the same way as the loaded loading units or as a commodity that is transported. 

Since in practice the transport of an empty loading unit is handled in the same way as the 

transport of a commodity it might be more convenient to do the same in the database; in practice 

one has to pay for this transport and a weight is registered for the loading unit. If the empty 

loading unit is treated as a commodity, a new code could be introduced for it. If this is done it 

can be treated in the same way as other commodities. A disadvantage of this method is that 

empty and loaded loading units must be traced in the database in different ways. It is also 

possible to introduce a variable indicating the number of containers transported. In this way the 

number of loaded and empty containers can be traced by combining it with the weight 

information (no weight means empty containers). 

In case it is considered in the same way as a loaded container, a special code can be defined for 

the loading unit variable for ‘empty container’. In case we use only one weight variable for the 

whole transport chain we register the weight of the transported commodities. As a result the 

weight of the transported ‘empty loading unit’ will be zero, which is a problem for both 

approaches. This means that if we want to know the amount of loading units transported we 

need to introduce another variable that registers for instance the weight of the loading units 

transported or the total weight of the commodities plus the weight of the container. 

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Another option is to include another variable with the number of empty loading units. However 

since the characteristics of this variable is so different it might be an option to register the empty 

loading units separate from the rest of the transport chain database. 

The theoretical ideal record structure 

In this section the ideal record structure of the transport chain freight database is described. The 

elements described before are included here: 

· origin 

· active mode 1 

· passive mode 1 

· loading unit 1 

· transhipment point1 





. . . 

. . . 

. . . 

. . . 

· active mode n 

· passive mode n 

· loading unit n 

· transhipment point n 

· destination 

· commodity 

· number of loading units 

· net weight 

· value 

It must be observed that this ideal record structure can handle all existing transport flows. So not 

only intermodal but also unimodal transport. 

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Table D.4: 

Examples for the use of the chain record structure 

Variable name 1) Direct road 2) RoRo, Truck on 

ferry 

· origin 









. . . 

. . . 

. . . 

. . . 

· active mode n 

· passive mode n 

· loading unit n 

· transhipment point n 

· destination 

· commodity 

· number of loading units 

· net weight 

· value 

n.r.=not relevant 

Cataluna 

Road 

 

 

 

 

 

 

 

 

 

 

 

Bayern 

Agriculture 

n.r. 

n.r. 

n.r. 

Bremen 

Road 

 

 

Hoek van Holland 

Sea 

Road 

 

Harwich 

Road 

 

 

 

West Midlands 

Manufactured prod. 

n.r. 

n.r. 

n.r. 

3) Sea–railroad, 

container 

USA 

Sea 

 

Container 

Rotterdam 

Rail 

 

Container 

Milano 

Road 

 

 

 

Lombardia 

Manufactured goods 

n.r. 

n.r. 

n.r. 

In table D.4 some examples are shown on how this structure can be used. In the first example 

one can see how direct road transport can be included. In this example Agricultural products are 

transported to Bayern by truck without any transhipment or use of loading unit. 

In the second example one can see how RoRo can be included. In this example manufactured 

products are transported from the region Bremen (D) to West Midlands (UK). The part between 

Hoek van Holland and Harwich is by ferry; the truck drives on the ship in Hoek van Holland 

and off again in Harwich and can be considered a passive mode on this link while it is active on 

the other links. 

In the final example we see transport from the USA to Lombardia (It) of manufactured 

products. The first part of the trip goes by ship to Rotterdam. The commodities are transported 

in a container, which goes from the ship on the train in Rotterdam. This train drives to Milano 

162 

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where the container is unloaded and stripped. Eventually the commodities are loaded on the 

truck to be brought to the final destination in Lombardia not far from the terminal. 

Conclusions 

Introducing the transport chain principle for data collection on a European scale can solve part 

of the existing information problems. Not all problems are easy to solve due to the complexity 

of the possible solutions and the fact that a lot of effort is needed to perform data handling. 

