Road freight transport is a key function of modern societies. At the same time, road freight transport accounts for significant emissions. Digitalization, including automation, digitized information, and artificial intelligence, provide opportunities to improve efficiency, reduce costs, and increase service levels in road freight transport. Digitalization may also radically change the business ecosystem in the sector. In this paper, the question, "How will digitalization change the road freight transport landscape?" is addressed by developing four exploratory future scenarios, using Sweden as a case study. The results are based on input from 52 experts. For each of the four scenarios, the impacts on the road freight transport sector are investigated, and opportunities and barriers to achieving a sustainable transportation system in each of the scenarios are discussed. In all scenarios, an increase in vehicle kilometers traveled is predicted, and in three of the four scenarios, significant increases in recycling and urban freight flows are predicted. The scenario development process highlighted how there are important uncertainties in the development of the society that will be highly important for the development of the digitized freight transport landscape. One example is the sustainability paradigm, which was identified as a strategic uncertainty.

Driving automation technology is attractive for the road freight transport sector since driverless trucks (DL-trucks) may drastically reduce driver costs, increase truck utilization and improve road safety. Although DL-trucks may bring significant impacts to the transport system, research on the future diffusion and impacts of DL-trucks is scarce compared to passenger transport. In this paper the sociotechnical innovation system developing, diffusing and utilizing DL-trucks in Sweden is analyzed based on the technological innovation systems (TIS) framework. The analysis is based on 20 expert interviews with a total of 23 representatives from 16 actors in the DL-truck TIS in Sweden. The TIS analysis shows that there are significant uncertainties in the timeline, operational capabilities, infrastructure requirements and regulative landscape for a widespread DL-truck deployment. There is a general view among the interviewees that DL-trucks is an important opportunity for Swedish industry and the economy. From a transport system perspective, DL-trucks are expected to bring sustainability benefits but it remains uncertain whether these benefits will be realized and what the negative side effects might be. The development of DL-trucks is heavily influenced by incumbent firms in the truck manufacturing industry but new actors from the telecom sector, energy sector and emerging truck technology companies are entering the area and shaping the development. The current relatively rigid institutions for truck manufacturing and road freight transport will require significant alignment to adapt to DL-truck operations in areas such as laws and regulations, business models and operational practices. The value chain of road freight transport may be disrupted as some of the current key actors, for instance traditional road carriers, could become less relevant in future DL-truck value chains. A critical uncertainty is how and by which actors the setting of requirements, deployment and financing of digital infrastructure for DL-trucks will be done.

Road freight transport is believed by many to be the first transport domain in which driverless (DL) vehicles will have a significant impact. However, in current literature almost no attention has been given to how the diffusion of DL trucks might occur and how it might affect the transport system. To make predictions on the market uptake and to model impacts of DL truck deployment, valid cost estimates of DL truck operations are crucial. In this paper, an analysis of costs and cost structures for DL truck operations, including indicative numerical cost estimates, is presented. The total cost of ownership for DL trucks compared with that for manually driven (MD) trucks has been analyzed for four different truck types (16-, 24-, 40-, and 64-ton trucks), for three scenarios reflecting pessimistic, intermediate, and optimistic assumptions on economic impacts of driving automation based on current literature. The results indicate that DL trucks may enable substantial cost savings compared with the MD truck baseline. In the base (intermediate) scenario, costs per 1,000 ton-kilometer decrease by 45%, 37%, 33%, and 29% for 16-, 24-, 40-, and 60-ton trucks, respectively. The findings confirm the established view in the literature that freight transport is a highly attractive area for DL vehicles because of the potential economic benefits.

This paper presents an analysis of the potential impacts of large-scale adoption of driverless trucks on transport patterns and system costs for a national freight transport system with Sweden as a case study. The analysis is performed by extending the application domain of the Swedish national freight transport model Samgods to analyze two types of driverless truck scenarios. The first scenario represents a full adoption of driverless trucks that can operate the complete road network and thereby substitute manually driven trucks. In this scenario, road transport tonne-kilometers on Swedish territory increase by 22%, vehicle kilometers traveled by trucks increase by 35% and annual total system costs decrease by 1.7 B(sic) compared to a baseline scenario without driverless trucks. In the second scenario, the current fleet of manually driven trucks is complemented by driverless trucks that can operate on major roads between logistics hubs, but not in complex traffic environments like urban areas due to a limited operational design domain. This may be an initial use-case for driverless trucks operating on public roads. In this scenario, road tonne-kilometers increase by 11%, truck vehicle kilometers traveled increase by 15%, and annual total system costs decrease by 1.2 B_ compared to the baseline. For both scenarios, the impacts of driverless trucks vary significantly between commodity types and transport distances which suggests heterogeneity of benefits of automated driving between different types of freight flows. A sensitivity analysis is performed in which the costs for driverless truck operations is varied, and for the second scenario, also which parts of the road network that driverless trucks can operate are varied. This analysis indicates that the magnitude of impacts is highly dependent on the cost level of driverless trucks and that the ability for DL-trucks to perform international, cross-border transport is crucial for achieving reductions in system costs. An overarching conclusion of the study is that driverless trucks may lead to a significant increase in road transport demand due to modal shifts from rail and sea as a result of the improved cost performance of road transport. This would further strengthen the need to decarbonize road transport to meet non-negotiable climate targets. Important topics for future research include assessing potential societal costs related to driverless trucks due to infrastructure investments and negative externalities such as increasing CO2 emissions and congestion.

