Battery electric trucks (BETs) offer environmental benefits but have been challenged by technical and economic viability compared to internal combustion engine trucks (ICETs). Meanwhile, fleet owner-operators have difficulties making strategic decisions of freight electrification based on Total Cost-of-Ownership (TCO) due to inaccurate representations of fleet operations, high uncertainty, and lack of generalizability. We therefore develop a data-driven analytical framework for BET and ICET fleets evaluating over 100,000 scenarios to measure the effects of strategic decisions, operational optimization, and deep uncertainty on TCO and competitiveness. We find that electrification becomes more efficient at scale, enabling optimization of routing, charging and scheduling to better mitigate operational constraints, thus improving service level and utilization. Partial electrification in large networks shows to be a robust pathway towards uncertainties such as battery prices and future battery technology. The study results provide guidelines for fleet operators realizing electrification and thereby their sustainability goals in operations.

This study explores the use of Many Objective Robust Decision Making (MORDM) to manage deep uncertainty in transport climate policy analysis. The focus is on policies supporting Sweden's ambition to achieve net-zero emissions by 2045. A case study analysis demonstrating how MORDM can be applied to a simplified policy analysis tool for the Swedish transport sector is performed. Candidate policies are generated using a multiobjective evolutionary algorithm. This algorithm searches for policies that meet the climate target while trying to minimize adverse impacts on driving-based accessibility and limiting the use of biofuels and electricity for a reference scenario for 2040. The candidate policies' robustness and vulnerabilities are then assessed by evaluating them over a set of 10,000 scenarios spanned by deeply uncertain scenario factors and model relationship assumptions. The results highlight tradeoffs among policy objectives within scenarios, as well as tradeoffs in robustness of various policy objectives over the scenario set. For all policies, the main vulnerability in terms of reaching the climate target relates to vehicle electrification rates. However, the MORDM policies do not distinctly outperform policies identified by a previous study by the Swedish Transport Administration applying the same tool. MORDM provides a framework for systematic evaluation of policy robustness and vulnerabilities under varying future uncertainties, something which has not previously been explicitly considered. The study contributes to a better understanding of the trade-offs and dependencies inherent in achieving robust transport climate policies.

Predicting the impacts of a transition to a decarbonized freight transport system is challenging due to the inherent uncertainty surrounding the development and deployment of electric and automated truck technologies. This paper presents an exploratory analysis of techno-economic uncertainties for the deployment of electric trucks and automated driving technology and their impacts on the Swedish freight transport system by 2045. A modified version of the Swedish national freight model, Samgods, extended to represent manual electric trucks (METs) and automated driverless electric trucks (AETs), is used to analyze over 300 scenarios. In these scenarios, assumptions about the development and performance of METs and AETs are varied relative to the Swedish reference forecast for freight transport. 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 contributing by exploring a plausible outcome space of electrification and automated driving technology, this paper demonstrates 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.

The freight transport system is expected to face significant changes driven by emerging technologies, increasing transport demand, and the need for rapid decarbonization. Automated driving systems and their application to road freight in the form of driverless trucks is one such technology that may influence the development. Driverless trucks could potentially enable cost efficient, safe, and flexible transport solutions, provided that technical, regulatory, and operational challenges are overcome. However, there is significant uncertainty regarding their development trajectory, future use, and system-level impacts such as changes in transport costs, freight patterns, and mode shifts, as well as their implications for sustainable freight transport.

This thesis explores potential long-term system-level impacts of driverless trucks and implications for planning, policy, and sustainability, with a focus on the Swedish freight transport system. Four objectives are addressed. First, future scenarios for the freight transport system are developed, and an analysis of the Swedish innovation system for driverless trucks is performed. The results suggest that plausible initial deployments of driverless trucks are within confined areas, short-distance repetitive flows, and for highway driving between logistics facilities. The innovation process of driverless trucks is characterized by cooperation among a broad set of actors, and it is possible that driverless trucks will disrupt the value chain of road freight transport.

Second, the potential impacts on road transport costs are modeled, showing that driverless trucks could reduce costs by 20% or more, largely determined by the extent to which total labor costs can be reduced.

Third, system-level impacts are analyzed for a large set of introduction scenarios, using national freight transport modeling. The change in cost structure could lead to increased demand for road transport and shifts from rail and sea to road, which may have implications for infrastructure planning, policymaking, and environmental sustainability. Furthermore, driverless trucks capable of operating on highways and strategically chosen access roads can address a substantial amount of freight demand and generate significant system impacts.

