European Strategic Traffic Forecasts and possible contribution for the TEM Master

Plan Bratislava 9 February 2004

Benno BultinkDG Public Works, the Netherlandsb.j.a.bultink@don.rws.minvenw.nl

Ming Chen & Adrian VilcanNEA Transport research and training

mch@nea.nl / avi@nea.nl

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Purpose:

»Making Dutch know-how and experience available to CEC

»Improving the efficiency of investments in central European infrastructure by applying this knowledge

»Improving market opportunities to commercial partners on both sides

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Partner for Roads, window:Roads and Regional Development

To strengthen the link between road projects and regional development by assisting planning authorities to make this link visible.

Put this into practice!

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Ingredients of presentation:

» TEN-STAC: objectives, approach» TEN-STAC: Phase 1 results» NEAC forecasting model» Possible integration TEN-STAC – TEM Master Plan

– Main guidelines / approach– Data requirements– Possible products

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The TEN-STAC project – objectives

TEN-STAC project objectives:

» traffic forecasts for 2020, including traffic assignment, estimate of international traffic load on the network and socio-economic and environmental impacts according to different scenarios,

» a review of national transport infrastructure plans, and macroeconomic analysis to estimate potential public financing in transport infrastructure until 2020,

» detailed analyses of 25 international corridors comprising screening of bottlenecks and environmental risks, and guidelines to select projects of high European interest within corridors,

» broad financial plans for selected major projects

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A DG-TREN project dedicated to the Revision of the TEN

Phase 1: January – July 2003

» Forecasts for 2020 freight and passengers» All modes» Assignment on the networks» Identify the main transport axes in Europe» Input for revision TEN

Final D3 report available at: http:\\www.nea.nl\ten-stac

Phase 2: August 2003 – March 2004

» Detailed analyses of the High Priority Projects of European interest: determine indicators per project / sub-section

» Assessment of the projects» Financial analyses

The TEN-STAC project

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The TEN-STAC project – main approach

Traffic flows on European networks, international, domestic, intra-regional – by mode, in vehicles

Demand flows expressed by total generated/attracted flows:per NUTS 2 region for core study area, mode, transport chain (combination of modes), market segment, type of flow (international, domestic, intra regional), distance classin tonnes and vehicles (light and heavy trucks for road transport)

Emissions:- CO2, CO, NOx, PM10Accessibility by NUTS2 zone and mode

SCENES Freight modelling systemCost function parameters

NEAC modelling system: freightdemand modelling: generation / distribution, modal splitmultimodality and role of portsassignment modellingGISCO European networks: rail & road Level-Of-Service freight

VACLAV environmental impactsocial & cohesion impact

VACLAV: passengerassignment modelling GISCO European networks: rail & road Level-Of-Service freight

COMMON MODELLING PLATFORM TEN-STAC

EUFRANET Rail Freight modelling system-Accompanying Measures rail freight

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Forecasts 2020, three scenarios

Basic policy actions and accompanying measures (AM) of TEN policy packages

TEN policies SCENARIOS Baseline socio-economic trends Basic policy

actions: liberalisation and harmonisation

Accompanying measures for TEN package I

Accompanying measures for TEN package II

TEN-basic

TEN-policy package I

TEN-policy package II

TREND+

EUROPEAN

EUROPEAN+

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Growth international freight transport demand flows

International freight transport demand flows, TREND+ scenario

2020 (base year 2000), Mio tonnes (crude oil excluded)

Origin\Destination EU15+2 CEEC 12 Rest Europe Rest World

EU15+2 1,785 (1,130) 130 (59) 98 (41) 570 (288) CEEC 12 238 (125) 110 (46) 55 (23) 55 (21)

Rest Europe 329 (121) 212 (81) 74 (27) -

Rest World* 1146 (742) 63 (26) - -

-

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Growth international freight transport demand flows

Growth international freight transport demand flows, 2020/2000 - TREND+ scenario

Origin\Destination EU15+2 CEEC 12 Rest Europe Rest World

EU15+2 1.58 2.20 2.39 1.98 CEEC 12 1.90 2.39 2.39 2.62 Rest Europe 2.72 2.62 2.74 - Rest World* 1.54 2.42 - -

-

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Road freight flows total interregional, TREND+ scenario

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Changes Road Freight2000 – 2020 TREND+ scenario

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Rail freight international, TREND+ scenario

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changes rail freight 2000 – 2020 TREND+ scenario

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Inland Waterways flows, TREND+ scenario (KTON)

KOBENHAVN

RIGA

DUBLIN

LONDON

AMSTERDAM

BRUXELLES

BERLIN

PRAHA

VILNIUS

MINSK

PARIS

VADUZBERN

LUXEMBOURG

LJUBLJANA

BUDAPEST

BRATISLAVA

KISHINEV

MONACO

ROMA

SARAJEVO

BEOGRAD

SOFIYA

WARSZAWA

SKOPJE

BUCHAREST

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Sea motorways TREND+ 2020 excluding crude oil (*mio tonnes)

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Changes in traffic performance 2000-2020

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Change in freight centrality, EUROPEAN scenario versus base year 2000 (NUTS2, indexed)

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Change in NOx emissions road, EUROPEAN scenario versus base year 2000 (indexed )

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Demand Supply Capacity of the infrastructure

Local traffic volume

Long distance traffic volume

RESULT • Transport time • Congestions ?

