Autonomous driving is being discussed as a promising solution for transportation-related issues and might bring some improvement for users of the system. For instance, especially high mileage commuters might compensate for some of their time spent travelling since they will be able to undertake other activities while going to work. At the same time, there are still many uncertainties and few empirical data on the impact of autonomous driving on mode choices. This study addresses the impact of autonomous driving on value of travel time savings (VTTS) and mode choices for commuting trips using stated choice experiments. Two use cases were addressed-a privately owned and a shared autonomous vehicle-compared to other modes of transportation. The collected data were analyzed by performing a mixed logit model. The results show that mode-related factors such as time elements, especially in-vehicle time and cost, play a crucial role for mode choices that include autonomous vehicles. The study provides empirical evidence that autonomous driving may lead to a reduction in the VTTS for commuting trips. We found that driving autonomously in a privately owned vehicle might reduce the VTTS by 31% compared to driving manually and is perceived similarly to in-vehicle time in public transportation. Also, riding in a shared autonomous vehicle is perceived 10% less negatively than driving manually. The study provides important insights on VTTS by autonomous driving for commuting trips and can be a base for future research to build upon.

Due to digitalization trends and rapid technological development, cars are becoming more technologically advanced with an ongoing trend towards fully automated vehicles. Understanding possible changes in user preferences and the impact on mobility of autonomous driving is of great importance for policy and transport planning authorities in light of urbanization trends, demographic change, and environmental challenges. Despite the relevance of the topic, there are limited empirical insights on user preferences, once autonomous driving becomes available. To close this gap and analyze the potential changes in the value of travel time savings (VTTS) resulting from the availability of autonomous driving, an online survey using revealed and stated preference methods was conducted. In the survey user preferences toward currently available and future available modes of transportation were assessed using two discrete choice experiments. VTTS calculations are based on an estimated joint mixed logit model. The results of the study show an average VTTS reduction of 41% for autonomous driving compared to driving a conventional car, however, only for commuting trips. For leisure or shopping trips, no significant changes in the VTTS were found. Considering shared autonomous vehicles (SAV), the results indicate that using SAV is perceived as a less attractive option than using a privately owned autonomous vehicle. Translating the results into policy implications, a potential conflict between individual benefits of autonomous driving and societal goals is identified. Finally, policy recommendations are discussed.

Battery electric vehicles (BEV) provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. Assumptions on fleet battery availability and a detailed representation of fast charging are found to have a strong impact on wind curtailment, renewable energy feed-in, and required gas power plant flexibility. A representation of fleet connection to the grid in high temporal detail is less important. Peak load can be reduced by 5% and 3% in both scenarios, respectively. Shifted load is robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.

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Background The introduction of a carbon tax on passenger transport is currently being discussed in Germany. Various stakeholders favour a consumption-based, revenue-neutral carbon tax with a uniform lump-sum offset for private households and a tax rate of 40 € per ton of CO2. Objective In this study, we examine the distributional effects of carbon taxation for the German passenger transport sector under the assumption of the proposed tax model. We discuss as to what extent which socioeconomic groups would be burdened and who might even benefit from carbon taxation. To answer these questions we use a uniquely modelled data set that encompasses all forms of passenger transport (i.e. in Germany and abroad) of the German resident population over 1 year. The national household travel survey Mobility in Germany 2017 is the basis of the microscopic data set. We derive annual CO2 emissions and carbon tax burdens for various population groups using the data on passenger transport, as well as specific emission factors. Results Results show that low income households, retirees, single parents and family households with two or more children would benefit from the proposed carbon taxation scheme due to below-average emissions per person; in contrast, working age households without children and car owners with heavy car use would be burdened. Our results are of particular relevance to transport researchers, transport politicians and decision makers as a basis for designing, developing and introducing a carbon taxation scheme.

Battery electric vehicles provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. In particular, state-of-charge assumptions and representing fast charging drive curtailment of renewable energy feed-in and required gas power plant flexibility. A detailed representation of fleet charge connection is less important. Peak load can be significantly reduced by 5% and 3% in both scenarios, respectively. Shifted load is very robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.

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