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Energy consumed in freight transport : first results from the shipper and operator survey

Energy consumed in freight transport : first results from the shipper and operator survey. Nicolas Lebelle Philippe Marchal Christophe Rizet. Structure of this presentation. Part One : an overview of SOS Objectives, structure and sample of the survey Geocoding aspects

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Energy consumed in freight transport : first results from the shipper and operator survey

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  1. Energy consumed in freight transport : first results from the shipper and operator survey Nicolas Lebelle Philippe Marchal Christophe Rizet

  2. Structure of this presentation Part One : an overview of SOS Objectives, structure and sample of the survey Geocoding aspects Illustration of general results : generation Part Two : energy consumption Computing energy consumption Energy efficiency of road freight transport

  3. Part one : an overview of INRETS Shipper & Operator Survey (SOS) - tracing the shipments in Europe

  4. 1 Objectives of the survey • We have good statistical data for each single mode, derived from the transport sector but • no relations between the modes (no information on multimodal transport) and • no link with economic activity • SOS makes this information, by tracking the shipment along the chain • A first Shipper surveys in France in 1988 and two limited surveys in 1999 (NPDC, Mystic) • 2004 SOS includes several improvements and a new objective : quantifying energy consumption

  5. The 4 levels of the survey

  6. Tracing the shipment along the chain

  7. Geographical aspects: pre-geocoding Before the field survey A list of pre-geocoded places in the CAPI limits the localization errors and the burden ; a draft list based on NIMA database (worldwide coverage of the survey) Problem : presence of multiple values in the names (Ex : 3 Frankfurt in Germany) An automated process was developed using another database of main cities with population data Ex : • Frankfurt /45km/ Nurnberg • Frankfurt /82km/ Berlin • Frankfurt /0km/ Frankfurt

  8. Geographical aspects : additional geocoding After the field survey Why an additional geocoding ? • missing or erroneus coordinates • mis-spellt names, inconsistency with the initial CAPI database How ? Spell checking functions : similarity tests between names

  9. Geographical aspects : transport chains validation and distances estimation a) Detection of "suspect" shipments, by checking : • Consistency between shipments and legs (final destination for shipment = destination for last leg) • Legs chaining (destination for step i = origin for step i+1) b) Additional checking based on distances • Shipments: distance classes and countries • Legs: distance classes and modes c) Road distances estimation A standalone tool for visualization and processing

  10. Geographical aspects : computational tool

  11. OPTIMIZING THE SAMPLE • 2 Objectives: • Sufficient number of shipments for non-road modes (& for the north region) • Improve the accuracy of the results • Sampling method: the 2 steps • Sampling the firms from an exhaustive list : oversample the firms using non-road modes and in the North (Activity & localization) ; Strates based on the number of employees to improve the accuracy • Sampling the shipments : in the CAPI, 3 shipments are randomly chosen among the last 20 shipments : The probability of being selected is weighted in order to adapt the sample to our objectives.

  12. Computing energy consumption • 1) At the leg level • Energy per vehicle & leg • Evl = (distance + empty) * f(vehicle type, total weight) • Energy per shipment & leg • Esl = Evl * (shipment weight / total vehicle load) • 2) At the shipment level • Energy per shipment Es = Sum[Esl] • 3) At the company level • Energy per Cy Ec = Sum [Es]

  13. The sample 2 962 establ. over 10 (or 6) employees 10 462 shipments traced of which 9 742 complete transport chains 27 069 operators (intervenants) 􀀹 20 074 legs The population 69 256 establ shippers 738 millions of shipments Part 2 : Overview of first results

  14. Traffic generation/ activity :Yearly tonage (1000 t.) / estab.

  15. Traffic generation / size ofestabl. Yearly tonage (1000 t.) / establ.

  16. Traffic generation / employeeYearly tones / employee

  17. Energy consumption first results

  18. Computing energy consumption • 1) At the leg level • Energy per vehicle & leg • Evl = (distance + empty) * f(vehicle type, total weight) • Energy per shipment & leg • Esl = Evl * (shipment weight / total vehicle load) • 2) At the shipment level • Energy per shipment Es = Sum[Esl] • 3) At the company level • Energy per Cy Ec = Sum [Es]

  19. Average energy efficiency for road goe/tkm

  20. Energy efficiency per shipment : goe/tkm is very scattered (2002 results)

  21. Next steps on SOS and energy • A very powerful tool for research, linking information on the shipper, the shipments, the operators, the transport & logistic services • Modeling the influence of logistical choices on energy consumption and energy efficiency • 4 steps model + energy consumption • Clusters of establ. in which a direct estimate of energy would be possible

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