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ETISplus Road Data Workshop TRANS-TOOLS : Road data gaps

Brussels, BELGIUM 13 May 2011. ETISplus Road Data Workshop TRANS-TOOLS : Road data gaps. ECKHARD SZIMBA (KIT-IWW), NICOLÁS IBAÑEZ-RIVAS (JRC-IPTS). Agenda. Brief overview of the TRANS-TOOLS model General features Structure and modules Road data gaps and challenges Conclusions.

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ETISplus Road Data Workshop TRANS-TOOLS : Road data gaps

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  1. Brussels, BELGIUM 13 May 2011 ETISplus Road Data Workshop TRANS-TOOLS: Road data gaps ECKHARD SZIMBA (KIT-IWW), NICOLÁS IBAÑEZ-RIVAS (JRC-IPTS)

  2. Agenda • Brief overview of the TRANS-TOOLS model • General features • Structure and modules • Road data gaps and challenges • Conclusions

  3. TRANS-TOOLS: General Features (I) • TOOLS for TRansport forecasting ANdScenario testing • Transport demand and assessment model developed on behalf of the European Commission by European project consortia: • 2005-08: TRANS-TOOLS project (6th FP) • 2008-09: TEN Connect 1 project (MOVE B1) • 2010-11: TEN Connect 2 project (MOVE B1)current version: recalibration, more efficient computation • 2011-13: TRANS-TOOLS v3 (7th FP) • Other recent EU projects related to TRANS-TOOLS developments: • iTREN, WORLDNET, ETISplus • IBU-Øresund, Baltic Transport Outlook

  4. TRANS-TOOLS: General Features (II) • Funding and coordination: • DG TREN/ MOVE (A.3/B.1) • Joint Research Centre (JRC), IPTS, Seville • Aim: Consolidation of European transport demand and assessment models and integration of the available European models on a common platform • ›Proprietary‹ and Intellectual propertyright free (IPR)reference model by the European Commission for the assessment of transport policy measures • Analogy to models of other sectors: • PRIMES/ POLES – Energy • TREMOVE/ RAINS (GAINS) – Environment

  5. TRANS-TOOLS: General Features (III) • Main model for transport policy analysis in the EC • Consistency to the EU Transport Policy Information System ETIS • Geographical scope: • 42 countries • Circa 300 NUTS-2 regions(trade/ logistics/ freight model, regional economic model) • Circa 1,300 NUTS-3 regions (passenger transport model) • Modes of transport: • Passenger transport: road/ rail/ air • Freight transport: road/ rail/ inland waterways/ maritime

  6. TRANS-TOOLS v1: Modules and structure Input data: Socio-economicdata, Network models

  7. TRANS-TOOLS v1: Modules and structure Input data: Socio-economicdata, Network models Passenger transport

  8. TRANS-TOOLS v1: Modules and structure Input data: Socio-economicdata, Network models Passenger transport Freighttransport andlogistics

  9. TRANS-TOOLS v1: Modules and structure Input data: Socio-economicdata, Network models Passenger transport Freighttransport andlogistics Regional economic model

  10. TRANS-TOOLS v1: Modules and structure Input data: Socio-economicdata, Network models Passenger transport Freighttransport andlogistics Regional economic model Traffic assignment

  11. TRANS-TOOLS v1: Modules and structure Input data: Socio-economicdata, Network models Passenger transport Freighttransport andlogistics Regional economic model Traffic assignment Externaleffects

  12. TRANS-TOOLS v1: Modules and structure Input data: Socio-economicdata, Network models Passenger transport Freighttransport andlogistics Regional economic model Traffic assignment Externaleffects

  13. TRANS-TOOLS v1: Feedback Loop Input data: Socio-economicdata, Network models Passenger transport Freighttransport andlogistics Regional economic model Traffic assignment Externaleffects

  14. Road Data Gaps and Challenges – Network, impedance and demand data • Traffic count data • Scope of availability of traffic count data • Differentiation of traffic count data by type of vehicle(currently AADT without differentiation) • Differentiation by time slice (peak/ off-peak) • Long-distance coach services • Level-of-Service (frequencies, travel times, costs) • Alignment of routes • Demand • Demand data at the level of origin/ destination (O/D) relations • Data on freight terminals (logistic centres/ distribution centres) • Data on vehicle use, e.g. load factors, empty runs, occupancy rates • Data for neighbouring countries of the EU

  15. Availability of traffic count data

  16. Example of availability of traffic count data • Detail of road counts for Andalusia: • Comparison with publicly available count data • Percentage of HGV • Split of the total count across time bands(peak, off-peak)

  17. Conclusions • Most prominent datagapsare ‘observed‘ demanddata • Level of O/D relations • Level ofnetworklinks • Level offreightterminals • Importanceoftrafficcountdata • Differentiation by type ofvehicle • Transparencyregarding original datasourcesandmethodologyofimplementation • Long-distancecoachmarket, a markethighlydependent on country-specificregulatorysettings • => well-elaboratedlevel-of-serviceanalysesrequiredto model long-distancecoachservicesaccurately • Supporting TRANS-TOOLS‘ capabilitiesto cover transportflowsbetween EU andneighbouringcountries

  18. Thank you for your attention.  Eckhard Szimba, Ph.D. Karlsruhe Institute of Technology (KIT) Institute for Economic Policy Research (IWW) Network Economics szimba@kit.edu Nicolás Ibañez-Rivas, Ph.D. European Commission, JRC Institute for Prospective Technological Studies (IPTS) Nicolas.IBANEZ-RIVAS@ec.europa.eu

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