Workday transport models are extensively used in practice to guide policy development while the more leisure-oriented weekend days are left out of the analysis. This is peculiar, as weekend travel can be as busy as workday travel, or even worse. Consequently, weekend travel behaviour is fundamentally different, showcasing more variability in the activities that people perform. Given this, can such a model be relevant for policy makers and does this outweigh the effort that is required to make such a model? These questions are investigated by thinking whether and how an operational workday transport model can be adapted to represent Saturday travel behaviour in an aggregate approach. Due to the lack of a clear starting point for model development, literature and travel data were used to propose a set of adaptations which can be used to transform a workday model. From this, two Saturday model versions were created which contain different trip purposes and Saturday travel data, whereby one of the models is improved in the trip generation component. The results show that adapting a model is feasible but more work is required for a proper implementation. Nevertheless, a Saturday model can be used in specific use cases but a clear purpose to regard Saturdays in policy development is still lacking. This research further recommends to explore the applicability of Saturday transport models and to conceptually develop a model which can portray complex aspects of travel behaviour in an Activity-Based approach.

The transition to de-fossilized energy systems plays a central role in achieving climate neutrality in Europe, and the transport sector is pivotal in this transformation. Nowadays, passenger vehicles represent an important share of the final energy consumption and the greenhouse gas emissions. As internal combustion engine vehicles (ICEVs) are gradually phased out and replaced by battery electric vehicles (BEVs), and hydrogen fuel cell electric vehicles (FCEVs), understanding the evolving energy demand profile of passenger transport becomes more and more critical. This thesis addresses this challenge by quantifying the way in which the differences in powertrain technologies, in the vehicle types, and in weather conditions influence the energy consumption and the total energy demand across European regions.

The core of this research is the enhancement of the Vehicle Consumption Assessment Model (VCAM), a simulation platform capable of evaluating the energy consumption of different powertrains under various environmental and operational conditions. The model was extended to include a detailed representation of FCEVs and used under dynamic weather profiles and region-specific fleet compositions. These advancements allowed the simulation of real-world driving scenarios by using both historical and projected climate data, as well as the assessment of policy pathways and fleet evolution trends through 2050.

The methodological framework followed a multi-layered approach. First, a powertrain comparison for BEVS, FCEVs, and ICEVs has been made to evaluate them across different vehicle segments, considering performance under different driving cycles. Second, a temperature sensitivity analysis has been made for forty years of temperature data for Greece, Germany, and Finland, which were used to quantify the impact of cold and hot conditions on energy consumption and range for each powertrain technology. Lastly, a scenario-based analysis has been performed to scrutinize the effects of different IEA policy pathways (STEPS, SDS, NZE) and IPCC climate scenarios (RCP 2.6, 4.5, 8.5), as well as the influence of the growing SUV market share, on the passenger vehicle energy demand in 2050.

The results show that the energy demand is highly sensitive to the selection of powertrain technology, with BEVs offering the highest efficiency, as well as the highest sensitivity to ambient temperature. The FCEVs perform more consistently across extreme temperatures, but consume more energy than BEVs. ICEVs are the least efficient vehicles, but they present a moderate sensitivity to temperature because of their capability to use the engine's waste heat to cover the thermal loads. Vehicle size can significantly alter consumption, especially for electrified vehicles in extreme climates.

Regarding the regional effects, the projections for the passenger vehicle energy demand differ substantially. Germany's demand remains the highest due to population and mobility volume, while Finland shows the greatest sensitivity to climate conditions. Greece, where the most moderate climate conditions exist, presents the lowest variability. Across all technologies, BEVs offer the highest efficiency but also the greatest vulnerability to temperature extremes, with the energy consumption rising to more than 40% in cold conditions. FCEVs, which are less efficient overall, keep a more stable performance across temperature variations. The scenario analysis made shows that the ambitious decarbonization strategies (STEPS, SDS, NZE) could reduce total passenger vehicle energy demand by more than 60% relative to 2019 levels. However, this reduction is sensitive to fleet composition. To be more specific, for example, an annual SUV market growth of 2 percent could increase energy demand by up to 19% in Germany compared to a no-growth baseline. Similarly, consumption in the coldest years can exceed the warmest by 8 - 15 % for FCEVs and BEVs, depending on the region.


In conclusion, this thesis provides a detailed and geographically differentiated understanding of the passenger vehicle energy demand during the energy transition. It underlines the need to plan, while considering climate conditions, segments, and technologies to ensure that the electrification of the transport sector aligns with the broader goals.

Comparing the performance of distribution functions and a discrete choice model, on a mode & destination choice in a tour-based model. An discrete choice model is composed for a tour-based model. Both choice models are implemented in a large-scale tour-based model and the performances are compared.