Experience with transport chain databases show that it is not easy to use transport chain 

information to its full extent and to make the right interpretations. Even if only one 

transhipment location is included new insights can be obtained resulting in a better 

understanding but also resulting in spending more effort to find the right meaning of the data. 

Expert database users or sophisticated data extraction software can help overcoming this 

difficulty. 

Despite all these difficulties transport chains must be seen as a powerful tool with which much 

more information will be available than now is the case. The fact that besides the economic 

relation also (a part of) the transport relation is preserved, makes it possible to obtain better 

understanding of the economic mechanisms behind transport. This in its turn will lead to better 

understanding of the implications of policy decisions. 

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ANNEX E: NEWCRONOS TABLES (RELEVANT 

FOR ETIS)



Domain 

heading 

aviation 

Inland 

waterways 

(inlandwww) 

Collection definition Collection heading table name table contents description 

F.V1. Transport 

measurement passengers 

F.V2. Transport 

measurement goods 

avmepass 

avmegood 

avpat40w 

avpat40e 

avpaieu 

avpaiwr 

avparkt 

avparksc 

avparkns 

avpat_ft 

avpatw 

avpat_f 

avpat_fr 

avgorkeu 

avgorkw 

1. Top 40 routes worldwide for each of the ICP airports in the TransEuropean Airport 

Network 

2. Top 40 routes within the EU for each of the ICP airports in the TransEuropean Airport 

Network 

3. International passengers at each of the ICP airports to all EU countries 

4. International passengers at each of the ICP airports to world regions 

5. Ranking of top 50 airports in EU and Switzerland in terms of international 

origin/destination passengers total passengers carried within EU and Switzerland 


origin/destination passengers scheduled passengers carried to/from rest of world 


origin/destination passengers nonscheduled passengers carried to/from rest of world 


origin/destination passengers total passengers carried to/from rest of world 


origin/destination passengers total passengers carried worldwide 

10. EU countries and Switzerland : to/from EEA and Switzerland origin/destination 

international passenger traffic 

11. EU countries and Switzerland : to/from world regions origin/destination international 

passenger traffic 


origin/destination freight total freight loaded/unloaded within EU and Switzerland 


origin/destination freight total freight loaded/unloaded worldwide 

C.I. Transport infrastructure iwinfras iwintwvc 01. Navigable inland waterways by carrying capacity of vessels 

C.II. Transport equipment 

iwequipt 

iweqnvc 

iweqpowe 

iweqpowa 

iweqycon 

iweqycoc 

iweqycoa 

01. Number of selfpropelled vessels, of dumb and pushed vessels by load capacity 

02. Power of selfpropelled vessels by load capacity 

03. Power of selfpropelled vessels, tugs and pushers by age 

04. Number of vessels by age 

05. Load capacity of selfpropelled vessels and dumb and pushed vessels 

06. Load capacity of selfpropelled vessels and dumb and pushed vessels by age 

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Domain 

heading 

maritime 


C.III. Enterprises 

C.V3. Transport 


(Council Directive 

80/1119/EEC) 

iweconom 

iwmegood 

E. I. Passenger transport mamepa 

E. II. Freight mamego 

iwecentv 

iwecentc 

iwecnemp 

iwecexpe 

01. Number of inland waterway transport enterprises by number of vessels 

02. Carrying capacity of the enterprises vessels by number of vessels in enterprises 

03. Employment in inland waterways transport enterprises by number of vessels in 

enterprises 

05. Investment and maintenance expediture in vessels and infrastructure 

iwgoildg 01. International annual transport by link with loading country and by group of goods 

(1000 T, Mio Tkm) 

iwgoiulg 02. International annual transport by link with unloading country and by group of goods 

(1000 T, Mio Tkm) 

iwgoildv 03. International annual transport by link with loading country and vessel nationality (1000 