Finally, this thesis explores how model-based analysis of driverless trucks’ cost performance, system-level impacts, and climate policy implications under uncertainty can be enhanced using exploratory modeling and methods for decision-making under deep uncertainty. The application of such methods demonstrates that they can contribute to a broader understanding of potential impacts and policy robustness. Several challenges for their introduction in national transport planning are identified, including the need for more flexible and faster models, and managing fundamental differences in approach compared to the current prediction-based planning paradigm.

This thesis contributes with research on the potential system-level impacts of driverless trucks, which may be of relevance for the freight transport industry as well as planners and policymakers at the national level. The research offers an initial, broad examination of a topic for which literature is scarce. Several areas for future research are identified, including the relationship between driverless trucks, electrification, and freight decarbonization; improving modeling of costs and operations of driverless trucks at the vehicle and fleet levels; as well as developing tools to support exploratory modeling and planning to handle uncertainty about the future.

Godstransportsystemet förväntas stå inför betydande förändringar, drivna av ny teknik, ökande transportefterfrågan och behovet av en snabb omställning till fossilfria transporter. Automatiserade körsystem, och deras tillämpning i form av förarlösa lastbilar är en sådan teknologi som kan påverka utvecklingen under de kommande årtiondena. Förarlösa lastbilar kan ha potential att möjliggöra kostnadseffektiva, säkra och flexibla transportlösningar, förutsatt att tekniska, regulatoriska och operativa utmaningar hanteras. Det finns dock betydande osäkerhet kring teknikens framtida utvecklingsriktning och användning, samt kring systemeffekter såsom förändrade transportkostnader, transportmönster och val av transportslag, och dess påverkan på omställningen till ett hållbart transportsystem.

Denna avhandling utforskar potentiella, långsiktiga systemeffekter av förarlösa lastbilar och implikationer för planering, policy och hållbarhet med fokus på det svenska godstransportsystemet. Avhandlingen fokuserar på fyra områden. För det första utvecklas framtidsscenarier för godstransportsystemet och en analys av innovationssystemet för förarlösa lastbilar genomförs. Resultaten tyder på att förarlösa lastbilar initialt kan komma att implementeras inom avgränsade områden, för korta repetitiva flöden och för motorvägskörningmellan logistikfaciliteter. Innovationsprocessen för förarlösa lastbilar kännetecknas av samarbete mellan en bred uppsättning aktörer och det är möjligt att förarlösa lastbilar kommer att förändra värdekedjan för vägtransporter.

För det andra modelleras potentiella effekter av förarlösa lastbilar på vägtransportkostnader. Resultaten visar att förarlösa lastbilar kan minska kostnaderna med 20 % eller mer, där magnituden till stor del beror på i vilken utsträckning de totala arbetskostnaderna kan reduceras.

För det tredje analyseras systemeffekter i ett stort antal introduktionsscenarier, med hjälp av nationell godstransportmodellering. Förändringen i kostnadsstrukturen kan leda till ökad efterfrågan på vägtransporter och överflyttning från järnväg och sjöfart till väg, vilket kan ha konsekvenser för infrastrukturplanering, klimatstyrmedel och miljömässig hållbarhet. Vidare kan förarlösa lastbilar som kan köra på motorvägar samt strategiskt valda tillfartsvägar tillgodose en stor andel av transportefterfrågan.

Slutligen utforskar avhandlingen hur modellbaserad analys av förarlösa lastbilars kostnadsprestanda, systemeffekter och klimatpolitiska implikationer under osäkerhet kan förbättras genom användning av explorativ modellering och metoder för beslutsfattande under djup osäkerhet. Tillämpandet av sådana metoder visar att de kan bidra till en bredare förståelse av möjliga effekter och robustheten för olika styrmedel. Identifierade utmaningar för att införa sådana metoder i nationell transportplanering inkluderar behovet av mer flexibla och snabbare modeller samt att hantera grundläggande skillnader i angreppssätt jämfört med det nuvarande prognosbaserade planeringsparadigmet.

Avhandlingen bidrar med forskning om potentiella systemeffekter av förarlösa lastbilar, som kan vara relevant för transportindustrin samt för planering och policy på nationell nivå. Forskningen utgör en tidig, bred analys av ett område där litteraturen är begränsad. Flera områden för framtida forskning identifieras, vilka inkluderar relationen mellan förarlösa lastbilar, elektrifiering och fossilfria godstransporter; förbättrad modellering av kostnader, planering och användning av förarlösa lastbilar på fordons- och flottnivå; samt utveckling av verktyg som stödjer explorativ modellering och planering för att hantera osäkerhet om framtiden.