The recent development of battery electric trucks (BETs) suggests that they could play a vital role in transitioning to zero-emission road freight. To facilitate this transition, it is important to understand under which conditions BETs can be a viable alternative to internal combustion engine trucks (ICETs). Concurrently, the advancement of autonomous driving technology adds uncertainty and complexity to analyzing how the cost competitiveness of future zero-emissions trucks, such as autonomous electric trucks (AETs) may develop. This study examines the cost performance of BETs and AETs compared to ICETs, and how it varies over different market and technology conditions, charging strategies, and transport applications. Focus is on heavy-duty tractor-trailer trucks operating full truckload shuttle-flows in Sweden. Due to the inherent uncertainty and interactions among the analyzed factors, the analysis is performed as computational experiments using a simulation model of BET, AET, and ICET shuttle flow operations and associated costs. In total, 19,200 experiments are performed by sampling the model across 1200 scenarios representing various transport applications and technical and economic conditions for sixteen charging strategies with different combinations of depot, destination, and en route charging. The results indicate that both BETs and AETs are cost competitive compared to ICETs in a large share of scenarios. High asset utilization is important for offsetting additional investment costs in vehicles and chargers, highlighting the importance of deploying these vehicles in applications that enable high productivity. The cost performance for BETs is primarily influenced by energy related costs, charging strategy, and charging infrastructure utilization. The AET cost performance is in addition heavily affected by remote operations cost, and costs for the automated driving system. When feasible, relying only on depot charging is in many scenarios the most cost-effective charging strategy, with the primary exceptions being highly energy-demanding scenarios with long distances and heavy goods in which the required battery is too heavy to operate the truck within vehicle weight regulations if not complemented by destination, or en route charging. However, many experiments do not lead to a reduced payload capacity for BETs and AETs compared to ICETs, and a large majority of the considered scenarios are feasible to operate with a BET or AET within current gross vehicle weight regulations.

The impacts of a transition to a decarbonized freight transport system are hard to predict, partly due to the uncertainty surrounding electric and automated truck technologies and their future development trajectories. This paper presents an exploratory analysis of techno-economic uncertainties for the deployment of electric trucks and automated driving technology and their impact on the Swedish freight transport system in 2045. A modified version of the Swedish national freight model Samgods, extended to model manual electric trucks (METs) and automated driverless electric trucks (AETs), is used to analyze over 300 scenarios. In these scenarios, assumptions for the development and performance of METs and AETs are varied, using the Swedish reference forecast for freight transport as a baseline. System-level impacts including mode splits, logistics costs, and energy demand are analyzed. Higher levels of electric truck technology maturity correlate with reduced transport costs, increased road freight demand, and decreased reliance on biofuels. AETs further amplify these effects although with significant variation by operating model and technology maturity. Even without full SAE Level 5 automation, AETs operating exclusively on highways could, in some scenarios, perform over 75% of domestic road transport tonne-kilometers, provided their unit economics are favorable. In addition to this outcome space of plausible impacts of electrification and automated driving technology, this paper contributes by demonstrating a tractable approach for exploring system-level impacts of MET and AET deployment on logistics, mode shifts, and energy consumption with national-level freight models under uncertainty.

This study explores the use of Many Objective Robust Decision Making (MORDM) tomanage deep uncertainty in transport climate policy analysis. The focus is on policiessupporting Sweden's ambition to achieve net-zero emissions by 2045. A case studyanalysis demonstrating how MORDM can be applied to a simplified policy analysis toolfor the Swedish transport sector is performed. Candidate policies are generated usinga multi-objective evolutionary algorithm. This searches for policies that meet theclimate target while minimizing driving costs and the use of biofuels and electricity for areference scenario for 2040. The candidate policies’ robustness and vulnerabilities arethen assessed by evaluating them over a set of 2100 scenarios spanned by deeplyuncertain scenario- and model parameters. The analysis highlights the tradeoffsamong the various optimization objectives, as well as among achieving robustness invarious outcomes. The main vulnerability for all policies in terms of reaching theclimate target relates to vehicle electrification rates. The results from the analysis arecontrasted with a previous study by the Swedish Transport Administration applying thesame tool. This comparison shows that the MORDM policies do not distinctlyoutperform the policies identified in the previous study. However, MORDM provides aframework for systematic evaluation of policy robustness and vulnerabilities undervarying future uncertainties, something which has not previously been explicitlyconsidered. The study contributes to a better understanding of the trade-offs anddependencies inherent in achieving robust transport climate policies.