T, Mio Tkm) 

iwgoiulv 04. International annual transport by link with unloading country and vessel nationality 

(1000 T, Mio Tkm) 

iwgoild 05. International monthly transport by link with loading country (1000 T) 

iwgoiul 06. International monthly transport by link with unloading country (1000 T) 

iwgondcg 

iwgonvn 

07. National annual transport by distance class and group of goods (1000 T, Mio Tkm) 

08. National annual transport by vessel nationality (1000 T, Mio Tkm) 

iwgon 09. National monthly transport (1000 T) 

iwgorldg 

iwgorulg 

iwgotg 

iwgotvn 

iwgoalkv 

mamepaaa 

mamepaqm 

mamepaqc 

mamegoaa 

10. National annual transport by loading regions and by group of goods (T) 

11. National annual transport by unloading regions and by group of goods (T) 

12. Annual transit transport by country and group of goods (1000 T, Mio Tkm) 

13. Annual transit transport by country and vessel nationality (1000 T, Mio Tkm) 

14. Total quarterly transport by kind of vessel (1000 T, Mio Tkm) 

E.I.1. Passenger transport Total annual figures for all ports (main and small from 2000) of 

reporting country 

E.I.2. Passenger transport Quarterly figures for main ports of reporting countries (ports 

reporting over 200.000 passengers per year) 

E.I.3. Passenger transport Quarterly figures for main ports of each reporting country 

E.II.1. Freight Total annual seaborne transport for all ports (main and small from 2000) of 

reporting countries 

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Domain 

heading 

pipeline 

rail 


mamegoam 

mamegoqm 

mamegoac 

E.II.2. Freight Annual seaborne transport for main ports of reporting countries (ports 

handling over 1 mio tonnes per year) 

E.II.3. Freight Quarterly seaborne transport for main ports of reporting countries (ports 

handling over 1 mio tonnes per year) 

E.II.4. Freight Annual seaborne transport for main ports for each reporting country 

mamegoqc E.II.5. Freight Quarterly seaborne transport for main ports for each reporting country 

(ports handling over 1 mio tonnes per year) 

E. III. Vessel traffic mameve mameveqm E.III.1. Vessel traffic 

D.I. Transport Infrastructure 

D.III. Enterprises, economic 

performances and 

employment 

D.V. Transport measurement 

goods 

A.I. Infrastructure 

A.II. Transport equipment 

plinfras 

pleconom 

plmealtp 

plinlgpl 

plincppl 

plecnent 

plecnemp 

plecexpe 

02. Transport within the national territory by type of transport operations and products 

(Mio Tkm) 

01. Lenght of pipelines operated 

02. Carrying capacity of pipelines operated 

01. Oil pipeline enterprises 

02. Employment in oil pipeline enterprises 

03. Investment and maintenance in oil pipeline infrastructure 

plmegood plmealpd 01. Transport within the national territory by type of transport operations and products 

(1000 t) 

