Decision-makers in cities worldwide have the responsibility to contribute to the reduction of greenhouse gas emissions in urban transport. Therefore, effective measures and policies that allow for a change in people’s mobility towards sustainable mobility must be derived. To understand how different people respond to measures and policies, and to increase the effectiveness of such policies, individual mobility needs and mobility determinants have to be considered. For this, the definition of individual mobility styles as holistic descriptions considering travel behavior, attitudes, as well as life stages is useful. This study presents a segmentation approach that identifies eight urban mobility styles by using data from a multidimensional survey conducted in Berlin and San Francisco. We applied a cluster analysis with both behavioral and attitudinal characteristics as segmentation criteria. By analyzing the characteristics, we identified a mobility style—the Environmentally Oriented Multimodals—that is environmentally oriented, but not yet all people in this cluster are sustainable in their mobility. Thus, they are the group with the highest potential to accept and use sustainable mobility. Additionally, we found that within the Environmentally Oriented Multimodals, the change from one life stage to another is also likely to be accompanied by a car acquisition.

Travel behavior can be determined by its spatial context. If there are many shops and restaurants in close proximity, various activities can be done by walking or cycling, and a car is not needed. It is also more difficult (e.g., parking space, traffic jams) to use a car in high-density areas. Overall, travel behavior and dependencies on travel behavior are influenced by urbanity. These relationships have so far only been examined very selectively (e.g., at city level) and not in international comparison. In this study we define an Urbanity Index (UI) at zip code level, which considers factors influencing mobility, international comparability, reproducibility as well as practical application and the development of a scalable methodology. In order to describe urbanity, data were collected regarding spatial structure, population, land use, and public transport. We developed the UI using a supervised machine learning technique which divides zip codes into four area types: (1) super-urban, (2) urban, (3) suburban/small town, (4) rural. To train the model, the perception from experts in known zip codes concerning urbanity and mobility was set as ground truth. With the UI, it is possible to compare countries (Germany and France) with a uniform definition and comparable datasets.

Electric vehicles play a key role in strategic development plans of urban regions in Europe because they are seen as a promising technology to promote environmental quality, livability, and sustainability. Studies on electric mobility mostly concentrate on battery electric cars and disregard hybrid technologies which could address the weakness of range limitations. Therefore, this paper studies the impact of extended range electric vehicle (EREV) solutions on travel behavior, energy demand, environment, and overall sustainable development in the greater Stuttgart region in Germany. An integrated large-scale simulation approach merging different models is applied for future scenarios in 2025. The results show that with EREVs (1) most travel patterns can be fulfilled, (2) the impact on electricity generation is marginal, and (3) there is a high potential to reduce local emissions in areas with high traffic density. Overall, electric mobility is evaluated as one component toward sustainable development in the study area. This study demonstrates the complexity of the topic and highlights the importance of addressing this issue with a multidisciplinary approach.

The German Mobility Panel is one of the longest-lasting studies with a basically unchanged design in mobility behaviour research world-wide. As a result one central asset of this study is the provision of time series data. Nevertheless in repeated surveys, design changes are sometimes inevitable due to new research questions or external developments. Since 1994 the German Mobility Panel has seen only minor design adaptations. After nearly 20 years with a more or less unchanged design, declining participation rates by certain person groups and new survey methods have required fundamental changes in the survey design. This paper describes design changes to the German Mobility Panel in 2013 and analyses the first outcomes generated by the methodological changes.

To counteract climate change, electric vehicles are replacing vehicles with internal combustion engine on the automotive market. Therefore, electric vehicles must be accepted and used like conventional vehicles. This study aims to investigate to which extent electric vehicles are already being used like conventional vehicles. To do this, we present a supervised method where we combine usage data from conventional vehicles (from car use model based on survey data) and electric vehicles (from sensor data) in Germany and California. Based on conventional vehicles, eight car usage profiles were defined by hierarchical clustering in a previous study. Using a softmax regression, we estimate for each electric vehicle a probability of assignment for every car usage profile. Comparison of conventional and electric vehicles with a high probability reveals that electric vehicles are used similar for long-distance travel (>100 km) and different for short-distance travel (<10 km) to conventional vehicles. This implies that electric vehicles are indeed used for long-distance travel but are still not entirely used for everyday mobility. This could be because electric vehicles are not yet suitable for all trip purposes (e.g., transport of larger items).

Transportation policy measures aim to motivate people to change their travel behavior (e.g. to use sustainable modes of transportation instead of a car). Furthermore, life events (e.g. birth of a child, retirement) often result in changes in travel behavior. On the other hand, structural processes (e.g. the exchange of differently car-socialized cohorts) might result in changes in travel volumes as well, but the underlying reasons for this cannot be regarded as behavioral changes. A major goal in the workshop was to find a comprehensive definition of behavioral change. Based on this definition, methodological approaches have been discussed and distinguished from each other and methods for the identification and measurement of behavioral changes by use of different methodical approaches have been revealed.