Surveys on Urban Freight TransportWorkshop Summary

Workshop chair: Arnim MeyburgRapporteur: Matthew Roorda

Resource paper: Danièle Patier, Jean Louis Routhier, “How to Improve the Capture of Urban Goods Movement Data”

Discussant: Michael Browne

Contributed papers: Matthew Roorda, “ Comparing GPS and Non-GPS Methods…”

Wulf-Holger Arndt, “Combination of Quantitative and Qualitative Methods…”

Participants Michael Browne, UK Alban Igwe, Nigeria Kara Kockelman, USA Stephan Krygsman, S. Africa Jacques Leonardi, UK Richard McMahon, Ireland Julius Menge, Germany Arnim Meyburg, USA Simo Pasi, Luxembourg Danièle Patier, France

Pedro José Pérez Martinez, Spain

Alan Pisarski, USA Christophe Rizet, France Matthew Roorda, Canada Jean Louis Routhier, France Paulo Ueta, Brazil Wulf-Holger Arndt, Germany Imke Steinmeyer, Germany Nina Karasmaa, Finland

Key points raised Urban goods and services movement are very complex;

Therefore it is important to very clearly define the purpose of the survey;

This will lead to a well-defined scope of the survey;

We must describe the scope clearly in relation to the “universe” of commercial travel;

For many purposes, we need to look at establishments’ economic activity (including goods movement) and the vehicle operations that result.

Definitions and Harmonisation

Develop a glossary of definitions that can be used as a resource Researchers, practitioners can communicate Studies can be compared Data collection methods can be shared

Consistency with intercity freight flows and national economic accounts (I/O)

Technology (e.g. GPS) Use of technology for goods movement data

collection is likely to expand; Technology should be seen as a complementary

tool for data collection; Can provide highly precise, rich and wide-

ranging information; This can improve respondent burden; We need to find ways to overcome privacy

issues.

Obtaining data from existing sources

Researchers need to be entrepreneurial about obtaining data from Carriers, 3PL, large retailers , ports,

technology providers, toll authorities, governments, etc.

Find win-win situations for data sharing

Data and Modelling

Descriptive statistics can be as valid a basis for data collection as modelling;

A mathematical model is not a prerequisite for data collection.

Some Research Challenges Links between global supply chains and urban goods

movement;

Substitution between consumer and business goods movement (e-business);

Complexity of sampling with diverse economic structures;

Understand implications of public policy decisions using techniques such as Stated preference surveys Before / after studies;

New/alternative techniques for data collection, e.g. Qualitative methods Web-based surveys.

Merci beaucoup!