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Cities facing data collection and modeling in Urban Freight Transport

Cities facing data collection and modeling in Urban Freight Transport. SMILE partner meeting Bologna 05/11/2013. Partner’s logo. Presented by: Martin Brandt. KLOK Kooperationszentrum Logistik e.V. is the competence center for logistics in Stuttgart Region,

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Cities facing data collection and modeling in Urban Freight Transport

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  1. Cities facing data collection and modeling in Urban Freight Transport SMILE partner meeting Bologna 05/11/2013 Partner’s logo Presented by: Martin Brandt

  2. KLOK Kooperationszentrum Logistik e.V. • is the competence center for logistics in Stuttgart Region, • has the overview over the relevant transport chains in the region, • knows the scientific and educational institutions in logistics, • consults economy and politics and takes part in projects: • „C-LIEGE“ for sustainable goods transport on the urban last mile, • AlpFrail to shift trans-alpine traffic to rails, • Open ENLoCC, the European network of logistics competence centers(KLOK is secretariat of the network), • together with its partners runs the logistics network Baden-Württemberg LogBW.

  3. Goods transport data:The municipal starting point „Wewanttoinfluencegoodstrafficflows.“ • We must knowtheamountofgoodstransport. • We must knowthe different transportflows(origins, destinations, routes). => „Weneeddata!“

  4. What is there? • Traffic dataexists(sometimes lots ofdata). • Goodstrafficdatasometimesexists. But: Usually, dataexistsonlyfortrafficamountson certainroads / atcertainpoints.

  5. Data from traffic counts Sources: • Physicalcounting. • Mobile countingdevices. • Installedcountingdevices. • Other sources. • Differentiation usuallybysize/weightofvehicleonly. • Location ofthecountingdeviceinfluencestheresult.

  6. Data from traffic counts Processing: • Differentiation bysize/weightofvehicleonly. • Perhaps lots ofinterpolation. • Perhapsbased on manyassumptionsfrom(experienced) consultants. • Risk: Pseudo precision. • Risk: Assumptionsareinterpretedasresults.

  7. The initial problem • Nomatrixdata. • Nodataofactualrouteschosenbythe individual vehicles. Possible Solution: • More data? • Perhaps down to individual trips? Result: No end ofdatarequest.

  8. Potential solution: Model building • Imagine the different types of urban goods transport. • Imagine typical routes and flows for each type.

  9. Potential solution: Model building • First result: Urban goods mobility has many aspects. • Decide which flows and modesare relevant. ? ?

  10. Potentially relevant flows • By type oftransport: • Through traffic • Last miledeliveries • Express deliveries • Deliveryservices • Service cars • Motorbikes • Bicycles • ... • By type of cargo: • Bulk • Parcels • Textiles • Food • Cooled food • Bottles/barrels • Waste • ...

  11. Model Building Process • For relevant flows, get a first understanding of: • The order of magnitude for the relevant area, • Important sources and sinks in the area, • Likely routes.

  12. Model Building Process • The modelbuildingprocesscausesmoreandmoreunderstandingofthe relevant partial flows. • Combiningthe partial resultsallowsforfirstestimatesofvolumes. • Typical (existing) trafficdatacanbeusedto check andcalibratetheestimations.

  13. Result • Understanding of the different types of goods traffic. • Existing data now is useful. • Independent interpretation of consultants‘ work. • Additional data request remains limited and is more specific. • The influence of whatever measures can be estimated, regarding • Influence upon the specific type of goods traffic, • Influence upon goods traffic as a whole. • A planning tool has been created!

  14. Thankyouforyourkindattention! Martin Brandt KLOK Kooperationszentrum Logistik e.V. Stammheimer Straße 10 D-70806 Kornwestheim +49 7154 965 00 50 brandt@klok-ev.de www.klok-ev.de

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