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Energy Consumption in Freight Transport: Initial Survey Results

This presentation details the first results from a survey on energy consumed in freight transport, examining aspects such as geocoding, computing energy consumption, and energy efficiency of road transport. The survey aims to quantify energy consumption by tracing shipments in Europe. It covers geographical aspects, optimization of the sample, computational tools, and initial findings on traffic generation and energy efficiency.

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Energy Consumption in Freight Transport: Initial Survey Results

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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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