Research Projects

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Research Projects

STATUS: Complete YEAR: 2016 TOPIC AREA: Sustainability, energy, and health CENTER: MetroFreight

Developing and testing the freight landscape for Paris

Project Summary

Under such circumstances, the use of secondary data, in addition to primary data collection efforts, is an important research subject. However, again, the complexity and heterogeneity of urban freight data make the generalization of the urban freight characteristics challenging. While urban freight in two different cities may share characteristics in some aspects, they may not in others. To overcome this difficulty and make findings in different cities shareable, the accumulation of knowledge is a solution.

The objective of this research is methodological first. Following recent research work from Giuliano et al. (2015) on several California metropolitan areas, we wish to explore the use of available secondary data to estimate urban freight mobility. Can urban freight flows’ spatial patterns be accurately generated “using simple measures of population, employment and transport access?” (Giuliano et al., 2015). These authors define the concept of ‘freight landscape’ as “a description of freight activity imputed from population, employment and transport network characteristics,” with the hypothesis that “freight flows depend systematically on the spatial organization of freight suppliers and demanders as well as on the transportation facilities within the metropolitan areas.” In this line of work, we add a case study, choosing to focus on Paris, which is the largest urban cluster in terms of population and business activities in France, and one of the largest in Europe. Paris is an interesting case study because a comprehensive urban freight survey was carried out there (LAET, 2016), which will be tentatively used to further validate the results of the model.

We develop a model to estimate truck vehicle-kilometers on the Paris region’s road network in order to find the approach to generate the urban freight estimation with an adequate level of accuracy, without implementing a classic full-scale traffic model that is data exhaustive and requires a lot more work. For the analysis, we use, as a dependent variable, the estimated morning rush hour truck traffic available for 2009 that was provided by DRIEA, a French governmental agency for urban and regional development. This data is based on traffic count data and network traffic analysis. As for explanatory variables, we will test various demographic, economic and accessibility indicators that are usually available, such as population and population density, employment (by sector) and employment density, and accessibilities to them and to transportation hubs.

As part of a broader effort from other cities around the world to explore the relation between urban freight traffic and available secondary data (indicators) in large cities, we expect this research  will contribute to establishing a methodology of estimating urban freight traffic that is reliable and generalizable.



  1. Giuliano, G., Kang, S., Yuan, Q. (2015) Using proxies to describe the metropolitan freight landscape. Metrofreight report 15-1C. Available from:
  2. (last retrieved on February 12, 2016).
  3. Laboratoire Aménagement, Economie, Transports (LAET) (2016) Urban Goods Movement Surveys in Paris and Bordeaux. Presentation at Urban Freight Platform international workshop, February 11, University of Gothenburg. Available from:


Adrien Beziat
3 Rue Maurice Audin
Vaulx-en-Velin, Lyon, Auvergne-Rhône-Alpes 69120
[email protected]

Laetitia Dablanc
Director of Research, IFSTTAR, French Institute of Science and Technology for Transport, Development and Networks - University of Paris-East
14-20 boulevard Newton, Cite Descartes
Marne la Vallee cedex 2, 77447
[email protected]

Adeline Heitz
Assistant Professor
292 Rue Saint-Martin
Paris, 75003
[email protected]

Takanori Sakai
PhD Student, School of Urban Planning and Policy
412 S. Peoria
Chicago, IL 60607
United States
[email protected]