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Testing the “Freight Landscape” Concept for Paris

Testing the “Freight Landscape” Concept for Paris The concept of “freight landscape,” the basis for a modeling approach for urban freight traffic estimation using commonly available datasets, was proposed in 2017 with a case study applying it to the Los Angeles metropolitan area. To extend the scope of that research, we conduct another case study using data from the Paris region, France. We estimate spatial lag models using population, employment, or establishment transportation accessibilities as explanatory variables and network-based truck traffic as the dependent variable, modifying the approach used in the Los Angeles study. We identify differences in the characteristics of the variables and the models between the Los Angeles and Paris cases, each having a distinctively different urban structure. While the models estimated for the Paris region provide beneficial insights into the relationships between freight landscape indicators and urban freight traffic, the complex correlation structure among indicators, as well as the limitation of the models for specifying the areas of very high truck traffic, underlines the need for further research on the modeling framework and for more case studies. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Transportation Research Record SAGE

Testing the “Freight Landscape” Concept for Paris

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Publisher
SAGE
Copyright
© National Academy of Sciences: Transportation Research Board 2018
ISSN
0361-1981
eISSN
2169-4052
DOI
10.1177/0361198118776783
Publisher site
See Article on Publisher Site

Abstract

The concept of “freight landscape,” the basis for a modeling approach for urban freight traffic estimation using commonly available datasets, was proposed in 2017 with a case study applying it to the Los Angeles metropolitan area. To extend the scope of that research, we conduct another case study using data from the Paris region, France. We estimate spatial lag models using population, employment, or establishment transportation accessibilities as explanatory variables and network-based truck traffic as the dependent variable, modifying the approach used in the Los Angeles study. We identify differences in the characteristics of the variables and the models between the Los Angeles and Paris cases, each having a distinctively different urban structure. While the models estimated for the Paris region provide beneficial insights into the relationships between freight landscape indicators and urban freight traffic, the complex correlation structure among indicators, as well as the limitation of the models for specifying the areas of very high truck traffic, underlines the need for further research on the modeling framework and for more case studies.

Journal

Transportation Research RecordSAGE

Published: Dec 1, 2018

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