GeoPython 2024: BIMZEC

Petar Koljensic
"BIMZEC" - A Modelling Approach Towards Zero-Emission Logistics of Urban Highrise Construction in Amsterdam

BIMZEC (Biobased, Industrialized, Modular, Zero-Emission, Circular) represents a group of solutions expected to reduce emissions caused by the (high-rise) construction sector. To validate this we created a Python model, that uses an agent-based modeling approach to simulate the behavior of construction sites, hubs, and suppliers, and estimate transportation emissions associated with construction. The case study is based on the Amsterdam Metropolitan Area (AMA).

Research Team TU Delft (R. Vrijhoef, P. Koljensic, T. Tsui, T. van Binsbergen)

The Dutch housing market is facing huge demand for residential units, but at the same time, there is a big challenge in the construction sector to meet the necessary emission reduction standards. BIMZEC is an acronym for biobased, industrialized, modular, zero-emission, and circular construction solutions for high-rise buildings. The assumption for the research is threefold:
Industrial and modular construction enables customized dimensions and reduced weight of 2D and 3D building elements, and thus more efficient and lighter transport.
Biobased material use causes further weight reduction and therefore fewer emissions from fossil logistics, and increased options to apply zero-emission vehicles and equipment. * Circular solutions support the reuse of more local and regional secondary materials, and will thus lead to fewer transport movements and distance traveled to source new materials.

In this research, we have investigated the application and contribution of multifunctional construction hubs in and around the cities to support the named solutions for high-rise construction.

To test these and to create future strategies we applied a quantitative modeling approach using Python. It is based on the many collected and generated (geospatial) datasets. This model uses agent-based modeling to simulate the behavior of construction sites, hubs, and suppliers to estimate transportation emissions associated with construction to estimate emissions associated with construction logistics in the Amsterdam Metropolitan Area (AMA) for the next decade.

The focus of this talk is on the application of Python in the fields of Urban Data Science, explanations of this modeling approach, its outcomes, and the lessons learned.