1. Freight transport modelling - an approach to understand demand and use of transport energy Annecy, May 26th, 2008 Ole Kveiborg and Jean-Louis Routhier Institute for TransportLaboratoire d'Economie des Technical University Transports of DenmarkLyon, France ok@transport.dtu.dkok@transport.dtu.dkjean-louis.routhier@let.ish- lyon.cnrs.fr

2. Freight transport model typology Energy consumption

3. Freight transport model typology  Production and consumption. Deals with decisions about the location of the production and consumption, type of products and the volume.  Spatial distribution of trade. The relation between production and consumption locations has to be established. To what region will the produced goods be sold (sales) or from what region are the goods bought (sourcing)?  Logistics on firm level. Decisions about the use and the location of inventories and about supply chain management.  Mode and route choice. What transport modes and what types of vehicles will be used for transporting the goods on which overall route (not necessarily linked to a detailed network.  Transport logistics. The utilisation of the vehicles, load factors, empty running and similar decisions made by the transport provider.  Networks and assignment. The actual allocation of vehicles onto the physical road network.  Energy and environmental consequences.

4. Aggregate and disaggregate approaches  What is the relevant policy question? Future flows in spcific corridors National energy consumption Determining macro drivers for future flows  Choose appropriate model Detailing on the focus area Not one model can answer all questions

5. An aggregate approach  The influence of different macro drivers Kveiborg et al. (Arcueil, 2005; Torino, 2007) A decomposition of freight traffic (and transport) growth on  Economic activity  Physical content of production  Handling (logistics in an macro sense)  Load and length  Empty runs Decoupling (or coupling) is a consequences of adverse influencing major drivers

6. Urban freight modelling and energy consumption An illustration of modelling approach

7. ...To explain the goods vehicle energy consumption in the urban area... * logistical choice (routing, packing, tracking…) * location of firms, warehouses and consumers * types of vehicles (LGV, HGV), engine specifications Urban goods modelling and energy issues Calculation of the impact of the urban logistics on vehicle flows:...Policy Oriented (PO) Models to help forecasting and decision making. * consumer behaviours (home deliveries, e-commerce,…) Demand modelling Delivery and pick-up generation for the total industry in the urban area Several submodels... Vehicle flows modelling Vehicle movement generation (veh.km, Vehicle energy consumption modelling type of vehicle, motorization, speed, loading, acceleration, * economic valuation

8. The commodity gravity model is failing Inside the Town : the organisation in tours is dominant - Vehicles have very different sizes and freight volume - Packaging are very different One quantity of goods may be delivered by different types of vehicles, of way of organisation etc. For a given commodity, the flows of vehicles are determined by factors exogenous to transport. Methodological commitment of urban freight modelling - The O/D matrix of goods is different from the O/D matrix of vehicles The movement : an efficient unit of observation a good knowledge of the generators a good description of the deliveries and pick-ups

9. Few PO UGM models to answer the issues * number of vehicle.km for different types of purpose and types of organisation * fuel consumption and CO2 emission (per day, per inhabitant, per job…) * number of deliveries and pick-ups per economic sector Urban goods modelling and energy issues An example: The Freturb Model (COST Arcueil and Berlin 2005) Pick-ups and deliveries model (comodity flows between establishments) The total freight in the city: Three modules: Town management module (raw material and goods works, urban networks, removals) Purchasing trips model (last mile by the consumer) Output Indicators: Specific surveys (4500 establishments 2,200 drivers in three different size towns) * number of on street parking (congestion) Specific surveys Household trip survey and specific surveys An effort of data collection:

10.  Street occupancy, Traffic and Energy consumption: *per vehicle; *per industry sector; *per traffic segment; *according to the management mode (hauliers, own account); *energy balance between goods movement and trips for purchase in a town.  Examples 77% 10% 13% 73% 12% 14% Urban goods modelling and energy issues 2 : UDC with cooperation 1 : UDC without coop. Référence peripheral platforms Highway Railway - 34,000 veh.km/week ( <1%) UDC simulation - 350,000 veh.km/week ( 7%) Output of Freturb:

11.  “Good” Input Variables for Simulation: *location of firms, trade centres, warehouses and consumers; *logistical choices (management mode, co-operation,..) *consumer behaviours; *interaction between commercial transport system and individual trips for purchase; *interaction demand(need of goods)-supply(transport operating) Urban goods modelling and energy issues

12.  Brakes on PO-UGM modelling: * it is difficult to consider the total transport activity * lack of data (costly, lack of interest) * models are not widely policy-oriented * O/D distribution is difficult to calculate in urban areas * connection with the upper scope models (regional, national) * difficult to compare methods and results of different modelling approaches Urban goods modelling and energy issues

13.  Some recommendations: *Effort in developing and harmonising data collection *Improvement of the integration of different scopes of models, *to harmonise the space and time units; *to list and analyse the main input exogenous and endogenous variables *to improve the efficiency in terms of prediction of energy consumption. Urban goods modelling and energy issues

14. General recommendations and future unsolved issues  Links between scales of interest Local policy and global policy analysis require different types of models, but links and consistency between them is often lacking Local O/D matrices difficult to obtain and how are they related to regional/national O/D Data on commodity based production and freight transport (vehicle approach) difficult to combine  Knowledge of logistics and inclusion in models still in its infancy  Data focussing on linking demand with e.g. supply chains and final use