No abstract

It is hoped that Automated Driving (AD) will make alternatives to the private car more attractive and facilitate the transition to sustainable transport. However, this expectation may underestimate both the resistance of private automobility and the unintended consequences of automated driving. Whether AD will contribute to sustainable mobility depends largely on its implementation and how its risks are prevented. This paper provides empirical insights into the design of acceptable forms of AD by investigating specific use cases with respect to the requirements of different mobility configurations. We pay special attention to people who travel with children. Our use cases comprise three probable types, covering the spectrum from demand-responsive transport (DRT) to private vehicles. Our results include the identification of mobility configurations and an analysis of AD use cases considering several empirically derived criteria: improved accessibility, ease of daily life and well-being, and improvement of the traffic situation and the transport system. Our analysis is based on a qualitative study in the Berlin area, Germany. The discussion focuses on the usefulness of AD against the background of different user perspectives, sustainability, and societal requirements, as well as an evaluation of AD in terms of its acceptability. We conclude that automated mobility use cases should meet the requirements of different mobility configurations to promote the transformation from private to shared automobility and, eventually, less automobility overall.

The energy consumption of passenger vehicles is affected by the physical properties of the environment. The ambient temperature in particular has a significant impact on the operating energy consumption. To quantify the impact of a changed climate on vehicles with different drivetrain systems, we set up a model that calculates the change in energy demand with respect to multiple global warming levels. In particular, the effect of rising temperatures on the energy consumption of battery electric vehicles and vehicles with internal combustion engines was investigated. Our results indicate that climate change will likely lead to a rise in energy consumption of vehicles with an internal combustion engine. This is mostly due to the increase in cabin climatization needs caused by the higher ambient temperatures. At a global warming level (GWL) of 4.0 °C, the calculated annual energy consumption on average is 2.1% higher than without taking the climate-change-related changes in temperature into account. Battery electric vehicles, on the other hand, are expected to have a lower overall energy consumption (up to −2.4% at 4 °C GWL) in cold and moderate climate zones. They benefit from the lower heating needs during winter caused by global warming.

Bicycle usage is significantly affected by weather conditions. Climate change is, therefore, expected to have an impact on the volume of bicycle traffic, which is an important factor in the planning and design of bicycle infrastructures. To predict bicycle traffic in a changed climate in the city of Berlin, this paper compares a traditional statistical approach to three machine learning models. For this purpose, a cross-validation procedure is developed that evaluates model performance on the basis of prediction accuracy. XGBoost showed the best performance and is used for the prediction of bicycle counts. Our results indicate that we can expect an overall annual increase in bicycle traffic of 1–4% in the city of Berlin due to the changes in local weather conditions caused by global climate change. The biggest changes are expected to occur in the winter season with increases of 11–14% due to rising temperatures and only slight increases in precipitation.