Construction logistics can be described as the process of coordinating the movement of goods and information from their origin to the point of use within a construction context. In a circular economy (CE), retaining the value of materials across multiple use phases depends not only on the physical management of materials, but also on the availability and coordination of information regarding their production, use, and end-of-use (EoU) management.

Actors seeking to integrate circular materials into production or sales channels require information on available supply for planning, while those managing EoU materials require knowledge of potential demand to enable efficient reverse logistics. Information sharing is therefore essential for matching supply and demand and for planning circular material flows. From this perspective, logistics management plays a key role in enabling circularity at scale, particularly in the construction industry—the largest generator of waste in the European Union. In this context, construction logistics related to construction and demolition waste management (C&DWM) constitutes a critical lever for enabling circular material flows.

Focusing on C&DWM, this thesis investigates logistics-related activities, challenges, and enablers associated with the reutilization of construction materials and products in Swedish new-build and renovation projects. Through semi-structured interviews and case studies, the three studies included in this thesis examine how actors involved in construction logistics perceive circularity, how logistics activities support the reutilization of materials and products, and which challenges and enablers influence their implementation. The thesis addresses the following research questions:

RQ1:

How do actors involved in construction logistics perceive circularity, their role, and the challenges and enablers associated with increasing circular material flows?

RQ2:

What logistics activities can support operations for circular material flows?

Taken together, the findings indicate that enabling circular material flows depends on coordinating not only physical logistics activities, but also information, incentives, and actors across projects, markets, and system levels.

Construction is a transport reliant sector accounting for a large share of urban freight transport in European cities. Traditionally, construction planners have focused on on-site efficiency, often neglecting logistics to and from the site. Additionally, urban planners focus on the current and future city structure, neglecting the transitional phase during construction. Traffic planners in the municipality tend to solve temporary traffic management plans ad-hoc, only looking at one construction site at one specific time and space, instead of looking at how multiple temporary traffic management plans affect the urban area. Thus, creating a planning gap for how we get from the current city structure to the future city structure.

The purpose of this thesis is to investigate how traffic simulation and machine learning can support collaborative planning processes for construction transport in urban environments. To address this purpose, two research questions were formulated and answered through two studies: one qualitative and one quantitative. The first research question and corresponding study aim to understand how traffic simulations can support collaborative planning processes to reduce disruptions caused by construction projects in urban areas. The second research question and its associated study aim to examine how machine learning can support decision-making in construction transport planning.

The qualitative study explores the collaborative planning process of the Ostlänken megaproject in Norrköping, a new highspeed railway connecting Stockholm and Linköping. Earlier studies show that to tackle the issues caused by construction projects, urban planners and traffic planners in the municipality needs tools to assess multiple disturbances at once. A tool that is currently used by traffic planners is traffic simulations; however, previous studies show that the use of traffic simulations in construction focus on car mobility, neglecting pedestrians, cyclists and public transport users. The qualitative study resulted in two conference papers, Paper I and Paper II. The findings highlight that disturbances often stem from a project-centric focus and that municipalities need to coordinate planning at an early stage. The study introduces strategic traffic management planning as a way to coordinate temporary traffic management plans. Traffic simulations are a key component, supporting both the assessment of traffic disturbances and the evaluation of mitigation measures. However, current simulations used by municipalities often lack detailed hourly data on travel flows across all modes (cars, pedestrians, cyclists, public transport, and goods transport), limiting analyses to a daily level.

The quantitative study examines one of the least detailed components of traffic simulations, construction transport in urban environments. Currently, construction transports are either omitted or included only in aggregate form within other transport sectors. This study takes a different approach by estimating construction transport demand using machine learning with construction context data as input. The findings show a potential of machine learning models to predict construction transport demand, however, the high variance in the contextual data limits accuracy. To provide more effective decision support for urban and traffic planners, the study highlights the need for higher-quality data and standardization of existing datasets.