rainlgt 

01. Length of tracks 

rainfras 

raequipt 

rainlgnt 

rainlggt 

rainlgpg 

rainlgtc 

raeqnlsp 

raeqnrsp 

raeqnltp 

raeqnrtp 

raeqpvtv 

raeqpacv 

raeqpvtc 

02. Length of lines, by number of tracks 

03. Length of lines, by track gauge 

04. Length of lines, by nature of transport 

05. Length of electrified lines, by type of current 

01. Number of locomotives, by source of power 

02. Number of railcars, by source of power 

03. Number of locomotives, by tractive power 

04. Number of railcars, by tractive power 

05. Passenger railway vehicles, by type of vehicle 

06. Passenger railway vehicles, by category of vehicle 

07. Capacity of passenger railway vehicles, by type of vehicle 

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Domain 

heading 


A.III. Enterprises, economic 


employment 

A.IV. Traffic 

A.V1. Transport 


A.V3. Transport 


(detailed data from EC 

Directive and regulation from 

1982 onwards 

raeconom 

ratrafic 

ramepass 

ramegood 

raeqpacc 

raeqnvan 

raeqnwan 

raeqnwal 

raecnent 

raecnemp 

raecnems 

raecexpe 

ratrtvsp 

ratrvsp 

ratrhvsp 

ratrhvtv 

ratrsekm 

ratrsetk 

rapancin 

rapancit 

rapancim 

rapancic 

rapaiar 

rapaidp 

rapaal 

rapaap 

08. Capacity of passenger railway vehicles, by category of seats or berths 

09. Number of vans 

10. Number of wagons, by status of enterprise 

11. Load capacity of wagons, by status of enterprise 

01. Principal railway enterprises 

02. Employment in principal railway enterprises, by type of activity 

03. Employment in principal railway enterprises by sex 

04. Nature of expenditure in principal railway enterprises, by type of expenditure 

01. Trainmovements, by type of vehicle and source of power 

02. Tractive vehicle movements, by type of vehicle and source of power 

03. Hauled vehicle movements, by source of power 

04. Hauled vehiclekilometres, by type of hauled vehicle 

05. Hauled vehicles movements seat kilometres offered 

06. Hauled vehicles movements Tkm offered 

01A. Passenger transport by national/international transport (1000 passengers) 

01B. Passenger transport by class (1000 passengers) 

02A. Passenger transport by national/international transport (Mio pkm) 

02B. Passenger transport by class (Mio pkm) 

03. International passenger transport by country of arrival in the reporting country and by 

class 

04. International passenger transport by country of departure from reporting country and 

by class 

05A. Accompagnied passenger car transport, by type of transport (passenger cars) 

05B. Accompagnied passenger car transport, by type of transport (number of passengers) 

ragoildg 01.International annual transport by link with loading country and by group of goods (1000 

T, Mio Tkm) 

ragoiulg 02. International annual transport by link with unloading country and by group of goods 

(1000 T, Mio Tkm) 

ragoild 03. International monthly transport by link with loading country (1000 T) 

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Domain 

heading 

road 


A.VII. Accidents 

B.I. Infrastructure 

B.II1. Transport equipement 

Stock of vehicles 

raaccidt 

roinfras 

roeqstok 

ragoiul 04. International monthly transport by link with unloading country (1000 T) 

ragondcg 

05. National annual transport by distance class and group of goods (1000 T, Mio Tkm) 

ragon 06. National monthly transport (1000 T) 

ragorldg 07. National annual transport by loading regions and by group of goods (1000 T) 

ragorulg 08. National annual transport by unloading regions and by group of goods (1000 T) 

ragotg 

ragotlk 

ragoalrr 

raacoa 

raacoat 

raacota 

raacot 

roinlgmw 

roinlger 

roinlgor 

rostmopd 

rostmotr 

rostpacr 

rostpaca 

rostpaco 

rostbus 

rostbuss 

rostbusa 

rostbuso 

rosttram 

rostlowc 

rostlown 

09. Transit annual transport by group of goods (1000 T, Mio Tkm) 

10. Transit annual transport by link (1000 T, Mio Tkm) 

11. National, international and transit annual transport by container and road/rail (number, 

1000 T) 

01. Number of victims by type of injury 

02. Number of victims by origin of accident 

03. Accidents by type of accident 

04. Dangerous goods transport accidents 

1. Length of motorways 

2. Length of eroads 

3. Length of other roads by category of roads 

1. Mopeds 

2. Motorcycles, by power of vehicles 

3A. Passenger cars, by motor energy 

3B. Passenger cars, by age 

3C. Passenger cars, by alternative motor energy and by power of vehicles 

4A. Motor coaches, buses and trolley buses, by motor energy 

4B. Motor coaches, buses and trolley buses, by number of seats 

4C. Motor coaches, buses and trolley buses, by age class 

4D. Motor coaches, buses and trolley buses, by alternative motor energy 

5. Trams 

6A. Lorries, by load capacity (1000t) 

