Surveys on Urban Freight Transport Workshop Summary Workshop chair: Arnim Meyburg Rapporteur: 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 pointsraised 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!