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.

This study aims to systematically investigate and structure future technological developments within automation, electrification, and digitalization (AED) in the transportation sector. To address the significant complexity and uncertainty of these developments, a scenario analysis technique known as morphological analysis is used. A set of 23 AED-related technologies and various alternatives for how each technology could develop are compiled in the form of a morphological box. Then, four scenarios are mapped to illustrate future development pathways. This type of holistic analysis provides decision-makers with a comprehensive picture of the future transportation system, allowing them to make more informed decisions. The main contribution of the study is a better understanding of how to approach and structure such a complex research question.

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.

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 aim of this prestudy is to investigate how developments within automation, electrification and digitalization (AED) may affect the demand for passenger and freight transport in Sweden in terms of transport activity (ton-kilometers TKM and passenger-kilometers PKM), traffic activity (vehicle kilometers traveled VKT), modal distribution and other characteristics of the transport system, in order to assess whether the current base forecasts for 2040 that are developed and used by Trafikverket are still robust when accounting for developments and impacts of AED. Both freight and passenger transports are considered, as well as several transport modes. These include road (passenger cars, light and heavy trucks), rail (long and short distance), marine (ships and ferries) and air (planes). In addition, support infrastructure such as charging stations and goods terminals are considered. Automation technologies include automated vehicles and goods handling. Electrification refers to the replacement of conventional fuels with electric energy, as well as charging infrastructure. Digitalization is the broadest of the technological fields, and includes both digital services and digital infrastructure. The latter is furthermore an enabler for first and foremost automation, but also for electrification to some extent.

The theoretical perspective of the study is that transport demand is derived from the need to transport goods and people. Several drivers of transport demand (such as mode characteristics and economic structure) are presented and included in a general framework for assessing transport demand. The framework further incorporates a variety of previously constructed models and consists of three layers (activities & material flows, transport services and infrastructure) which connect in two markets (the transport and traffic market). The effects on transport demand are assessed from a set of demand parameters, including TKM, PKM and VKT. Finally, six mechanisms through which AED could affect transport demand are presented and integrated into the general framework. 

Through literature reviews and workshops, a set of general trends within AED were identified. Since there is a considerable uncertainty regarding how these trends could develop until 2040, an explorative scenario-based approach was employed. In order to structure this approach, a morphological analysis was conducted where the identified trends were formulated as parameters and their stages of development as attributes. Combined, these parameters and attributes formed a morphological box which could be used to illustrate different scenarios. In this study, four scenarios were then mapped in the morphological box: a base scenario intended to mimic explicit and implicit assumptions in the base forecast and three alternative scenarios (Partnership Society, Social Engineering 2.0 and Swimming in Data) intended to contrast the base scenario by illustrating alternative societal and technological development paths. 

These scenarios and their respective morphological box mappings were then analyzed based on the general framework. The first step in this impact analysis consisted of investigating possible separate impacts of the parameters on each layer and market in the general framework. The mechanisms of which each parameter would affect the system were also identified. Examples of effects include changes in generalized costs and service levels. In the second step, the impacts from combined AED development were studied based on the scenario mappings in the morphological box. This highlights possible synergies between the technologies. Finally, the combined effects were compared with the base scenario in order to reach the study’s aim.  

The results of the analysis show that automation, electrification and digitalization technologies separately could lead to changes in transport efficiency as well costs. Furthermore, synergetic effects leading to even stronger impacts on factors such as these could arise when they are combined. Through the general framework and the demand impact mechanisms, it was shown that factors such as these could lead to changes in the transport demand, modal distribution and transport system characteristics. Since the scenario mapping shows that the base forecasts do not consider development in automation and digitalization to a significant extent, the base forecasts would probably not be robust if these technologies see a continued development and implementation in the transport system.

Understanding the value of travel time for mobility concepts based on self-driving vehicles is crucial to accurately value transport investments and predict future travel patterns. In this paper, we carry out a morphological analysis to illustrate the diversity of mobility concepts based on self-driving vehicles and the complexity of determining the value of travel time for such concepts. We consider four categories of parameters that directly or indirectly impact the value of travel time: (i) vehicle characteristics, (ii) operating principles, (iii) journey characteristics and (iv) traveler characteristics. The parameters and respective attributes result in a morphological matrix that spans all possible solutions. Out of these, we analyze five plausible solutions based on the implications of the concept characteristics on the total value of travel time. We conclude by suggesting an alternative approach to differentiate value of travel time based on travel characteristics rather than the usual decomposition into transport modes.