The transportation sector is undergoing a significant shift towards electrification, driven by sustainability challenges and battery electric vehicle (BEV) technology advancements. This transition also leads to a convergence of transport and energy industries, introducing new dynamics and creating new business opportunities in these sectors. Such changes have extensive implications not only for single firms but for entire ecosystems as these adapt to new technologies, activities, and business models. This study introduces the concept of Industry-Converging Ecosystems, where traditional industrial boundaries become less distinct, requiring collaboration among unfamiliar participants across various industries. This paper investigates the tensions between value creation and value capture and control in such ecosystems through a case study of an innovative electric charging system for buses in V & auml;ster & aring;s, Sweden. The findings advance ecosystem research by (1) introducing the industry-converging ecosystem concept, (2) revealing two sources for business model tensions stemming from monetisation uncertainties and resource competition, and (3) demonstrating the lack of clarity in ecosystem control caused by limited influence over business models and diminished legitimacy due to their newness.

The adoption of electric heavy trucks holds great potential for decarbonising freight transportation, but the market remains nascent. Electrification of the road freight transportation system is complex, involving many interrelated variables, including vehicles, charging infrastructure, and various stakeholders. Effective policy interventions are crucial for accelerating the transition, and developing dynamic models is helpful for understanding the dynamics involved. This study develops a system dynamics model to explore the long-term adoption of electric trucks and charging infrastructure development, considering technology maturity, awareness, and cost. Using real-world data from Sweden (2017–2060), the model analyses various policy levers. The results show that increasing subsidies for charging stations leads to a considerable rise in electric truck adoption, while investments in vehicle technology maturity are the most cost-efficient when financial resources are constrained. By modelling policy interventions endogenously, the study highlights the dynamic impact of policymaking on accelerating the transition to sustainable road freight transport.

Transport-related externalities have a negative impact on urban environments and health. Therefore, urban logistic systems encounter significant sustainability challenges. One urban transport strategy is city hubs, which can achieve a more effective system. However, city hubs have yet to realize their full potential due to the challenges of their implementation. This paper investigates the dynamics of city hub implementation from the perspectives of the Logistic Service Providers (LSPs), the hub operator and the receivers of the goods. Drawing from qualitative and quantitative insights from a case study in Stockholm, a system dynamics model is built to understand these dynamics. The results of this model demonstrate that the city hub is economically sustainable only if collaboration is achieved between LSPs and receivers. However, the total system cost increases with collaboration. When collaboration cannot be achieved, the public sector’s involvement is crucial to achieving hub adoption. The results also show that implementing an environmental zone (internal combustion engine vehicles are forbidden entry) does not help hub adoption, as the share of the LSPs cost spent on vehicles is low compared to other costs. Therefore, policies favoring the actors involved in the scheme should be considered. The main contribution of this paper lies in identifying possible strategies and outcomes of city hub implementation within the studied area and providing guidance for policymakers on policies for successful implementation. Moreover, the model can be adapted to other case studies by adjusting the input data, thereby serving as a tool for policymakers in different geographical contexts.

The electrification of heavy road freight is a promising pathway to decarbonise the sector, yet it depends on the electricity supply system, which itself is undergoing a complex transition to low-carbon energy. Achieving net-zero emissions requires understanding the interdependencies between transport and electricity systems. To address this challenge, we developed a system dynamics (SD) model to explore how freight electrification interacts with electricity supply capacity and market-based pricing. Drawing on literature review and expert interviews, the model integrates three modules: demand (electric truck fleet and charging behaviour), supply (capacity expansion with construction delays), and price (merit-order dispatch). Preliminary results indicate that e-truck adoption has limited direct impact on electricity prices under baseline assumptions. However, cross-sectoral competition emerges as critical, where accelerated electrification in other sectors may drive electricity prices upward, reducing electric truck competitiveness. Charging coordination strategies also influence electricity price dynamics. The model provides a holistic perspective and highlights the need for coordinated planning between transport and electricity sectors. However, model calibration is ongoing, and results should be interpreted cautiously.

