MetroFreight

Developing and testing the freight landscape for Paris

Project Number

4.2b

Project Summary

In many cities around the world, the scarcity of data for urban freight, compared with passenger mobility, is a long-lasting issue that hampers a through-understanding of urban freight flows and their impacts. The complexity and heterogeneity of urban freight activities, as well as corporate confidentiality issues make the data collection costly and difficult to implement. As a result, only a limited number of cities can implement surveys for urban freight; even in the case surveys are implemented, their scopes are often limited.

Project Status

Complete

Year

2016

Topic Area

Sustainable Urban Freight

P.I. Name & Address

PhD candidate
University Paris-Est, IFSTTAR/SPLOTT
14-20 boulevard Newton, Cite Descartes
77447 Marne la Vallee cedex 2
France
Adrien.beziat@ifsttar.fr
Director of Research, IFSTTAR, French Institute of Science and Technology for Transport, Development and Networks - University of Paris-East
IFSTTAR, AME
14-20 boulevard Newton, Cite Descartes
77447 Marne la Vallee cedex 2
France
Laetitia.dablanc@ifsttar.fr
Ph.D. candidate
University Paris-Est, IFSTTAR/SPLOTT
14-20 boulevard Newton, Cite Descartes
77447 Marne la Vallee cedex 2
France
adeline.heitz@ifsttar.fr
PhD Student, School of Urban Planning and Policy
University of Illinois at Chicago
412 S. Peoria
Chicago, IL 60607
United States
tsakai3@uic.edu

In many cities around the world, the scarcity of data for urban freight, compared with passenger mobility, is a long-lasting issue that hampers a through-understanding of urban freight flows and their impacts. The complexity and heterogeneity of urban freight activities, as well as corporate confidentiality issues make the data collection costly and difficult to implement. As a result, only a limited number of cities can implement surveys for urban freight; even in the case surveys are implemented, their scopes are often limited. This project is looking at ways to estimate freight flows from more "simple" data related to population and employment. It is based on a methodology developed by Giuliano et al. for Los Angeles. It is testing that methodology and providing methodological conclusions.

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.

 

References

  1. Giuliano, G., Kang, S., Yuan, Q. (2015) Using proxies to describe the metropolitan freight landscape. Metrofreight report 15-1C. Available from:
  2.  https://www.metrans.org/sites/default/files/research-project/MF%2015-1C_... (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:
  4.  www.chalmers.se/en/centres/lead/urbanfreightplatform/Pages/default.aspx