Quantifying freight trip and freight generation from spatial developments in the Netherlands

Master Thesis (2024)
Author(s)

W.G.J. Gommans (TU Delft - Civil Engineering & Geosciences)

Contributor(s)

Lóránt A. Tavasszy – Mentor (TU Delft - Transport and Planning)

M.A. de Bok – Graduation committee member (TU Delft - Transport and Planning)

M Kroesen – Graduation committee member (TU Delft - Transport and Logistics)

JA Annema – Graduation committee member (TU Delft - Transport and Logistics)

T.S. Vlot – Graduation committee member (Movares)

Jessica van Rijn – Graduation committee member (Movares)

Faculty
Civil Engineering & Geosciences
More Info
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Publication Year
2024
Language
English
Graduation Date
30-05-2024
Awarding Institution
Delft University of Technology
Programme
Transport, Infrastructure and Logistics
Sponsors
Movares
Faculty
Civil Engineering & Geosciences
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Abstract

This research aims to enhance the understanding and quantification of logistics in the Netherlands by identifying factors influencing freight activities in urban areas. Using a linear regression model developed from BasGoed freight tour data, CBS location data, and Dutch zonal plan data, the study integrates 26 variables to predict freight trip generation and freight generation. The main explaining factors include the surface area of distribution centers, employment in various sectors, the presence of rail or inland waterway terminals, urban density, and the surface area of business spaces. The model, validated through statistical tests and literature comparisons, provides reliable predictions, with employment influencing freight production and surface area affecting freight attraction.

A case study in the Utrecht A12 zone demonstrated the practical relevance of the model, offering insights for policymakers to design logistics-friendly urban environments. Limitations include simplified statistical procedures and reduced accuracy in seaport and transhipment areas. Despite these constraints, the model delivers valuable predictions for various spatial scenarios.

Future research should explore model performance using CBS XML-microdata, consider non-linear relationships, and examine variables for shipment size and vehicle type. Addressing causality and endogeneity with instrumental variables is also recommended. This study contributes significantly to both scientific and societal domains, providing a robust model for freight trip generation and freight generation, and guiding municipalities in effective spatial planning and decision-making for sustainable urban development.

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