6B. Lorries, by load capacity (number) 

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Domain 

heading 


B.II2. Transport equipement 

New registration of vehicles 

roeqregi 

rostloec 

rostloeo 

rostvehv 

rostvehc 

rostveha 

roststct 

roststcn 

rosttct 

rosttcn 

roremotr 

rorevehe 

roreveho 

rorebus 

roreloc 

rorevkt 

rorevkn 

rorestct 

rorestcn 

roretct 

roretcn 

rorellc 

rorelme 

6C. Lorries, by type of motor energy and load capacity 

6D. Lorries, by type of alternative motor energy and load capacity 

7A. Load capacity of lorries, semitrailers and trailers, by kind of transport (1000t) 

7B. Lorries, road tractors, semitrailers and trailers, by kind of transport (number) 

7C. Lorries and road tractors, by age (number) 

8A. Semitrailers, by load capacity (1000t) 

8B. Semitrailers, by load capacity (number) 

9A. Trailers, by load capacity (1000t) 

9B. Trailers, by load capacity (number) 

1. New registrations of motorcycles 

2A. New registrations of passenger cars, motor coaches, buses and trolley buses, by type 

of vehicle and motor energy 

2B. New registrations of passenger cars, motor coaches, buses and trolley buses, by type 

of vehicle and alternative motor energy 

3. New registrations of motor coaches, buses and trolley buses, by seat capacity 

4. New registrations of lorries, by load capacity (1000T) 

5A. New registrations of lorries, semitrailers and trailers, by kind of transport and load 

capacity (1000T) 

5B. New registrations of lorries, road tractors, semitrailers and trailers, by kind of transport 

(number) 

6A. New registrations of semitrailers, by load capacity (1000T) 

6B. New registrations of semitrailers, by load capacity (number) 

7A. New registrations of trailers, by load capacity (1000t) 

7B. New registrations of trailers, by load capacity (number) 

8A. New registrations of lorries, by load capacity (number) 

8B. New registrations of lorries, by motor energy and load capacity (+/ 1500kg) (number) 

rorelmo 8C. New registrations of lorries, by alternative motor energy and load capacity (+/ 1500 

kg) (number) 

B.III. Enterprises, economic roeconom roecente 1A. Goods transport enterprises, by number of employees 

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Domain 

heading 



employment 

roecentv 

roecnemp 

roecnems 

roecexpe 

1B. Goods transport enterprises, by number of vehicles 

2A. Employment in goods transport enterprises 

2B. Employment in goods transport enterprises, by sex 

3. Investment and maintenance expenditure, by nature of expenditure 

B.IV. Traffic rotrafic rotrmveh 1. Motor vehicle movements on national territory, by vehicles registration 

B.V1. Transport 


B.V3. Transport 


romepass ropantv 1. Passenger transport on national territory, by type of vehicles registered in the reporting 

country 

rogoildt 

01. International annual transport by link with loading country, by group of goods and type 

of carriage (1000 T) 

rogoildm 02. International annual transport by link with loading country by type of carriage (Mio 

Tkm) 

rogoiult 

03. International annual transport by link with unloading country, by group of goods and 

type of carriage (1000 T) 

rogoiulm 04. International annual transport by link with unloading country by type of carriage (Mio 

Tkm) 

rogoild 05. International quarterly transport by link with loading country and type of carriage (1000 

T, Mio Tkm) 

romegood 

rogoiul 

06. International quarterly transport by link with unloading country and type of carriage 

(1000 T, Mio Tkm) 

rogong 

07. National annual transport by group of goods and type of carriage (1000 T, Mio Tkm) 

rogondct 08. National annual transport by distance class, type of carriage and group of goods (1000 

