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.