A Firm Population Synthesis Model for Urban Freight Transport Demand Modeling

A Part of Micro-simulation Freight Transport Model for the Province of South Holland

Master Thesis (2019)
Author(s)

M.S. Nasir (TU Delft - Civil Engineering & Geosciences)

Contributor(s)

Lori Tavasszy – Mentor (TU Delft - Transport and Planning)

Michiel de Bok – Graduation committee member (TU Delft - Transport and Planning)

Bilge Atasoy – Graduation committee member (TU Delft - Transport Engineering and Logistics)

R. van Nes – Graduation committee member (TU Delft - Transport and Planning)

Faculty
Civil Engineering & Geosciences
Copyright
© 2019 Mesay Nasir
More Info
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Publication Year
2019
Language
English
Copyright
© 2019 Mesay Nasir
Graduation Date
26-09-2019
Awarding Institution
Delft University of Technology
Programme
['Transport, Infrastructure and Logistics']
Faculty
Civil Engineering & Geosciences
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Abstract

The agent - based modeling of freight transport that captures both the variety of actors and their complex decisions has not made as much progress as in passenger transport. Efforts towards agent-based models must include firms. However, the synthesis of a firm population in the context of urban freight transport has gotten little attention in literature. This research focuses on synthesis of a firm population for urban freight transport demand model. It uses literature review to define the firm agents and specifies a general population synthesis model based on iterative proportional updating (IPU). The model is then implemented on a case study on the province of South Holland. The utilized data is open, free-of-charge data offered by the Central Bureau for Statistics (CBS) of The Netherlands. The synthesis uses a sample-free, indicator-based approach to initialize the IPU-based model. The results show correlation between firms’ distribution and urban density of neighborhoods for most of the population. However, the suitability of a neighborhood to host a firm type has a more nuanced definition that cannot be encapsulated in a single indicator. More robust, economic-sector specific indicators are needed to disaggregate firm agents to neighborhoods and finer levels. Further research into IPU-based, sample-free firm synthesis models is recommended.

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