T) 

rogondcm 09. National annual transport by distance class and type of carriage (Mio Tkm) 

rogorldg 10. National annual transport by regions of loading and by group of goods (1000 T) 

rogorulg 11. National annual transport by regions of unloading and by group of goods (1000 T) 

rogon 

12. National quarterly transport by type of carriage (1000 T, Mio Tkm) 

rogogbg2 13. Cross trade transport annual by link, group of goods and type of carriage (1000 T) 

rogogbg1 

14. Cross trade transport quarterly by type of carriage (1000T, Mio Tkm) 

B.V4. Transport romecabo rocahaul Cabotage by hauliers from each reporting country 

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Domain 

heading 

tranlink 


measurement goods 

cabotage 

B.VII. Accidents 

regitran Regional transport 

statistics 

roaccidt 

regitran 

rocacoun 

roacka 

roacmt 

roacas 

roacta 

reavgocc 

reavgf98 

reavgu98 

reavpacc 

reavpf98 

reavpu98 

remagocc 

remagf98 

remagu98 

remapacc 

remapf98 

remapu98 

reroacci 

reroaccc 

rerotruc 

reroequi 

reroeqcc 

reinlinf 

reinlicc 

Cabotage transport by country in which cabotage takes place 

1. Injury accidents during the year, by kind of area 

2. Casualties in injury accidents (killed and injured), by means of transport 

3. Casualties in injury accidents (killed and injured), by age and sex 

4. Casualties in injury accidents (killed and injured), by means of transport and by age 

(detailled up to 20 years) 

Air transport of freight Candidate Countries 

Air transport of freight from 1998 onwards (new methodology) 

Air transport of freight until 1998 (old methodology) 

Air transport of passengers Candidate Countries 

Air transport of passengers from 1998 onwards (new methodology) 

Air transport of passengers until 1998 (old methodology) 

Maritime transport of freight Candidate Countries 

Maritime transport of freight from 1998 onwards (new methodology) 

Maritime transport of freight until 1998 (old methodology) 

Maritime transport of passengers Candidate Countries 

Maritime transport of passengers from 1998 onwards (new methodology) 

Maritime transport of passengers until 1998 (old methodology) 

Road safety 

Road safety Candidate Countries 

Transport data for medtrans med_avia Aviation statistics 

Road transport of goods Journeys made by vehicles 

Road transport, stock of vehicles by category 

Road transport, stock of vehicles by category Candidate Countries 

Road, rail and navigable inland waterway networks 

Road, rail and waterway networks Candidate Countries 

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Domain 

heading 

tranover 


Mediterranean countries 

Ad hoc tables used in 

Eurostat Yearbook 

Transport Euroindicators 

(EUROZONE and EU15) 

tran_eyb 

transif 

treurind 

med_mari 

med_pipe 

med_rail 

med_roac 

med_road 

5a1fr 

5a1fv 

pass_tr 

good_tr 

5h6st 

5a1fo 

5a1fq 

5a1fk 

5h5st 

5h4st 

5a1fu 

5h7st 

5a1fh 

infra 

length 

5s1tr 

5a1fp 

sif79909 

Maritime statistics 

Pipelines 

Rail statistics 

Road accidents 

Road infrastructure and equipment 

Air transport of goods. 1 000 t 

Air transport of passengers. Millions 

Car, bus and rail transport of passengers (Mio PKM) 

Goods transport by road, by rail, by inland waterways and by oil pipelines (Mio TKM) 

Goods transport by road. Million tonnekm 

Inland goods transport. 1 000 million tonnekm. EEA and Switzerland 

Million tonnekm Sea transport of goods. Million t 

Passenger cars per 1 000 inhabitants 

Passenger cars per 1 000 inhabitants 

Passenger cars. 1 000s 

Passenger transport. 1 000 million passengerkm. EEA and Switzerland 

Persons killed in road accidents 

Total inland transport per mode: EEA and Switzerland 

Total length of motorways and railways lines in km 

Total length of motorways and railways lines in km (EU15 and Candidate countries) 