Part i of corridor j

LOS/ Bottleneck ?

Road link analysis and information needs

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Economic corridors using the link Rotterdam - Antwerp

1 m illion t.

to ta l 18 m illion tonnes

R

A

1 m illion t.

2 m illion t.

2 m illion t.

2 m illion t.

2 m illion t.

5 m illion t.

3 m illion t.

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Flow types in corridor analysis

1 23

4 56

7 89 10

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Transport chains

Origin regionof th e

com m odity

D est inat ionregionof th e

com m odity

M odeat origin

M ode atdest inat ion

M ode in

betweentransh ipm ent

Origin regionof th e

com m odity

D est inat ionregionof th e

com m odityM odeat origin

M odeat dest inat ion

Transh ipm en t

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Construction of NEAC database

1. identification of trade flows2. identification of transhipment sites3. regionalisation of country-to-country-total4. extension with domestic transport

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Top-down approach

nationalregional data

transhipmentdata

direct transhipment

Belgianports

Germanports

trade flows

(direct) Belgianports

Germanports

Dutchports

Dutchports

COUNTRYTO

COUNTRY

REGIONTO

REGION

direct

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NEAC regions Europe

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Russian Federation (tonnes x 1000)

> 30001500 - 3000

600 - 1500300 - 600150 - 300

60 - 15030 - 60

< 30

(C) Copyright NEA 1999

rpc9

8.w

or

Domestic production by region of the Russian Federation, 1998rail, all routes, containerised

NEAC region Russian Federation

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trade m ode l andpassenger m ode l

scenarioeconom ic anddem ograph ic

scenarioin frastruc tu re andtransport po licy

netw ork

m oda l sp litm ode l

ass ignm entm ode l

fo recast flow s(O /D m atrices )

base yea r

m oda l sp lit(O /D m atrices)

tra fficon links

CLASSICAL MODEL APPROACH

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Trade forecast

The computation of forecast of international trade flows

)AA(*)

PP(*T=T 3

bj

pj2

bi

pib

ijpij

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Modal-split model

Segmentation of transportmarkets

• commodity group• distance• total tonnage

Relative change of costs + timeof modes

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trade m ode l andpassenger m ode l

scenarioeconom ic anddem ograph ic

scenarioin frastruc tu re andtransport po licy

netw ork

m oda l sp litm ode l

ass ignm entm ode l

fo recast flow s(O /D m atrices )

base yea r

m oda l sp lit(O /D m atrices)

tra fficon links

CLASSICAL MODEL APPROACH

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More information on NEAC:www.nea.nl/neac

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Transport demand determinants - freight

GDP – Gross Domestic Product (per sector and per expenditure) – byNUTS2 region for the core area:• GDP/head• Agriculture,• Mining and quarrying,• Basic metal,• Construction,• Chemicals, petroleum,• Metal products,• Food consumption, • Residential construction,• Private final consumption.

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Transport demand determinants - passenger

Exogeneous variables for passenger demand generation, distributionand modal split: • Motorisation• Population by sex and age classes• Employment by sectors• GVA by sectors • Accommodation offer for leisure in relation with tourism trip purpose• GDP

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Transport supply modeling variables – passenger & freight

Transport times and costs: • absolute values base year 2000 and changes per mode for 2020• by transport mode based on:

- link/node attributes of GISCO networks as length, speed, capacity, resistance (impedance), link type, speed-flow function (road)

- cost functions- route choice as from assignment modelling- specific model variables for rail - EUFRANET

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Possible approach for linking TEN-STAC and TEM Master Plan

- Get a consistent zoning system TEN-STAC – TEM. The description of the zoning system has been provided to the TEM project.

- Identification of the TEM countries and other countries to be considered at the similar level of detail as most countries in the TEN-STAC study (NUTS2).

- The supplementary data needed for the countries would cover: socio-economic trends, observed trade / transport and infrastructure data.

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Possible approach for linking TEN-STAC and TEM Master Plan

- Analyse different options after the Bratislava meeting.

- One possible option is to build one scenario for horizon 2020 to give an example on how the linking process can be developed – focus on the road transport.

- Integration at a certain level of detail of 2 TEM countries in the scenario could be considered.

- socio-economic impact in terms of accessibility and environmental impact at country / regional level could be also considered as output of the scenario.