The transition to electric trucks is a multi-system transition (MST), shaped by dynamic interactions between the transport and electricity systems. While scientific research has begun to consider the complexities of such MST phenomena, there is limited understanding of their temporal impact on transitions. This research provides an overview of the critical couplings shaping the e-truck MST using the technology-actor-institution structure and employs qualitative system dynamics (SD) modelling to identify emerging multi-system dynamics. We apply the SD models to develop two case studies of e-truck adoption in Swedish forestry and port hinterland transport in the Netherlands. The results show similarities and differences between cases, emphasising the context-dependency of road freight electrification transitions. The forestry case demands geographical extension of electricity grids, whilst the port case requires grid congestion management, with profound consequences for policies in both systems. We conclude that SD modelling supports mapping the multi-system dynamics of the road freight electrification transition, providing insights into temporal effects and feedback.

Electrification of heavy-duty transport will play a key role in achieving the EU climate goals to cut CO2 emission. To enable electrification, an extensive network of charging infrastructure will be needed. This paper takes the perspective of the charging station. The Swedish forestry industry is used as a case. The study explores the relation between transport work, truck specifications, and charging infrastructure setup. A stochastic, discrete-event model is developed, simulating the power demand and queue situation when charging electric heavy roundwood trucks at the receiving industries. The simulations show that with 600 kWh available energy in the battery, 71–83% of the incoming trucks or 48–71% of the incoming transport work (measured in tonne-km) can be electrified. The variations depend primarily on differences in average transport lengths. With unlimited number of chargers, the peak power needed is 6.2–6.6 MW, but there are large variations in power demand over the day. If the number of chargers is limited, the peak power is also limited, but there might instead be queues. However, if the number of chargers is selected appropriately or the truck inflow is actively and efficiently planned, the peak power can be reduced to around a third while still keep average queue time on acceptable levels. Reducing peak power is important, as it reduces investment costs, and limiting the capacity cost for the grid connection.

To help automated bus services to be competitive in the market, understanding what factors influence the public's acceptance and adoption of an automated bus service and how these factors change over time is critical. Various factors affect users’ acceptance of this new bus mode, with the quality of service standing out as a significant consideration. Based on pilot demonstrations, some prior studies have explored the factors influencing the user acceptance of new automated vehicle technology based on real-life riding experience. However, these studies are restricted to predicting the adoption of an automated bus by utilising cross-sectional data, but with no data to explore whether public attitudes and acceptance would change over time. To fill the research gap, a longitudinal survey was conducted. Using the panel data, the present study focuses on users with real-world riding experiences on automated buses operated in a mixed-traffic environment on public roads in Stockholm. Contributing to the longitudinal analysis of the public's acceptance of automated buses, we develop a novel conceptual model integrating the service quality and the technology acceptance model (TAM). A dynamic structural equation model is employed to explore the changes in judging criteria regarding service adoption among adopters and non-adopters. The findings indicate that comfort and convenience are the most significant determinants of satisfaction and the perception of usefulness, which, in turn, positively affect people's adoption intentions, as well as encouraging favourable word-of-mouth behaviour. It is expected that the provision of faster, safer, more comfortable and convenient riding experiences with automated buses will eventually increase the use of these buses, as well as improve word-of-mouth communication.

There is a growing recognition that traditional forecasting and decision-making approaches might fall short considering the many uncertainties and complexities facing the development of the transport system. The project Managing deep Uncertainty in planning for Sustainable Transport (MUST), funded by Trafikverket and conducted by KTH ITRL and VTI, aims to explore emerging methods for improving the handling of deep uncertainty in the long-term planning of future transport systems. The core of MUST is to explore, develop, and demonstrate tools and methods grounded in Decision Making under Deep Uncertainty (DMDU) and Exploratory Modeling and Analysis (EMA). These approaches are intended to support a shift towards more robust and adaptable planning methodologies.