Transport growth. EU15(I90=100) 

Worldwide commercial space launches 

Statistics in focus 09/1999 : Long distance passenger travel 

sif70202 Trends in road freight transport 19901999 

treugoqt 1. International and national transport of goods Quarterly (1000 T) 

treugoat 2. International and national transport of goods Annual (1000 T) 

treupaan 3. International and national transport of passengers by plane and boat Annual (1000) 

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ANNEX F: 

OVERVIEW OF DATA SOURCES 

DATA COLLECTION UP TO TEST 

OF METHODOLOGY



Transnational datasources 

Source Description Status 

Eurostat COMEXT trade by mode (quarterly data 1997 – 2002) 

NewCronos 

International road O/D matrix on regional level (estimate) 

Partly received 

Received 

Received 

UN UN trade by commodity on country level (value and weight (partly)) Received 

UNECE UNECE common database; quarterly trade data Eastern Europe In discussion 

WTO Trade on country level In discussion 

UIRR Intermodal statistics on country level Received 

ICF Intermodal statistics on country level Received 

UIC Rail transport statistics (intermodal) In discussion 

Lloyds Data about movements of ships between ports (costs 28.000) In negotiation 

Piers Detailed maritime (container) data with route information In negotiation 

CAFT Cross Alpine Freight Transport Received 

MDS Data about bulk and unitised transport on country level Received 

ISL Data about containerised transport flows in Europe Not available 

National datasources Western Europe 


SITRAM (FR) National and int. regional transport data (incl. transhipment) In discussion 

NBB (B) International transport flows via Belgium ports Received 

BRAIL (B) National and int. Regional transport data – rail Received 

NIS (B) National and int. Regional transport data – road and inl.ww Received 

Statec (L) No contact 

CBS (NL) National and int. Regional transport data by mode and by comm. Received 

SBA (DE) National and int. Regional transport data – excl. Road Received 

KBA (DE) National and int. Regional transport data – Road transport Ordered 

SLA Hamburg (DE) International transport flows via Hamburg Received 

SLA Bremen (DE) International transport flows via Bremen Received 

ISTAT (IT) 

Regional trade data by transport mode and commodity 

Maritime data by port 

Received 

Received 

DFT (UK) Domestic road transport / rail data / maritime data Received 

MDS (UK) Regional data UK by port, by mode and by commodity Received 

CSO (IE) Regional road transport, aggregate maritime data Received 

DST (DK) Regional road transport, aggregate data other modes Received 

Greece Statistics Aggregate transport data Received 

INE (PT) Regional road transport, aggregate data other modes Received 

MoT (ES) Regional information road transport Received 

AEAT (ES) Regional trade by mode, by comm. And by country Received 

SSB (NO) Trade by mode of transport, domestic road (regional) Received 

SCB (SE) Regional road transport –nat.and int.; aggr data other modes Received 

SIKA (SE) Rail data Not available 

Statistics Finland Domestic and international road transport / rail / detailed maritime Partly received 

BFS (CH) National and international regional road transport data (1998) 

Aggregate data other modes 

Received 

In discussion 

Satistics Austria National and int. regional transport by mode and comm. Received 

National datasources applicant countries 

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PMI/MoI (PL) National and int. Transport by mode and by comm. Received 

Viapont (CZ) Aggregate data by mode and comm. For int. transport Received 

MoTC (CZ) In discussion 

Viapont (SK) Aggregate tables road, rail, inland waterways Received 

Slovak statistics Trade and transport data Received 

KTI (HU) International regional transport by commodity for road and rail Received 

KSH (HU) In discussion 

TRI (EE) Aggregate data Received 

Estonia statistics In discussion 

CSB (LV) In discussion 

Mot / STD (LT) In discussion 

Slovenia statistics Aggregate data by mode and by commodity In discussion 

Malta statistics No reaction 

Cyprus statistics No reaction 

180 

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