The project is performed in two phases, with the first phase dedicated to laying a foundational understanding of deep uncertainty in transport planning. This report covers the first phase which has included the following tasks: 

• A literature review on deep uncertainty and existing decision-making and system analysis methods under such conditions, with a focus on transportation. 
• A workshop series with Trafikverket identifying transport planning challenges marked by deep uncertainty.
• A case study of applying DMDU through a case study on climate policy robustness (primarily reported in other deliverables).

The literature review covers how the nature of uncertainty in socio-technical systems can be understood, classified, and analyzed. For policy analysis and decision making, the literature underscores the importance of considering multiple futures in model-based analysis when faced with deep uncertainties. DMDU and EMA methods are reviewed and summarized, and their application to transport are discussed. The literature also summarizes studies on uncertainty in model-based transport planning and policy analysis and concludes that the primary location of deep uncertainty is in the model inputs in the form of “scenario uncertainty”. In the workshop series, uncertainty related to producing the base forecast (Swe: basprognos) and policy analysis for domestic transport climate policy was analyzed. This analysis suggested that scenario uncertainty is a main source of deep uncertainty, but also uncertainty related to the system boundaries where highlighted. Furthermore, potential benefits and drawbacks of EMA and DMDU were discussed. In the case study, it is explored how the Scenario tool can be further leveraged by DMDU. More specifically, MORDM (see Section 2.2.3) is applied to assess to what extent it may allow a broader set of policy options to be explored, and how it can provide a better understanding of the robustness and vulnerabilities of different types of policies. 

A key takeaway from MUST phase 1 is that DMDU and EMA could provide several potential benefits and that methods and tools for applying them are maturing. However, it is possibly a long way to go before DMDU and EMA can be integrated as a regularly used method during the planning process. This is due to organization and process-related issues, as well as technical issues on how to effectively apply DMDU and EMA to Trafikverket’s national transport models. These technical issues will partly be explored in MUST phase 2. 

Purpose: Electrification is a promising solution for decarbonising the road freight transport system, but it is challenging to understand its impact on the system. The purpose of this research is to provide a system-level understanding of how electrification impacts the road freight transport system. The goal is to develop a model that illustrates the system and its dynamics, emphasising the importance of understanding these dynamics in order to comprehend the effects of electrification. Design/methodology/approach: The main methodological contribution of the study is the combination of the multi-layer model with system dynamics methodology. A mixed methods approach is used, including group model building, impact analysis, and literature analysis. Findings: The study presents a conceptual multi-layer dynamic model, illustrating the complex causal relationships between variables in the different layers and how electrification impacts the system. It distinguishes between direct and induced impacts, along with potential policy interventions. Moreover, two causal loop diagrams (CLDs) provide practical insights: one explores factors influencing electric truck attractiveness, and the other illustrates the trade-off between battery size and fast charging infrastructure for electric trucks. Originality/value: The study provides stakeholders, particularly policymakers, with a system-level understanding of the different impacts of electrification and their ripple effects. This understanding is crucial for making strategic decisions and steering the transition towards a sustainable road freight transport system.

The urban logistics system causes negative externalities, such as pollution, noise and congestion. This study focuses on city hubs as a concept to reduce these externalities by improving consolidation and adopting zero-emission vehicles. Our research employs system dynamics as a method to uncover the dynamics and mechanisms related to the barriers and potentials of city hub implementation in Stockholm from the perspectives of the Logistic Service Providers (LSPs), the receivers and the public sector. Moreover, a mixed method approach is used for data collection, allowing us to extract the knowledge from the real implementation case, co-create a qualitative model as a Causal Loop Diagram (CLD) with the stakeholders involved in the system, and generalise the model. The mixed method approach includes a group model-building workshop, literature review, existing city hub analysis and stakeholder interviews. The main result is a CLD, visualising the dynamics of the introduction of city hubs. The CLD explores three potential incentives and policies: (i) the receivers change address; (ii) shippers oblige LSPs to use the hubs; (iii) the public sector gives monetary incentives to LSPs. The CLD presented in this paper establishes a validated system structure for the urban logistic system and facilitates the policymakers' understanding of the barriers to implementing city hubs and what policies could help their implementation.